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Dockerfile

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+FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04
+
+RUN apt-get update && apt-get install -y \
+  git \
+  python3 \
+  python3-pip \
+  && rm -rf /var/lib/apt/lists/*
+
+RUN pip install --upgrade "jax[cuda]" -f https://storage.googleapis.com/jax-releases/jax_releases.html \
+  && pip install -q \
+  git+https://github.com/borisdayma/dalle-mini.git \
+  git+https://github.com/patil-suraj/vqgan-jax.git
+
+RUN pip install jupyter
+
+WORKDIR /workspace
+

FUNDING.yml

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+github: [borisdayma]

README.md

@@ -1,13 +1,16 @@
----
-title: DALLEMINI
-emoji: 🦀
-colorFrom: blue
-colorTo: gray
-sdk: gradio
-sdk_version: 4.19.2
-app_file: app.py
-pinned: false
-license: mit
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Running Dalle-mini With Docker
+
+This folder contains the Dockerfile needed to build a Docker image that can easily run Dalle-mini.
+
+## Inference
+
+Steps to run inference with Dalle-mini are as follows:
+
+1. Build the docker image with ```dalle-mini/Docker/build_image.sh```
+2. Run the container with ```dalle-mini/run_docker_image```
+3. Navigate to ```/workspace/tools/inference/``` and run ```run_infer_notebook.sh```
+4. Click the Jupyter Notebook link and run through the notebook.
+
+### Inference Video Tutorial
+
+Alteratively check out a video tutorial on how to run Dalle-mini on [Linux](https://www.youtube.com/watch?v=eWpzLIa6v9E&t=9s) and [Windows](https://www.youtube.com/watch?v=OqEuEe-xSKk&t=59s)

__init__.py

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+__version__ = "0.1.1"
+
+from .model import DalleBart, DalleBartProcessor

app.py

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+#!/usr/bin/env python
+# coding: utf-8
+import os
+
+import gradio as gr
+from backend import get_images_from_backend
+
+block = gr.Blocks(css=".container { max-width: 800px; margin: auto; }")
+backend_url = os.environ["BACKEND_SERVER"] + "/generate"
+
+
+def infer(prompt):
+    response = get_images_from_backend(prompt, backend_url)
+    return response["images"]
+
+
+with block:
+    gr.Markdown("<h1><center>DALL·E mini</center></h1>")
+    gr.Markdown(
+        "DALL·E mini is an AI model that generates images from any prompt you give!"
+    )
+    with gr.Group():
+        with gr.Box():
+            with gr.Row().style(mobile_collapse=False, equal_height=True):
+
+                text = gr.Textbox(
+                    label="Enter your prompt", show_label=False, max_lines=1
+                ).style(
+                    border=(True, False, True, True),
+                    margin=False,
+                    rounded=(True, False, False, True),
+                    container=False,
+                )
+                btn = gr.Button("Run").style(
+                    margin=False,
+                    rounded=(False, True, True, False),
+                )
+        gallery = gr.Gallery(label="Generated images", show_label=False).style(
+            grid=[3], height="auto"
+        )
+        text.submit(infer, inputs=text, outputs=gallery)
+        btn.click(infer, inputs=text, outputs=gallery)
+
+    gr.Markdown(
+        """___
+   <p style='text-align: center'>
+   Created by <a href="https://twitter.com/borisdayma" target="_blank">Boris Dayma</a> et al. 2021-2022
+   <br/>
+   <a href="https://github.com/borisdayma/dalle-mini" target="_blank">GitHub</a> | <a href="https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini-Generate-images-from-any-text-prompt--VmlldzoyMDE4NDAy" target="_blank">Project Report</a>
+   </p>"""
+    )
+
+
+block.launch(enable_queue=False)

backend.py

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+# Client requests to Dalle-Mini Backend server
+
+import base64
+from io import BytesIO
+
+import requests
+from PIL import Image
+
+
+class ServiceError(Exception):
+    def __init__(self, status_code):
+        self.status_code = status_code
+
+
+def get_images_from_backend(prompt, backend_url):
+    r = requests.post(backend_url, json={"prompt": prompt})
+    if r.status_code == 200:
+        json = r.json()
+        images = json["images"]
+        images = [Image.open(BytesIO(base64.b64decode(img))) for img in images]
+        version = json.get("version", "unknown")
+        return {"images": images, "version": version}
+    else:
+        raise ServiceError(r.status_code)
+
+
+def get_model_version(url):
+    r = requests.get(url)
+    if r.status_code == 200:
+        version = r.json()["version"]
+        return version
+    else:
+        raise ServiceError(r.status_code)

build_docker.sh

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+docker build . -t dalle-mini:latest

check_size.yml

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+name: Check file size
+
+on:
+  pull_request:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - name: Check large files
+        uses: ActionsDesk/lfs-warning@v2.0
+        with:
+          filesizelimit: 10485760 # = 10MB, so we can sync to HF spaces

config.json

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+{
+  "activation_dropout": 0.0,
+  "activation_function": "gelu",
+  "attention_dropout": 0.0,
+  "bos_token_id": 16385,
+  "d_model": 2048,
+  "decoder_attention_heads": 32,
+  "decoder_ffn_dim": 4096,
+  "decoder_layerdrop": 0.0,
+  "decoder_layers": 24,
+  "decoder_start_token_id": 16384,
+  "do_sample": true,
+  "dropout": 0.0,
+  "encoder_attention_heads": 32,
+  "encoder_ffn_dim": 4096,
+  "encoder_layerdrop": 0.0,
+  "encoder_layers": 24,
+  "encoder_vocab_size": 50272,
+  "eos_token_id": 16385,
+  "force_ln_scale": false,
+  "gradient_checkpointing": false,
+  "image_length": 256,
+  "image_vocab_size": 16415,
+  "init_std": 0.01,
+  "is_encoder_decoder": true,
+  "ln_positions": "normformer",
+  "ln_type": "layernorm",
+  "max_length": 257,
+  "max_text_length": 64,
+  "min_length": 257,
+  "model_type": "dallebart",
+  "normalize_text": true,
+  "pad_token_id": 16385,
+  "scale_embedding": false,
+  "sinkhorn_iters": 1,
+  "tau_init": 0.05,
+  "tie_word_embeddings": false,
+  "use_absolute_position_embeddings": true,
+  "use_alibi": false,
+  "use_bias": false,
+  "use_cache": true,
+  "use_cosine_attention": false,
+  "use_deepnet_scaling": false,
+  "use_final_ln_decoder": true,
+  "use_final_ln_encoder": true,
+  "use_glu": true,
+  "use_head_scale": false,
+  "use_swin_position_embeddings": false
+}

configuration.py

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+# coding=utf-8
+# Copyright 2021 The Fairseq Authors and The HuggingFace Inc. team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" DalleBart model configuration """
+import warnings
+
+from transformers.configuration_utils import PretrainedConfig
+from transformers.utils import logging
+
+from .utils import PretrainedFromWandbMixin
+
+logger = logging.get_logger(__name__)
+
+
+class DalleBartConfig(PretrainedFromWandbMixin, PretrainedConfig):
+    model_type = "dallebart"
+    keys_to_ignore_at_inference = ["past_key_values"]
+    attribute_map = {
+        "num_attention_heads": "encoder_attention_heads",
+        "hidden_size": "d_model",
+    }
+
+    def __init__(
+        self,
+        normalize_text=False,
+        encoder_vocab_size=50264,
+        image_vocab_size=16384,  # encoded image token space
+        image_length=256,  # number of encoded tokens
+        max_text_length=64,  # max number of text tokens
+        encoder_layers=12,
+        encoder_ffn_dim=4096,
+        encoder_attention_heads=16,
+        decoder_layers=12,
+        decoder_ffn_dim=4096,
+        decoder_attention_heads=16,
+        activation_function="gelu",
+        d_model=1024,
+        dropout=0.1,
+        attention_dropout=0.0,
+        activation_dropout=0.0,
+        init_std=0.02,
+        scale_embedding=False,
+        gradient_checkpointing=True,
+        use_scan=None,
+        use_cache=True,
+        is_encoder_decoder=True,
+        forced_eos_token_id=None,
+        tie_word_embeddings=False,  # different modalities and sizes
+        do_sample=True,
+        # transformer variants
+        use_bias=False,  # use bias in attention and dense layers (except for lm_head)
+        ln_type="layernorm",  # layer normalization type, "rmsnorm", "layernorm"
+        ln_positions="normformer",  # layer normalization positions, "normformer", "swinv2", "cogview", "postln", "preln", "deepnet" (same as postln)
+        use_head_scale=False,  # used in NormFormer
+        use_cosine_attention=False,  # used in Swin v2
+        tau_init=0.05,  # used only in cosine attention (Swin v2)
+        use_absolute_position_embeddings=True,  # default
+        use_swin_position_embeddings=False,  # used in Swin v1/v2
+        use_deepnet_scaling=False,  # used in Deepnet
+        use_glu=True,  # "GLU Variants Improve Transformer"
+        use_alibi=False,  # Not implemented yet - from "Train Short, Test Long: Attention with Linear Biases Enables Input Length Extrapolation"
+        sinkhorn_iters=1,  # used in SinkFormers
+        use_final_ln_encoder=True,  # final layer normalization in encoder
+        use_final_ln_decoder=True,  # final layer normalization in decoder
+        # parameters that should not be necessary but could affect results
+        force_ln_scale=False,  # force scale in layernorm even when followed by dense layers
+        **kwargs,
+    ):
+        # text normalizer
+        self.normalize_text = normalize_text
+
+        # transformer variants
+        self.use_bias = use_bias
+        assert ln_type in [
+            "rmsnorm",
+            "layernorm",
+        ], "ln_type must be 'rmsnorm' or 'layernorm'"
+        self.ln_type = ln_type
+        if ln_positions == "deepnet":
+            ln_positions = "postln"
+        assert ln_positions in [
+            "normformer",
+            "swinv2",
+            "cogview",
+            "postln",
+            "preln",
+        ], "ln_positions must be 'normformer', 'swinv2', 'cogview', 'postln', 'preln'"
+        self.use_head_scale = use_head_scale
+        assert use_alibi is False, "use_alibi is not supported yet"
+        self.ln_positions = ln_positions
+        self.use_cosine_attention = use_cosine_attention
+        self.tau_init = tau_init
+        self.use_absolute_position_embeddings = use_absolute_position_embeddings
+        self.use_swin_position_embeddings = use_swin_position_embeddings
+        self.use_deepnet_scaling = use_deepnet_scaling
+        self.use_glu = use_glu
+        self.use_alibi = use_alibi
+        self.sinkhorn_iters = sinkhorn_iters
+        if ln_positions == "postln":
+            assert (
+                use_final_ln_encoder
+            ), "use_final_ln_encoder must be True when ln_positions is 'postln'"
+            assert (
+                use_final_ln_decoder
+            ), "use_final_ln_decoder must be True when ln_positions is 'postln'"
+        self.use_final_ln_encoder = use_final_ln_encoder
+        self.use_final_ln_decoder = use_final_ln_decoder
+        self.force_ln_scale = force_ln_scale
+
+        # common parameters
+        self.encoder_vocab_size = encoder_vocab_size
+        self.image_vocab_size = image_vocab_size
+        self.image_length = image_length
+        self.max_text_length = max_text_length
+        self.d_model = d_model
+        self.encoder_ffn_dim = encoder_ffn_dim
+        self.encoder_layers = encoder_layers
+        self.encoder_attention_heads = encoder_attention_heads
+        self.decoder_ffn_dim = decoder_ffn_dim
+        self.decoder_layers = decoder_layers
+        self.decoder_attention_heads = decoder_attention_heads
+        self.dropout = dropout
+        self.attention_dropout = attention_dropout
+        self.activation_dropout = activation_dropout
+        self.activation_function = activation_function
+        self.init_std = init_std
+        self.use_cache = use_cache
+        self.gradient_checkpointing = gradient_checkpointing
+        # all layers are the same in most configurations
+        self.use_scan = use_scan if use_scan is not None else ln_positions != "swinv2"
+        assert not (
+            self.use_scan and ln_positions == "swinv2"
+        ), "scan cannot be used with 'swinv2'"
+        self.scale_embedding = (
+            scale_embedding  # scale factor will be sqrt(d_model) if True
+        )
+
+        # special token id's are appended to vocab if not provided
+        decoder_start_token_id = kwargs.pop("decoder_start_token_id", image_vocab_size)
+        bos_token_id = kwargs.pop("bos_token_id", image_vocab_size)
+        pad_token_id = kwargs.pop("pad_token_id", image_vocab_size)
+        eos_token_id = kwargs.pop("eos_token_id", image_vocab_size)
+
+        # we generate to image_length + 1 (for bos) by default
+        min_length = kwargs.pop("min_length", image_length + 1)
+        max_length = kwargs.pop("max_length", image_length + 1)
+
+        super().__init__(
+            # args required in parent class
+            is_encoder_decoder=is_encoder_decoder,
+            tie_word_embeddings=tie_word_embeddings,
+            forced_eos_token_id=forced_eos_token_id,
+            decoder_start_token_id=decoder_start_token_id,
+            bos_token_id=bos_token_id,
+            pad_token_id=pad_token_id,
+            eos_token_id=eos_token_id,
+            min_length=min_length,
+            max_length=max_length,
+            do_sample=do_sample,
+            **kwargs,
+        )
+
+        # ensure backward compatibility for BART CNN models
+        if self.forced_bos_token_id is None and kwargs.get(
+            "force_bos_token_to_be_generated", False
+        ):
+            self.forced_bos_token_id = self.bos_token_id
+            warnings.warn(
+                f"Please make sure the config includes `forced_bos_token_id={self.bos_token_id}` in future versions."
+                "The config can simply be saved and uploaded again to be fixed."
+            )

data.py

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+import random
+from dataclasses import dataclass, field
+from functools import partial
+from pathlib import Path
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+from braceexpand import braceexpand
+from datasets import Dataset, load_dataset
+
+from .model.text import TextNormalizer
+
+
+@dataclass
+class Dataset:
+    dataset_repo_or_path: str
+    train_file: str = None
+    validation_file: str = None
+    streaming: bool = True
+    use_auth_token: bool = False
+    text_column: str = "caption"
+    encoding_column: str = "encoding"
+    max_train_samples: int = None
+    max_eval_samples: int = None
+    preprocessing_num_workers: int = None
+    overwrite_cache: bool = False
+    do_train: bool = False
+    do_eval: bool = True
+    seed_dataset: int = None
+    shard_by_host: bool = False
+    blank_caption_prob: float = 0.0
+    clip_score_column: str = "clip_score"
+    min_clip_score: float = None
+    max_clip_score: float = None
+    filter_column: str = None
+    filter_value: str = None
+    multi_eval_ds: bool = False
+    train_dataset: Dataset = field(init=False)
+    eval_dataset: Dataset = field(init=False)
+    other_eval_datasets: list = field(init=False)
+    rng_dataset: jnp.ndarray = field(init=False)
+    multi_hosts: bool = field(init=False)
+
+    def __post_init__(self):
+        if self.seed_dataset is None:
+            # create a random seed
+            self.seed_dataset = random.randint(0, 2**32 - 1)
+        # set numpy rng
+        self.np_rng = np.random.default_rng(self.seed_dataset)
+        self.multi_hosts = jax.process_count() > 1
+        # feed blank captions only in streaming mode for now
+        # otherwise dataset could be cached with same blanked captions
+        if self.blank_caption_prob:
+            assert (
+                self.streaming is True
+            ), "blank_caption_prob can only be used in streaming mode"
+        # define data_files
+        if self.train_file is not None or self.validation_file is not None:
+            # accept braceexpand notation
+            for k in ["train_file", "validation_file"]:
+                f = getattr(self, k)
+                if isinstance(f, str):
+                    setattr(self, k, list(braceexpand(f)))
+            # for list of files, split training data shards by host
+            if (
+                isinstance(self.train_file, list)
+                and self.multi_hosts
+                and self.shard_by_host
+            ):
+                self.train_file = self.train_file[
+                    jax.process_index() :: jax.process_count()
+                ]
+            data_files = {
+                "train": self.train_file,
+                "validation": self.validation_file,
+            }
+        else:
+            data_files = None
+
+        # multiple validation datasets
+        if self.multi_eval_ds:
+            assert Path(
+                self.dataset_repo_or_path
+            ).is_dir(), f"{self.dataset_repo_or_path} is not a directory, required for multi_eval_ds"
+            data_files = {
+                split.name: [str(f) for f in split.glob("*.parquet")]
+                for split in Path(self.dataset_repo_or_path).glob("*")
+            }
+            # rename "valid" to "validation" if present for consistency
+            if "valid" in data_files:
+                data_files["validation"] = data_files["valid"]
+                del data_files["valid"]
+            self.dataset_repo_or_path = "parquet"
+
+        # load dataset
+        dataset = load_dataset(
+            self.dataset_repo_or_path,
+            data_files=data_files,
+            streaming=self.streaming,
+            use_auth_token=self.use_auth_token,
+        )
+        if self.do_train:
+            if "train" not in dataset:
+                raise ValueError("Training requires a training dataset")
+            self.train_dataset = dataset["train"]
+            if self.max_train_samples is not None:
+                self.train_dataset = (
+                    self.train_dataset.take(self.max_train_samples)
+                    if self.streaming
+                    else self.train_dataset.select(range(self.max_train_samples))
+                )
+        if self.do_eval:
+            if "validation" not in dataset:
+                raise ValueError("Evaluating requires a validation dataset")
+            self.eval_dataset = dataset["validation"]
+            if self.max_eval_samples is not None:
+                self.eval_dataset = (
+                    self.eval_dataset.take(self.max_eval_samples)
+                    if self.streaming
+                    else self.eval_dataset.select(range(self.max_eval_samples))
+                )
+            # other eval datasets
+            other_eval_splits = dataset.keys() - {"train", "validation"}
+            self.other_eval_datasets = {
+                split: dataset[split] for split in other_eval_splits
+            }
+
+    def preprocess(self, tokenizer, config):
+        # get required config variables
+        decoder_start_token_id = config.decoder_start_token_id
+        normalize_text = config.normalize_text
+        max_length = config.max_text_length
+
+        if self.streaming:
+            # we need to shuffle early in streaming mode
+            if hasattr(self, "train_dataset"):
+                self.train_dataset = self.train_dataset.shuffle(
+                    buffer_size=5000, seed=self.seed_dataset
+                )
+        else:
+            self.rng_dataset = jax.random.PRNGKey(self.seed_dataset)
+
+        # filter data
+        partial_filter_function = partial(
+            filter_function,
+            filter_column=self.filter_column,
+            filter_value=self.filter_value,
+            clip_score_column=self.clip_score_column,
+            min_clip_score=self.min_clip_score,
+            max_clip_score=self.max_clip_score,
+        )
+        for ds in ["train_dataset", "eval_dataset"]:
+            if hasattr(self, ds):
+                setattr(
+                    self,
+                    ds,
+                    (
+                        getattr(self, ds).filter(partial_filter_function)
+                        if self.streaming
+                        else getattr(self, ds).filter(
+                            partial_filter_function,
+                            num_proc=self.preprocessing_num_workers,
+                            load_from_cache_file=not self.overwrite_cache,
+                            desc="Filtering datasets",
+                        )
+                    ),
+                )
+        if hasattr(self, "other_eval_datasets"):
+            self.other_eval_datasets = {
+                split: (
+                    ds.filter(partial_filter_function)
+                    if self.streaming
+                    else ds.filter(
+                        partial_filter_function,
+                        num_proc=self.preprocessing_num_workers,
+                        load_from_cache_file=not self.overwrite_cache,
+                        desc="Filtering datasets",
+                    )
+                )
+                for split, ds in self.other_eval_datasets.items()
+            }
+
+        # normalize text
+        if normalize_text:
+            text_normalizer = TextNormalizer()
+            partial_normalize_function = partial(
+                normalize_function,
+                text_column=self.text_column,
+                text_normalizer=text_normalizer,
+            )
+            for ds in ["train_dataset", "eval_dataset"]:
+                if hasattr(self, ds):
+                    setattr(
+                        self,
+                        ds,
+                        (
+                            getattr(self, ds).map(partial_normalize_function)
+                            if self.streaming
+                            else getattr(self, ds).map(
+                                partial_normalize_function,
+                                num_proc=self.preprocessing_num_workers,
+                                load_from_cache_file=not self.overwrite_cache,
+                                desc="Normalizing datasets",
+                            )
+                        ),
+                    )
+            if hasattr(self, "other_eval_datasets"):
+                self.other_eval_datasets = {
+                    split: (
+                        ds.map(partial_normalize_function)
+                        if self.streaming
+                        else ds.map(
+                            partial_normalize_function,
+                            num_proc=self.preprocessing_num_workers,
+                            load_from_cache_file=not self.overwrite_cache,
+                            desc="Normalizing datasets",
+                        )
+                    )
+                    for split, ds in self.other_eval_datasets.items()
+                }
+
+        # blank captions
+        if self.blank_caption_prob:
+            partial_blank_caption_function = partial(
+                blank_caption_function,
+                text_column=self.text_column,
+                blank_caption_prob=self.blank_caption_prob,
+                rng=self.np_rng,
+            )
+            if hasattr(self, "train_dataset"):
+                self.train_dataset = (
+                    self.train_dataset.map(partial_blank_caption_function)
+                    if self.streaming
+                    else self.train_dataset.map(
+                        partial_blank_caption_function,
+                        num_proc=None
+                        if self.seed_dataset
+                        else self.preprocessing_num_workers,
+                        load_from_cache_file=False,
+                        desc="Blanking some captions",
+                    )
+                )
+
+        # preprocess
+        partial_preprocess_function = partial(
+            preprocess_function,
+            tokenizer=tokenizer,
+            text_column=self.text_column,
+            encoding_column=self.encoding_column,
+            max_length=max_length,
+            decoder_start_token_id=decoder_start_token_id,
+        )
+        for ds in ["train_dataset", "eval_dataset"]:
+            if hasattr(self, ds):
+                setattr(
+                    self,
+                    ds,
+                    (
+                        getattr(self, ds).map(
+                            partial_preprocess_function,
+                            batched=True,
+                            remove_columns=[
+                                self.text_column,
+                                self.encoding_column,
+                            ],
+                        )
+                        if self.streaming
+                        else getattr(self, ds).map(
+                            partial_preprocess_function,
+                            batched=True,
+                            remove_columns=getattr(ds, "column_names"),
+                            num_proc=self.preprocessing_num_workers,
+                            load_from_cache_file=not self.overwrite_cache,
+                            desc="Preprocessing datasets",
+                        )
+                    ),
+                )
+        if hasattr(self, "other_eval_datasets"):
+            self.other_eval_datasets = {
+                split: (
+                    ds.map(
+                        partial_preprocess_function,
+                        batched=True,
+                        remove_columns=[
+                            self.text_column,
+                            self.encoding_column,
+                        ],
+                    )
+                    if self.streaming
+                    else ds.map(
+                        partial_preprocess_function,
+                        batched=True,
+                        remove_columns=getattr(ds, "column_names"),
+                        num_proc=self.preprocessing_num_workers,
+                        load_from_cache_file=not self.overwrite_cache,
+                        desc="Preprocessing datasets",
+                    )
+                )
+                for split, ds in self.other_eval_datasets.items()
+            }
+
+    def dataloader(self, split, batch_size, epoch=None):
+        def _dataloader_datasets_non_streaming(
+            dataset: Dataset,
+            rng: jax.random.PRNGKey = None,
+        ):
+            """
+            Returns batches of size `batch_size` from truncated `dataset`, sharded over all local devices.
+            Shuffle batches if rng is set.
+            """
+            steps_per_epoch = len(dataset) // batch_size
+
+            if rng is not None:
+                batch_idx = jax.random.permutation(rng, len(dataset))
+            else:
+                batch_idx = jnp.arange(len(dataset))
+
+            batch_idx = batch_idx[
+                : steps_per_epoch * batch_size
+            ]  # Skip incomplete batch.
+            batch_idx = batch_idx.reshape((steps_per_epoch, batch_size))
+
+            for idx in batch_idx:
+                batch = dataset[idx]
+                batch = {k: jnp.array(v) for k, v in batch.items()}
+                yield batch
+
+        def _dataloader_datasets_streaming(
+            dataset: Dataset,
+            epoch: int,
+        ):
+            keys = ["input_ids", "attention_mask", "labels", "decoder_input_ids"]
+            batch = {k: [] for k in keys}
+            first_loop = True  # stop after one loop in some cases
+            while (self.multi_hosts and split == "train") or first_loop:
+                # in multi-host, we run forever (no epoch) as hosts need to stop
+                # at the same time and training data may not be split equally
+                # For validation data we put the entire batch on each host and then
+                # keep only the one specific to each host (could be improved but not necessary)
+                if epoch is not None:
+                    assert split == "train"
+                    # reshuffle training data at each epoch
+                    dataset.set_epoch(epoch)
+                    epoch += 1
+                for item in dataset:
+                    for k in keys:
+                        batch[k].append(item[k])
+                    if len(batch[keys[0]]) == batch_size:
+                        batch = {k: jnp.array(v) for k, v in batch.items()}
+                        yield batch
+                        batch = {k: [] for k in keys}
+                first_loop = False
+
+        if split == "train":
+            ds = self.train_dataset
+        elif split == "eval":
+            ds = self.eval_dataset
+        else:
+            ds = self.other_eval_datasets[split]
+
+        if self.streaming:
+            return _dataloader_datasets_streaming(ds, epoch)
+        else:
+            if split == "train":
+                self.rng_dataset, input_rng = jax.random.split(self.rng_dataset)
+            return _dataloader_datasets_non_streaming(ds, input_rng)
+
+    @property
+    def length(self):
+        len_train_dataset, len_eval_dataset = None, None
+        if self.streaming:
+            # we don't know the length, let's just assume max_samples if defined
+            if self.max_train_samples is not None:
+                len_train_dataset = self.max_train_samples
+            if self.max_eval_samples is not None:
+                len_eval_dataset = self.max_eval_samples
+        else:
+            len_train_dataset = (
+                len(self.train_dataset) if hasattr(self, "train_dataset") else None
+            )
+            len_eval_dataset = (
+                len(self.eval_dataset) if hasattr(self, "eval_dataset") else None
+            )
+        return len_train_dataset, len_eval_dataset
+
+
+def shift_tokens_right(input_ids: np.array, decoder_start_token_id: int):
+    """
+    Shift input ids one token to the right.
+    """
+    shifted_input_ids = np.zeros(input_ids.shape)
+    shifted_input_ids[:, 1:] = input_ids[:, :-1]
+    shifted_input_ids[:, 0] = decoder_start_token_id
+    return shifted_input_ids
+
+
+def blank_caption_function(example, text_column, blank_caption_prob, rng=None):
+    if (
+        blank_caption_prob
+        and (rng.random() if rng is not None else np.random.random())
+        < blank_caption_prob
+    ):
+        example[text_column] = ""
+    return example
+
+
+def normalize_function(example, text_column, text_normalizer):
+    example[text_column] = text_normalizer(example[text_column])
+    return example
+
+
+def filter_function(
+    example,
+    min_clip_score,
+    max_clip_score,
+    clip_score_column,
+    filter_column,
+    filter_value,
+):
+    if min_clip_score is not None and example[clip_score_column] < min_clip_score:
+        return False
+    if max_clip_score is not None and example[clip_score_column] > max_clip_score:
+        return False
+    if filter_column is not None and example[filter_column] != filter_value:
+        return False
+    return True
+
+
+def preprocess_function(
+    examples,
+    tokenizer,
+    text_column,
+    encoding_column,
+    max_length,
+    decoder_start_token_id,
+):
+    inputs = examples[text_column]
+    # Setting padding="max_length" as we need fixed length inputs for jitted functions
+    model_inputs = tokenizer(
+        inputs,
+        max_length=max_length,
+        padding="max_length",
+        truncation=True,
+        return_tensors="np",
+    )
+
+    # set up targets
+    # Note: labels correspond to our target indices
+    # decoder input ids are the same but shifted to the right with bos at the beginning (and without last token)
+    labels = examples[encoding_column]
+    labels = np.asarray(labels)
+
+    # We need the labels, in addition to the decoder_input_ids, for the compute_loss function
+    model_inputs["labels"] = labels
+
+    # In our case, this prepends the bos token and removes the last one
+    decoder_input_ids = shift_tokens_right(labels, decoder_start_token_id)
+    model_inputs["decoder_input_ids"] = decoder_input_ids
+
+    return model_inputs

distributed_shampoo.py

@@ -0,0 +1,2280 @@
+# coding=utf-8
+# Copyright 2022 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# An implementation of distributed Shampoo optimizer from:
+#
+#  Scalable Second Order Optimization for Deep Learning
+#  Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer
+#  Preprint Paper: https://arxiv.org/abs/2002.09018
+#
+# This implementation moves computation of inverse pth root back to the
+# accelerator (if higher precision is available).
+#
+# Authors: Rohan Anil (rohananil at google dot com)
+#    &     Vineet Gupta (vineet at google dot com)
+#
+"""Distributed Shampoo Implementation."""
+
+import enum
+import functools
+import itertools
+from typing import Any, List, NamedTuple, Tuple
+
+import chex
+import jax
+import jax.experimental.pjit as pjit
+import jax.numpy as jnp
+import numpy as np
+import optax
+from flax import struct
+from jax import lax
+
+from .quantization_utils import QuantizedValue
+from .symmetric_matrices import symmetric_matrices
+
+# Dtype for inverse-pth root routine
+# Switch to f64 if you have hardware that supports it. Enable the jax flag
+# jax_enable_x64 for this to work, otherwise it will default to float32.
+_MAT_INV_PTH_ROOT_DTYPE = jnp.float64
+
+
+@struct.dataclass
+class TrainingMetrics:
+    inverse_pth_root_errors: chex.Array  # Error for inverse-pth roots.
+    # TODO(rohananil): Add more important metrics to track during training.
+
+
+# Per parameter optimizer state used in data-parallel training.
+class ParameterStats(NamedTuple):
+    """State associated to each parameter of the model being trained."""
+
+    diagonal_statistics: QuantizedValue  # Accumulator for diagonal preconditioner
+    statistics: List[Any]  # Statistics (QuantizedValue, chex.Array)
+    preconditioners: List[Any]  # Preconditioners (QuantizedValue, chex.Array)
+    diagonal_momentum: QuantizedValue  # Momentum for the diagonal preconditioner
+    momentum: QuantizedValue  # Momentum for the shampoo preconditioner
+    training_metrics: TrainingMetrics  # Metrics (optional for training).
+
+
+# For training extremely large model; We keep a global state with a concatenated
+# statistics and preconditioner states for all vars. This is so that we can
+# annotate the leading axis to be sharded to save memory at the cost of
+# communication.
+@struct.dataclass
+class GlobalShardedParameterStats:
+    statistics: chex.Array  # Statistics
+    preconditioners: chex.Array  # Preconditioners
+    exponents: chex.Array  # exponents
+
+
+# These are per-parameter local states; All statistics here mirror the parameter
+# Thus the sharding is copied over from the param specification.
+@struct.dataclass
+class LocalShardedParameterStats:
+    """State associated to each parameter of the model being trained."""
+
+    diagonal_statistics: QuantizedValue  # Accumulator for diagonal preconditioner
+    diagonal_momentum: QuantizedValue  # Momentum for the diagonal preconditioner
+    momentum: QuantizedValue  # Momentum for the shampoo preconditioner
+    training_metrics: TrainingMetrics  # Metrics (optional for training).
+    index_start: np.int32 = struct.field(
+        pytree_node=False
+    )  # Index into global statistics array
+    sizes: Any = struct.field(pytree_node=False)  # Sizes of the statistics.
+
+
+def init_training_metrics(num_statistics):
+    # Since the downstream apis expect a jnp.array - we create a dummy one if
+    # num_statistics=0.
+    if not num_statistics:
+        return TrainingMetrics(jnp.array(0, jnp.float32))
+    else:
+        return TrainingMetrics(jnp.zeros([num_statistics], jnp.float32))
+
+
+def init_training_metrics_shapes(num_statistics):
+    # Since the downstream apis expect a jnp.array - we create a dummy one if
+    # num_statistics=0.
+    if not num_statistics:
+        return TrainingMetrics([[], jnp.float32])
+    else:
+        return TrainingMetrics([[num_statistics], jnp.float32])
+
+
+def init_training_metrics_pspec():
+    return TrainingMetrics(pjit.PartitionSpec())
+
+
+class ShardedShampooStats(NamedTuple):
+    """Shampoo state in sharded mode."""
+
+    global_stats: Any
+    local_stats: Any
+
+
+class ShampooState(NamedTuple):
+    count: chex.Array
+    stats: Any
+
+
+class InitFnState(NamedTuple):
+    init_fn: Any
+    pspec_fn: Any
+    shape_and_dtype_fn: Any
+
+
+class GraftingType(enum.IntEnum):
+    SGD = 1
+    ADAGRAD = 2
+    RMSPROP = 3
+    RMSPROP_NORMALIZED = 4
+    SQRT_N = 5
+    ADAGRAD_NORMALIZED = 6
+
+
+def power_iteration(
+    matrix,
+    num_iters=100,
+    error_tolerance=1e-6,
+    precision=lax.Precision.HIGHEST,
+):
+    r"""Power iteration algorithm.
+
+    The power iteration algorithm takes a symmetric PSD matrix `A`, and produces
+    a scalar `\lambda` , which is the greatest (in absolute value) eigenvalue
+    of `A`, and a vector v, which is the corresponding eigenvector of `A`.
+
+    References:
+      [Wikipedia, 2021](https://en.wikipedia.org/wiki/Power_iteration)
+
+    Args:
+      matrix: the symmetric PSD matrix.
+      num_iters: Number of iterations.
+      error_tolerance: Iterative exit condition.
+      precision: precision XLA related flag, the available options are: a)
+        lax.Precision.DEFAULT (better step time, but not precise) b)
+        lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST
+        (best possible precision, slowest)
+
+    Returns:
+      eigen vector, eigen value
+    """
+    matrix_size = matrix.shape[-1]
+
+    def _iter_condition(state):
+        i, unused_v, unused_s, unused_s_v, run_step = state
+        return jnp.logical_and(i < num_iters, run_step)
+
+    def _iter_body(state):
+        """One step of power iteration."""
+        i, new_v, s, s_v, unused_run_step = state
+        new_v = new_v / jnp.linalg.norm(new_v)
+
+        s_v = jnp.einsum("ij,j->i", matrix, new_v, precision=precision)
+        s_new = jnp.einsum("i,i->", new_v, s_v, precision=precision)
+        return (
+            i + 1,
+            s_v,
+            s_new,
+            s_v,
+            jnp.greater(jnp.abs(s_new - s), error_tolerance),
+        )
+
+    # Figure out how to use step as seed for random.
+    v_0 = (
+        np.random.RandomState(1729).uniform(-1.0, 1.0, matrix_size).astype(matrix.dtype)
+    )
+
+    init_state = tuple([0, v_0, jnp.zeros([], dtype=matrix.dtype), v_0, True])
+    _, v_out, s_out, _, _ = lax.while_loop(_iter_condition, _iter_body, init_state)
+    v_out = v_out / jnp.linalg.norm(v_out)
+    return v_out, s_out
+
+
+def mat_power(
+    mat_m,
+    p,
+    precision=lax.Precision.HIGHEST,
+):
+    """A simple matrix power method. M^p where p can be TracedValue."""
+    power = jnp.eye(mat_m.shape[0], dtype=_MAT_INV_PTH_ROOT_DTYPE)
+
+    def _iter_condition(state):
+        i, _, _ = state
+        return i > 0
+
+    def _iter_body(state):
+        i, power, mat = state
+
+        power = jax.lax.cond(
+            i % 2 == 1,
+            lambda: jnp.matmul(mat, power, precision=precision),
+            lambda: power,
+        )
+        i //= 2
+        mat = jnp.matmul(mat, mat, precision=precision)
+        return i, power, mat
+
+    _, result, _ = lax.while_loop(_iter_condition, _iter_body, (p, power, mat_m))
+    return result
+
+
+def matrix_inverse_pth_root(
+    matrix,
+    p,
+    num_iters=100,
+    ridge_epsilon=1e-6,
+    error_tolerance=1e-6,
+    precision=lax.Precision.HIGHEST,
+):
+    """Computes `matrix^(-1/p)`, where `p` is a positive integer.
+
+    This function uses the Coupled newton iterations algorithm for
+    the computation of a matrix's inverse pth root.
+
+
+    References:
+      [Functions of Matrices, Theory and Computation,
+       Nicholas J Higham, Pg 184, Eq 7.18](
+       https://epubs.siam.org/doi/book/10.1137/1.9780898717778)
+
+    Args:
+      matrix: the symmetric PSD matrix whose power it to be computed
+      p: exponent, for p a positive integer.
+      num_iters: Maximum number of iterations.
+      ridge_epsilon: Ridge epsilon added to make the matrix positive definite.
+      error_tolerance: Error indicator, useful for early termination.
+      precision: precision XLA related flag, the available options are: a)
+        lax.Precision.DEFAULT (better step time, but not precise) b)
+        lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST
+        (best possible precision, slowest)
+
+    Returns:
+      matrix^(-1/p)
+    """
+
+    # If the input is not square, materialize it from the concatenated form.
+    if matrix.shape[0] != matrix.shape[1]:
+        matrix = symmetric_matrices.materialize_matrix_from_concat(matrix)
+
+    assert matrix.shape[0] == matrix.shape[1]
+
+    # We use _MAT_INV_PTH_ROOT_DTYPE for the matrix inverse pth root.
+    # Switch to f64 if you have hardware that supports it. Enable the jax flag
+    # jax_enable_x64 for this to work.
+    matrix_size = matrix.shape[0]
+    orig_dtype = matrix.dtype
+    matrix = matrix.astype(_MAT_INV_PTH_ROOT_DTYPE)
+    alpha = jnp.asarray(-1.0 / p, _MAT_INV_PTH_ROOT_DTYPE)
+    identity = jnp.eye(matrix_size, dtype=_MAT_INV_PTH_ROOT_DTYPE)
+    _, max_ev = power_iteration(
+        matrix=matrix, num_iters=100, error_tolerance=1e-6, precision=precision
+    )
+    ridge_epsilon = ridge_epsilon * jnp.maximum(max_ev, 1e-6)
+
+    def _iter_condition(state):
+        (i, unused_mat_m, unused_mat_h, unused_old_mat_h, error, run_step) = state
+        error_above_threshold = jnp.logical_and(error > error_tolerance, run_step)
+        return jnp.logical_and(i < num_iters, error_above_threshold)
+
+    def _iter_body(state):
+        (i, mat_m, mat_h, unused_old_mat_h, error, unused_run_step) = state
+        mat_m_i = (1 - alpha) * identity + alpha * mat_m
+        new_mat_m = jnp.matmul(mat_power(mat_m_i, p), mat_m, precision=precision)
+        new_mat_h = jnp.matmul(mat_h, mat_m_i, precision=precision)
+        new_error = jnp.max(jnp.abs(new_mat_m - identity))
+        # sometimes error increases after an iteration before decreasing and
+        # converging. 1.2 factor is used to bound the maximal allowed increase.
+        return (i + 1, new_mat_m, new_mat_h, mat_h, new_error, new_error < error * 1.2)
+
+    if matrix_size == 1:
+        resultant_mat_h = (matrix + ridge_epsilon) ** alpha
+        error = jnp.array(0, jnp.float32)
+    else:
+        damped_matrix = matrix + ridge_epsilon * identity
+
+        z = (1 + p) / (2 * jnp.linalg.norm(damped_matrix))
+        new_mat_m_0 = damped_matrix * z
+        new_error = jnp.max(jnp.abs(new_mat_m_0 - identity))
+        new_mat_h_0 = identity * jnp.power(z, 1.0 / p)
+        init_state = tuple([0, new_mat_m_0, new_mat_h_0, new_mat_h_0, new_error, True])
+        _, mat_m, mat_h, old_mat_h, error, convergence = lax.while_loop(
+            _iter_condition, _iter_body, init_state
+        )
+        error = jnp.max(jnp.abs(mat_m - identity)).astype(jnp.float32)
+        is_converged = jnp.asarray(convergence, old_mat_h.dtype)
+        resultant_mat_h = is_converged * mat_h + (1 - is_converged) * old_mat_h
+        resultant_mat_h = jnp.asarray(resultant_mat_h, orig_dtype)
+    return resultant_mat_h, error
+
+
+def merge_small_dims(shape_to_merge, max_dim):
+    """Merge small dimensions.
+
+    If there are some small dimensions, we collapse them:
+    e.g. [1, 2, 512, 1, 2048, 1, 3, 4] --> [1024, 2048, 12] if max_dim = 1024
+         [1, 2, 768, 1, 2048] --> [2, 768, 2048]
+
+    Args:
+      shape_to_merge: Shape to merge small dimensions.
+      max_dim: Maximal dimension of output shape used in merging.
+
+    Returns:
+      Merged shape.
+    """
+    if shape_to_merge and np.all(np.array(shape_to_merge) == 1):
+        return [1]
+
+    resulting_shape = []
+    product = 1
+    for d in shape_to_merge:
+        if product * d <= max_dim:
+            product *= d
+        else:
+            if product > 1:
+                resulting_shape.append(product)
+            product = d
+    if product > 1:
+        resulting_shape.append(product)
+    return resulting_shape
+
+
+def pad_square_matrix(mat, max_size):
+    """Pad a square matrix up to max_size.
+
+    Args:
+      mat: a matrix to pad.
+      max_size: matrix size requested.
+
+    Returns:
+      Given M returns [[M, 0], [0, I]]
+    """
+    rows, cols = mat.shape
+    if rows != cols:
+        raise ValueError(
+            "Must have rows == cols, instead got " f"rows={rows}, cols={cols}"
+        )
+    if cols > max_size:
+        raise ValueError(
+            "Must have cols <= max_size. Instead got "
+            f"cols={cols}, max_size={max_size}."
+        )
+    if rows == max_size:
+        return mat
+    pad_size = max_size - rows
+
+    zs1 = jnp.zeros([rows, pad_size], dtype=mat.dtype)
+    zs2 = jnp.zeros([pad_size, rows], dtype=mat.dtype)
+    eye = jnp.eye(pad_size, dtype=mat.dtype)
+    mat = jnp.concatenate([mat, zs1], 1)
+    mat = jnp.concatenate([mat, jnp.concatenate([zs2, eye], 1)], 0)
+    return mat
+
+
+def make_sliced_padding(
+    symmetric_block_size,
+    num_blocks,
+    starting_block,
+    dtype,
+):
+    """Returns padding for symmetric block matrix.
+
+    Specifically, the padding is given concatenated rectangular matrices
+    representing the lower-triangular rows below the starting block. For example,
+    if we want to pad the symmetric matrix
+
+    M = [[A, B^T]
+         [B, C]],
+
+    the desired output (in terms of the full matrix) with num_blocks = 4 is
+
+    M_padded = [[A, B^T, 0, 0]
+                [B, C,   0, 0]
+                [0, 0,   I, 0]
+                 0, 0,   0, I].
+
+    We would represent M as the block matrix mat = [A, B, C]. In this form, the
+    additional padding to provide has form [0, 0, I, 0, 0, 0, I] (only the lower
+    triangular parts in the third and fourth rows).
+
+    Args:
+      symmetric_block_size: The size of each block.
+      num_blocks: The total number of blocks.
+      starting_block: The block where to start the padding.
+      dtype: The type to use for the blocks.
+    """
+    if starting_block == num_blocks:
+        return jnp.zeros(shape=(symmetric_block_size, 0), dtype=dtype)
+
+    blocks = []
+    for i in range(starting_block, num_blocks):
+        blocks.append(
+            jnp.zeros(
+                shape=(symmetric_block_size, symmetric_block_size * i), dtype=dtype
+            )
+        )
+        blocks.append(jnp.eye(symmetric_block_size, dtype=dtype))
+    return jnp.concatenate(blocks, axis=-1)
+
+
+def pad_block_symmetric_matrix(
+    mat,
+    symmetric_block_size,
+    max_num_blocks,
+):
+    """Returns the padded blocked symmetric matrix.
+
+    The size of the padded matrix will be:
+      [symmetric_block_size, symmetric_block_size * max_num_blocks]
+
+    The input matrix can either:
+      - Be square with size less or equal to symmetric_block_size. In this case,
+        mat will first be padded to a square matrix of size symmetric_block_size,
+        and then be padded again up to the full size of the blocked matrix.
+      - Be a rectangle with number of rows equal to block size.
+        In this case, number of columns must be a multiple of number of rows, and
+        the ratio must correspond to a block representation of a symmetric matrix.
+        That is, the ratio must have form x * (x + 1) / 2. Here, x represents the
+        number of block rows represented by the matrix.
+
+    Args:
+      mat: The input block matrix.
+      symmetric_block_size: The size of blocks.
+      max_num_blocks: The largest number of blocks to pad to.
+    """
+    rows, cols = mat.shape
+    if rows > symmetric_block_size:
+        raise ValueError(
+            "Must have rows <= symmetric_block_size. Instead got "
+            f"rows={rows}, symmetric_block_size={symmetric_block_size}."
+        )
+    if rows > cols:
+        raise ValueError(
+            "Must have rows <= cols, instead got " f"rows={rows}, cols={cols}."
+        )
+    if cols > symmetric_block_size * max_num_blocks:
+        raise ValueError(
+            "Must have cols <= symmetric_block_size * max_num_blocks "
+            f"Instead got cols={cols}, "
+            f"symmetric_block_size={symmetric_block_size}, "
+            f"max_num_blocks={max_num_blocks}."
+        )
+    if rows < symmetric_block_size:
+        mat = pad_square_matrix(mat, max_size=symmetric_block_size)
+    # Update rows and cols after possibly padding in pad_square_matrix.
+    rows, cols = mat.shape
+    assert rows == symmetric_block_size
+    assert cols % rows == 0
+    filled_blocks = cols // rows
+    padding_blocks = make_sliced_padding(
+        symmetric_block_size=symmetric_block_size,
+        num_blocks=symmetric_matrices.num_blocks_from_total_blocks(max_num_blocks),
+        starting_block=symmetric_matrices.num_blocks_from_total_blocks(filled_blocks),
+        dtype=mat.dtype,
+    )
+    return jnp.concatenate([mat, padding_blocks], axis=-1)
+
+
+def pad_vector(vec, max_size):
+    """Pad a vector to a max_size.
+
+    Args:
+      vec: a vector to pad.
+      max_size: matrix size requested.
+
+    Returns:
+      Given V returns [V, 0]
+    """
+    size = vec.shape[0]
+    assert size <= max_size
+    if size == max_size:
+        return vec
+    pad_size = max_size - size
+    zs1 = jnp.zeros([pad_size], dtype=vec.dtype)
+    return jnp.concatenate([vec, zs1], 0)
+
+
+def efficient_cond(predicate, compute_fn, init_state, *args, **kwargs):
+    """Avoids wasteful buffer allocation with XLA."""
+
+    def _iter_body(unused_state):
+        results = compute_fn(*args, **kwargs)
+        return tuple([False] + list(results))
+
+    def _iter_condition(state):
+        return state[0]
+
+    results = jax.lax.while_loop(
+        _iter_condition, _iter_body, tuple([predicate] + init_state)
+    )
+    return tuple(results[1:])
+
+
+class BlockPartitioner:
+    """Partitions a tensor into smaller tensors."""
+
+    def __init__(self, param, block_size):
+        self._shape = param.shape
+        self._splits = []
+        split_sizes = []
+        # We split params into smaller blocks. Here we store the metadata to make
+        # that split.
+        for i, d in enumerate(param.shape):
+            if 0 < block_size < d:
+                # d-1, otherwise split appends a 0-size array.
+                nsplit = (d - 1) // block_size
+                indices = (np.arange(nsplit, dtype=np.int32) + 1) * block_size
+                sizes = np.ones(nsplit + 1, dtype=np.int32) * block_size
+                sizes[-1] = d - indices[-1]
+                self._splits.append((i, indices))
+                split_sizes.append(sizes)
+            else:
+                split_sizes.append(np.array([d], dtype=np.int32))
+        self._num_splits = len(split_sizes)
+        self._preconditioner_shapes = []
+        for t in itertools.product(*split_sizes):
+            self._preconditioner_shapes.extend([[d, d] for d in t])
+
+    def shapes_for_preconditioners(self):
+        return self._preconditioner_shapes
+
+    def num_splits(self):
+        return self._num_splits
+
+    def partition(self, tensor):
+        """Partition tensor into blocks."""
+
+        assert tensor.shape == self._shape
+        tensors = [tensor]
+        for (i, indices) in self._splits:
+            tensors_local = []
+            for t in tensors:
+                tensors_local.extend(jnp.split(t, indices_or_sections=indices, axis=i))
+            tensors = tensors_local
+        return tensors
+
+    def merge_partitions(self, partitions):
+        """Merge partitions back to original shape."""
+
+        for (i, indices) in reversed(self._splits):
+            n = len(indices) + 1
+            partial_merged_tensors = []
+            ind = 0
+            while ind < len(partitions):
+                partial_merged_tensors.append(
+                    jnp.concatenate(partitions[ind : ind + n], axis=i)
+                )
+                ind += n
+            partitions = partial_merged_tensors
+        assert len(partitions) == 1
+        return partitions[0]
+
+
+class Preconditioner:
+    """Compute statistics/shape from gradients for preconditioning."""
+
+    def __init__(self, param, block_size, best_effort_shape_interpretation):
+        self._original_shape = param.shape
+        self._transformed_shape = param.shape
+        if best_effort_shape_interpretation:
+            self._transformed_shape = merge_small_dims(self._original_shape, block_size)
+        reshaped_param = jnp.reshape(param, self._transformed_shape)
+        self._partitioner = BlockPartitioner(reshaped_param, block_size)
+
+    def statistics_from_grad(self, grad):
+        """Compute statistics from gradients.
+
+        Args:
+          grad: Gradient to compute statistics from.
+
+        Returns:
+          A list of gradient statistics for each partition.
+        """
+        reshaped_grad = jnp.reshape(grad, self._transformed_shape)
+        partitioned_grads = self._partitioner.partition(reshaped_grad)
+        stats = []
+        for g in partitioned_grads:
+            g_stats = []
+            rank = len(g.shape)
+            for i in range(rank):
+                axes = list(range(i)) + list(range(i + 1, rank))
+                stat = jnp.tensordot(g, g, axes=(axes, axes))
+                g_stats.append(stat)
+            stats.extend(g_stats)
+        return stats
+
+    def shapes_for_preconditioners(self):
+        """Returns shape from statistics."""
+        return self._partitioner.shapes_for_preconditioners()
+
+    def exponent_for_preconditioner(self):
+        """Returns exponent to use for inverse-pth root M^{-1/p}."""
+        return 2 * len(self._transformed_shape)
+
+    def preconditioned_grad(self, grad, preconditioners):
+        """Precondition the gradient.
+
+        Args:
+          grad: A gradient tensor to precondition.
+          preconditioners: A list of preconditioners to apply.
+
+        Returns:
+          A preconditioned gradient.
+        """
+
+        reshaped_grad = jnp.reshape(grad, self._transformed_shape)
+        partitioned_grads = self._partitioner.partition(reshaped_grad)
+        preconditioned_partitioned_grads = []
+        num_splits = self._partitioner.num_splits()
+        for i, g in enumerate(partitioned_grads):
+            preconditioners_for_grad = preconditioners[
+                i * num_splits : (i + 1) * num_splits
+            ]
+            rank = len(g.shape)
+            precond_g = g
+            for j in range(rank):
+                precond_g = jnp.tensordot(
+                    precond_g, preconditioners_for_grad[j], axes=[[0], [0]]
+                )
+            preconditioned_partitioned_grads.append(precond_g)
+        merged_grad = self._partitioner.merge_partitions(
+            preconditioned_partitioned_grads
+        )
+        return jnp.reshape(merged_grad, self._original_shape)
+
+
+def _convert_to_parameter_stats(global_stats, local_stat):
+    """Creates parameter stats from sharded stats."""
+    index_start = int(local_stat.index_start)
+    index_end = int(len(local_stat.sizes)) + index_start
+    statistics = global_stats.statistics[index_start:index_end, :, :]
+    preconditioners = global_stats.preconditioners[index_start:index_end, :, :]
+    new_statistics = []
+    new_preconditioners = []
+    for i, size in enumerate(local_stat.sizes):
+        new_statistics.append(statistics[i][:size, :size])
+        new_preconditioners.append(preconditioners[i][:size, :size])
+    return ParameterStats(
+        local_stat.diagonal_statistics,
+        new_statistics,
+        new_preconditioners,
+        local_stat.diagonal_momentum,
+        local_stat.momentum,
+        local_stat.training_metrics,
+    )
+
+
+def _convert_from_parameter_stats(parameter_stats, local_stats):
+    """Creates sharded stats from parameter stats."""
+    return LocalShardedParameterStats(
+        parameter_stats.diagonal_statistics,
+        parameter_stats.diagonal_momentum,
+        parameter_stats.momentum,
+        parameter_stats.training_metrics,
+        local_stats.index_start,
+        local_stats.sizes,
+    )
+
+
+def _add_error_into_local_stats(local_stats, errors, inverse_failure_threshold):
+    """Adds errors back into local statistics."""
+    new_local_stats = []
+    for local_stat in local_stats:
+        if local_stat.sizes:
+            index_start = int(local_stat.index_start)
+            index_end = int(len(local_stat.sizes)) + index_start
+            per_stat_error = errors[index_start:index_end]
+        else:
+            per_stat_error = jnp.array(0, jnp.float32)
+        if local_stat.sizes:
+            per_stat_error = jnp.where(
+                jnp.logical_and(
+                    per_stat_error > 0.0, per_stat_error != inverse_failure_threshold
+                ),
+                per_stat_error,
+                local_stat.training_metrics.inverse_pth_root_errors,
+            )
+        new_local_stats.append(
+            LocalShardedParameterStats(
+                local_stat.diagonal_statistics,
+                local_stat.diagonal_momentum,
+                local_stat.momentum,
+                TrainingMetrics(per_stat_error),
+                local_stat.index_start,
+                local_stat.sizes,
+            )
+        )
+    return new_local_stats
+
+
+def batch(x, num_devices):
+    """Batch `x` so that so that leading axis is num_devices."""
+    n = len(x)
+    b = int(n / num_devices)
+    return jnp.stack([jnp.stack(x[idx : idx + b]) for idx in range(0, n, b)])
+
+
+def unbatch(batched_values):
+    """Unbatch values across leading axis and return a list of elements."""
+    b1, b2 = batched_values.shape[0], batched_values.shape[1]
+    results = []
+    for v_array in jnp.split(batched_values, indices_or_sections=b1, axis=0):
+        v_array = jnp.squeeze(v_array)
+        # b2 = batches (number of preconditioner computation) per core.
+        if b2 > 1:
+            for v in jnp.split(v_array, indices_or_sections=b2, axis=0):
+                results.append(jnp.squeeze(v))
+        else:
+            results.append(v_array)
+    return results
+
+
+def distributed_shampoo(
+    learning_rate,
+    block_size,
+    beta1=0.9,
+    beta2=0.999,
+    diagonal_epsilon=1e-10,
+    matrix_epsilon=1e-6,
+    weight_decay=0.0,
+    start_preconditioning_step=5,
+    preconditioning_compute_steps=1,
+    statistics_compute_steps=1,
+    best_effort_shape_interpretation=True,
+    graft_type=GraftingType.SGD,
+    nesterov=True,
+    exponent_override=0,
+    # Pass pmap 'batch axis name' in pmap mode.
+    batch_axis_name=None,
+    ### Only set following 3 params in pjit/spmd mode.
+    ### WARNING: Experimental
+    statistics_partition_spec=None,
+    preconditioner_partition_spec=None,
+    num_devices_for_pjit=None,
+    shard_optimizer_states=False,
+    ###
+    ### Experimental memory reduction mode
+    best_effort_memory_usage_reduction=False,
+    ###
+    inverse_failure_threshold=0.1,
+    moving_average_for_momentum=False,
+    skip_preconditioning_dim_size_gt=4096,
+    clip_by_scaled_gradient_norm=None,
+    precision=lax.Precision.HIGHEST,
+):
+    """Distributed Shampoo optimizer.
+
+    Distributed Shampoo is a second-order preconditioned method (concretely, a
+    variant of full-matrix Adagrad), that provides significant convergence and
+    wall-clock time improvements compared to conventional first-order methods,
+    and that has been shown to scale to large state-of-the-art deep learning
+    models.
+
+    References:
+      Scalable Second Order Optimization for Deep Learning,
+      Rohan Anil, Vineet Gupta, Tomer Koren, Kevin Regan, Yoram Singer
+
+      Preprint: https://arxiv.org/abs/2002.09018
+
+    Args:
+      learning_rate: the step size used to update the parameters.
+      block_size: Block size for large layers (if > 0). Preconditioning compute
+        operation is cubic in the dimension of the tensor. Block size allows us to
+        chunk the layers into sub-layers of maximal dimension dictated by this
+        value. Use 128 as default (increase if you have compute budget).
+      beta1: momentum parameter.
+      beta2: second moment averaging parameter.
+      diagonal_epsilon: epsilon for diagonal adagrad (only if layerwise grafting
+        to AdaGrad is enabled).
+      matrix_epsilon: epsilon to add to statistics before computing inverse pth
+        root. If you are running in f32 precision for inverse pth root
+        (recommended today) this can go upto 1e-6. If you have latest hardware
+        with native f64 precision, set this upto 1e-12.
+      weight_decay: Weight decay for regularization.
+      start_preconditioning_step: When to start Shampoo update before which
+        diagonal update is used. This is because we dont have enough information
+        to do stable inverse.
+      preconditioning_compute_steps: How often to compute preconditioner.
+        Performance tuning params for controlling memory and compute requirements.
+        Ideally set this and statistics_compute_steps params to 1.
+      statistics_compute_steps: How often to compute statistics.
+      best_effort_shape_interpretation: If there are some small dimensions,
+        collapse them e.g. [1, 2, 512, 1, 2048, 1, 3, 4] --> [1024, 2048, 12] if
+        block = 1024, [1, 2, 768, 1, 2048] --> [2, 768, 2048]
+      graft_type: Grafting is a technique to fix the layerwise scale of Shampoo
+        optimizer. This allows us to plugin the Shampoo optimizer into settings
+        where SGD/AdaGrad is already well tuned.
+      nesterov: Nesterov momentum.
+      exponent_override: Override the exponent used in matrix inverse.
+      batch_axis_name: labeled axis over pmap for data-parallel training the
+        optimizer used for.
+      statistics_partition_spec: PartitionSpec to be used in sharded mode.
+      preconditioner_partition_spec: PartitionSpec to be used in sharded mode.
+      num_devices_for_pjit: Number of devices to parallelize over when using pjit.
+      shard_optimizer_states: Shard optimizer states to save memory in model
+        parallel training.
+      best_effort_memory_usage_reduction: Best effort memory usage reduction. -
+        diagonal_statistics -> jnp.bfloat16 - momentum buffers (2x) -> jnp.int8 -
+        statistics, preconditioners -> jnp.int16 + diagonals
+      inverse_failure_threshold: numerics are hard and inverses fail sometimes; we
+        determine that using this threshold.
+      moving_average_for_momentum: Whether to use moving average for momentum
+        instead of exponential moving average.
+      skip_preconditioning_dim_size_gt: Skip if preconditioning dim size is
+        greater than this value.
+      clip_by_scaled_gradient_norm: Clip by scaled gradient norm (only useful when
+        using RMSProp Grafting).
+      precision: precision XLA related flag, the available options are: a)
+        lax.Precision.DEFAULT (better step time, but not precise) b)
+        lax.Precision.HIGH (increased precision, slower) c) lax.Precision.HIGHEST
+        (best possible precision, slowest)
+
+    Returns:
+      a GradientTransformation.
+    """
+
+    def _graft_type_has_diagonal_statistics():
+        """Returns True if using diagonal first order method for grafting."""
+        return graft_type != GraftingType.SGD and graft_type != GraftingType.SQRT_N
+
+    def _graft_type_has_diagonal_momentum_states():
+        """Returns False if using SQRT_N for grafting."""
+        return graft_type != GraftingType.SQRT_N
+
+    def quantized_dtype_for_momentum_buffers():
+        return jnp.int8 if best_effort_memory_usage_reduction else jnp.float32
+
+    # TODO(rohananil): Explore int8-16 quantization with non-linear bucket sizes.
+    def quantized_dtype_for_diagonal_statistics_buffers():
+        return jnp.float32
+
+    # Preconditioner and statistics are both stores as int16 in this mode.
+    # We take out the diagonal to make quantization easier.
+    def quantized_dtype_for_second_moment_statistics_buffers():
+        return (
+            jnp.int16
+            if best_effort_memory_usage_reduction and batch_axis_name
+            else jnp.float32
+        )
+
+    # Preconditioner and statistics are both stores as int16 in this mode.
+    # We take out the diagonal to make quantization easier.
+    def quantized_dtype_for_second_moment_preconditioner_buffers():
+        return (
+            jnp.int16
+            if best_effort_memory_usage_reduction and batch_axis_name
+            else jnp.float32
+        )
+
+    def _to_float(maybe_quantized):
+        if isinstance(maybe_quantized, QuantizedValue):
+            return maybe_quantized.to_float()
+        else:
+            return maybe_quantized
+
+    def _maybe_quantize_statistics(statistics_list):
+        return _maybe_quantize_matrices_with_dtype(
+            statistics_list, quantized_dtype_for_second_moment_statistics_buffers()
+        )
+
+    def _maybe_quantize_preconditioners(statistics_list):
+        return _maybe_quantize_matrices_with_dtype(
+            statistics_list, quantized_dtype_for_second_moment_preconditioner_buffers()
+        )
+
+    def _maybe_quantize_matrices_with_dtype(statistics_list, quantized_dtype):
+        if quantized_dtype != jnp.float32:
+            return [
+                QuantizedValue.from_float_value(
+                    s, quantized_dtype, extract_diagonal=True
+                )
+                for s in statistics_list
+            ]
+        else:
+            return statistics_list
+
+    def _maybe_dequantize_preconditioners(preconditioner_list):
+        return _maybe_dequantize_matrices_with_dtype(
+            preconditioner_list,
+            quantized_dtype_for_second_moment_preconditioner_buffers(),
+        )
+
+    def _maybe_dequantize_matrices_with_dtype(statistics_list, quantized_dtype):
+        if quantized_dtype != jnp.float32:
+            return [s.to_float() for s in statistics_list]
+        else:
+            return statistics_list
+
+    def _quantize_diagonal_statistics(diagonal_statistics):
+        return QuantizedValue.from_float_value(
+            diagonal_statistics, quantized_dtype_for_diagonal_statistics_buffers()
+        )
+
+    def _quantize_momentum(momentum_statistics):
+        return QuantizedValue.from_float_value(
+            momentum_statistics, quantized_dtype_for_momentum_buffers()
+        )
+
+    def sharded_init_fn(params):
+        """Returns optimizer state (for PJIT mode).
+
+        Args:
+          params: the parameters that should be updated.
+        """
+        params_flat, treedef = jax.tree_flatten(params)
+        # Find max size to pad to.
+        max_size = 0
+        for param in params_flat:
+            preconditioner = Preconditioner(
+                param, block_size, best_effort_shape_interpretation
+            )
+            if not _skip_preconditioning(param):
+                shapes = preconditioner.shapes_for_preconditioners()
+                sizes = [s[0] for s in shapes]
+                max_size = max(max(sizes), max_size)
+
+        padded_statistics = []
+        padded_preconditioners = []
+        local_stats_flat = []
+        exponents = []
+        for param in params_flat:
+            preconditioner = Preconditioner(
+                param, block_size, best_effort_shape_interpretation
+            )
+            shapes = preconditioner.shapes_for_preconditioners()
+            sizes = []
+
+            statistics = []
+            preconditioners = []
+            index_start = len(padded_statistics)
+            if not _skip_preconditioning(param):
+                sizes = [s[0] for s in shapes]
+                shapes = preconditioner.shapes_for_preconditioners()
+                statistics = [
+                    matrix_epsilon * jnp.eye(max_size, dtype=jnp.float32)
+                    for s in shapes
+                ]
+                preconditioners = [jnp.eye(max_size, dtype=jnp.float32) for s in shapes]
+                padded_statistics.extend(statistics)
+                padded_preconditioners.extend(preconditioners)
+                exponent = (
+                    preconditioner.exponent_for_preconditioner()
+                    if exponent_override == 0
+                    else exponent_override
+                )
+                exponents.extend([exponent] * len(shapes))
+
+            diagonal_statistics = []
+            if _graft_type_has_diagonal_statistics():
+                diagonal_statistics = jnp.zeros_like(param)
+
+            diagonal_momentum = _quantize_momentum([])
+            momentum = _quantize_momentum(jnp.zeros_like(param))
+            if _graft_type_has_diagonal_momentum_states():
+                diagonal_momentum = _quantize_momentum((jnp.zeros_like(param)))
+
+            local_stats_flat.append(
+                LocalShardedParameterStats(
+                    _quantize_diagonal_statistics(diagonal_statistics),
+                    diagonal_momentum,
+                    momentum,
+                    init_training_metrics(len(sizes)),
+                    index_start,
+                    sizes,
+                )
+            )
+
+        local_stats = jax.tree_unflatten(treedef, local_stats_flat)
+        to_pad = -len(padded_statistics) % num_devices_for_pjit
+        if max_size == 0:
+            to_pad = num_devices_for_pjit
+            max_size = block_size
+            stat_dtype = jnp.float32
+        else:
+            stat_dtype = padded_statistics[0].dtype
+        # Pad the statistics and preconditioner matrices to be a multiple of
+        # num devices.
+        # TODO(rohananil): Relax to only the size of the mesh axis where the dim
+        # is split on.
+        padded_statistics.extend(
+            [jnp.eye(max_size, dtype=stat_dtype) for _ in range(to_pad)]
+        )
+        padded_preconditioners.extend(
+            [jnp.eye(max_size, dtype=stat_dtype) for _ in range(to_pad)]
+        )
+        exponents.extend([1 for _ in range(to_pad)])
+        global_stats = GlobalShardedParameterStats(
+            jnp.stack(padded_statistics),
+            jnp.stack(padded_preconditioners),
+            jnp.stack(exponents),
+        )
+        return ShampooState(
+            count=jnp.zeros([], jnp.int32),
+            stats=ShardedShampooStats(global_stats, local_stats),
+        )
+
+    def _max_statistics_size_from_params(params):
+        max_size = 0
+        for param in params:
+            param_clone = jnp.zeros(param.shape, dtype=param.dtype)
+            preconditioner = Preconditioner(
+                param_clone, block_size, best_effort_shape_interpretation
+            )
+            if not _skip_preconditioning(param):
+                shapes = preconditioner.shapes_for_preconditioners()
+                sizes = [s[0] for s in shapes]
+                max_size = max(max(sizes), max_size)
+        return max_size
+
+    def _remove_leading_sharding_annotation(pspec):
+        """Mapping from N-d to (N-1)-d, used for quantization, factoring etc."""
+        # None and PSpec(None) are valid PSpecs.
+        if pspec and len(pspec) > 1:
+            return pjit.PartitionSpec(*pspec[1:])
+        else:
+            return []
+
+    def sharded_init_partition_spec_fn(
+        params, params_partition_spec, partition_spec_for_statistics
+    ):
+        """Returns a parallel state tree with PartitionSpec associated with state.
+
+
+        Args:
+          params: A pytree with params.
+          params_partition_spec: A pytree with PartitionSpec for params.
+          partition_spec_for_statistics: PartitionSpec for the statistics.
+        """
+        # Parallel lists of spec, and params.
+        param_pspec_flat, _ = jax.tree_flatten(
+            params_partition_spec, is_leaf=lambda x: x is None
+        )
+        params_flat, treedef = jax.tree_flatten(params)
+        assert param_pspec_flat
+        assert params_flat
+        # Step is replicated across cores.
+        # None means cores.
+        local_stats_flat = []
+        num_statistics = 0
+        for param, param_pspec in zip(params_flat, param_pspec_flat):
+            param_clone = jnp.zeros(param.shape, dtype=param.dtype)
+            preconditioner = Preconditioner(
+                param_clone, block_size, best_effort_shape_interpretation
+            )
+            shapes = preconditioner.shapes_for_preconditioners()
+            sizes = []
+
+            index_start = num_statistics
+            if not _skip_preconditioning(param):
+                sizes = [s[0] for s in shapes]
+                shapes = preconditioner.shapes_for_preconditioners()
+                num_statistics += len(shapes)
+
+            diagonal_statistics_pspec = []
+            diagonal_statistics_scale_pspec = []
+            diagonal_statistics_shape = []
+            if _graft_type_has_diagonal_statistics():
+                # Identically shaped param.
+                diagonal_statistics_pspec = param_pspec
+                diagonal_statistics_shape = list(param.shape)
+                if quantized_dtype_for_diagonal_statistics_buffers() != jnp.float32:
+                    diagonal_statistics_scale_pspec = (
+                        _remove_leading_sharding_annotation(param_pspec)
+                    )
+
+            m1_pspec = []
+            m1_shape = []
+            m1_scale_pspec = []
+            if _graft_type_has_diagonal_momentum_states():
+                m1_pspec = param_pspec
+                m1_shape = list(param.shape)
+                if quantized_dtype_for_momentum_buffers() != jnp.float32:
+                    m1_scale_pspec = _remove_leading_sharding_annotation(m1_pspec)
+
+            m2_pspec = param_pspec
+            m2_scale_pspec = []
+            if quantized_dtype_for_momentum_buffers() != jnp.float32:
+                m2_scale_pspec = _remove_leading_sharding_annotation(m2_pspec)
+
+            local_stats_flat.append(
+                LocalShardedParameterStats(
+                    QuantizedValue(
+                        diagonal_statistics_pspec,
+                        [],
+                        diagonal_statistics_scale_pspec,
+                        quantized_dtype_for_diagonal_statistics_buffers(),
+                        False,
+                        diagonal_statistics_shape,
+                    ),
+                    QuantizedValue(
+                        m1_pspec,
+                        [],
+                        m1_scale_pspec,
+                        quantized_dtype_for_momentum_buffers(),
+                        False,
+                        m1_shape,
+                    ),
+                    QuantizedValue(
+                        m2_pspec,
+                        [],
+                        m2_scale_pspec,
+                        quantized_dtype_for_momentum_buffers(),
+                        False,
+                        list(param.shape),
+                    ),
+                    init_training_metrics_pspec(),
+                    index_start,
+                    sizes,
+                )
+            )
+
+        local_stats = jax.tree_unflatten(treedef, local_stats_flat)
+        global_stats = GlobalShardedParameterStats(
+            partition_spec_for_statistics,
+            partition_spec_for_statistics,
+            pjit.PartitionSpec(),
+        )
+        count_pspec = pjit.PartitionSpec()
+        return ShampooState(
+            count=count_pspec, stats=ShardedShampooStats(global_stats, local_stats)
+        )
+
+    def sharded_init_shape_and_dtype_fn(params):
+        """Returns a parallel state tree with shape, dtype associated with state.
+
+
+        Args:
+          params: A pytree with params.
+        """
+        # Parallel lists of spec, and params.
+        params_flat, treedef = jax.tree_flatten(params)
+        assert params_flat
+        # Step is replicated across cores.
+        # None means cores.
+        local_stats_flat = []
+        num_statistics = 0
+        for param in params_flat:
+            param_clone = jnp.zeros(param.shape, dtype=param.dtype)
+            preconditioner = Preconditioner(
+                param_clone, block_size, best_effort_shape_interpretation
+            )
+            shapes = preconditioner.shapes_for_preconditioners()
+            sizes = []
+
+            index_start = num_statistics
+            if not _skip_preconditioning(param):
+                sizes = [s[0] for s in shapes]
+                shapes = preconditioner.shapes_for_preconditioners()
+                num_statistics += len(shapes)
+
+            diagonal_statistics_shape_and_dtype = []
+            diagonal_statistics_scale_shape_and_dtype = []
+            if _graft_type_has_diagonal_statistics():
+                diagonal_statistics_shape_and_dtype = [list(param.shape), param.dtype]
+                qdtype = quantized_dtype_for_diagonal_statistics_buffers()
+                if qdtype != jnp.float32:
+                    diagonal_statistics_shape_and_dtype = [list(param.shape), qdtype]
+                    diagonal_statistics_scale_shape_and_dtype = [
+                        list(param.shape)[1:],
+                        param.dtype,
+                    ]
+
+            qdtype = quantized_dtype_for_momentum_buffers()
+            m1_shape_and_dtype = []
+            m1_scale_shape_and_dtype = []
+            if _graft_type_has_diagonal_momentum_states():
+                m1_shape_and_dtype = [list(param.shape), qdtype]
+                if quantized_dtype_for_momentum_buffers() != jnp.float32:
+                    m1_scale_shape_and_dtype = [list(param.shape)[1:], qdtype]
+
+            m2_shape_and_dtype = [list(param.shape), param.dtype]
+            m2_scale_shape_and_dtype = []
+            if qdtype != jnp.float32:
+                m2_shape_and_dtype = [list(param.shape), qdtype]
+                m2_scale_shape_and_dtype = [list(param.shape)[1:], qdtype]
+
+            local_stats_flat.append(
+                LocalShardedParameterStats(
+                    QuantizedValue(
+                        diagonal_statistics_shape_and_dtype,
+                        [],
+                        diagonal_statistics_scale_shape_and_dtype,
+                        quantized_dtype_for_diagonal_statistics_buffers(),
+                        False,
+                        list(param.shape),
+                    ),
+                    QuantizedValue(
+                        m1_shape_and_dtype,
+                        [],
+                        m1_scale_shape_and_dtype,
+                        quantized_dtype_for_momentum_buffers(),
+                        False,
+                        list(param.shape),
+                    ),
+                    QuantizedValue(
+                        m2_shape_and_dtype,
+                        [],
+                        m2_scale_shape_and_dtype,
+                        quantized_dtype_for_momentum_buffers(),
+                        False,
+                        list(param.shape),
+                    ),
+                    init_training_metrics_shapes(len(sizes)),
+                    index_start,
+                    sizes,
+                )
+            )
+
+        local_stats = jax.tree_unflatten(treedef, local_stats_flat)
+        max_statistics_size = _max_statistics_size_from_params(params_flat)
+        to_pad = -num_statistics % num_devices_for_pjit
+        num_statistics += to_pad
+        if num_statistics == 0:
+            num_statistics = num_devices_for_pjit
+            max_statistics_size = block_size
+        statistics_shape = [num_statistics, max_statistics_size, max_statistics_size]
+        global_stats = GlobalShardedParameterStats(
+            [statistics_shape, jnp.float32],
+            [statistics_shape, jnp.float32],
+            [[num_statistics], jnp.int32],
+        )
+        return ShampooState(
+            count=[[], jnp.float32],
+            stats=ShardedShampooStats(global_stats, local_stats),
+        )
+
+    def sharded_update_fn(grads, state, params):
+        """Transform the input gradient and update all statistics in sharded mode.
+
+        Args:
+          grads: the gradient tensors for the parameters.
+          state: a named tuple containing the state of the optimizer
+          params: the parameters that should be updated.
+
+        Returns:
+          A tuple containing the new parameters and the new optimizer state.
+        """
+        params_flat, treedef = jax.tree_flatten(params)
+        grads_flat = treedef.flatten_up_to(grads)
+
+        global_stats = state.stats.global_stats
+        local_stats_flat = treedef.flatten_up_to(state.stats.local_stats)
+        stats_flat = [
+            _convert_to_parameter_stats(global_stats, local_stat)
+            for local_stat in local_stats_flat
+        ]
+        new_stats_flat = jax.tree_multimap(
+            lambda g, s, p: _compute_stats(g, s, p, state.count),
+            grads_flat,
+            stats_flat,
+            params_flat,
+        )
+
+        outputs = jax.tree_multimap(
+            lambda g, s, p: _transform_grad(g, s, p, state.count),
+            grads_flat,
+            new_stats_flat,
+            params_flat,
+        )
+        updates_flat, new_stats_flat = list(zip(*outputs)) if outputs else ((), ())
+
+        updates = jax.tree_unflatten(treedef, updates_flat)
+        # Create new local_stats
+        new_local_stats_flat = [
+            _convert_from_parameter_stats(new_stat, local_stat)
+            for new_stat, local_stat in zip(new_stats_flat, local_stats_flat)
+        ]
+
+        max_size = global_stats.statistics.shape[1]
+        new_padded_statistics = []
+        for stat in new_stats_flat:
+            new_padded_statistics.extend(
+                [pad_square_matrix(stat, max_size) for stat in stat.statistics]
+            )
+
+        # Create global stats
+        # TODO(rohananil): Preconditioner is not updated every step, so cost of
+        # stack/pad can be obviated away.
+        # Pad the statistics and preconditioner matrices to be a multiple of
+        # num devices.
+        # TODO(rohananil): Relax to only the size of the mesh axis where the dim
+        # is split on.
+        to_pad = -len(new_padded_statistics) % num_devices_for_pjit
+        new_padded_statistics.extend(
+            [
+                jnp.eye(max_size, dtype=new_padded_statistics[0].dtype)
+                for _ in range(to_pad)
+            ]
+        )
+        new_stacked_padded_statistics = jnp.stack(new_padded_statistics)
+        new_stacked_padded_statistics = pjit.with_sharding_constraint(
+            new_stacked_padded_statistics, statistics_partition_spec
+        )
+
+        def _internal_inverse_pth_root_all():
+            preconditioners, errors = _matrix_inverse_pth_root_pjit(
+                new_stacked_padded_statistics,
+                global_stats.exponents,
+                statistics_partition_spec,
+            )
+            return preconditioners, errors
+
+        if preconditioning_compute_steps == 1:
+            new_preconditioners, errors = _internal_inverse_pth_root_all()
+        else:
+            # Passing statistics instead of preconditioners as they are similarly
+            # shaped tensors. Note statistics will be ignored as we are passing in
+            # a large init value for error.
+            preconditioners_init = new_stacked_padded_statistics
+            n = new_stacked_padded_statistics.shape[0]
+            errors_init = jnp.ones([n], jnp.float32) * inverse_failure_threshold
+            init_state = [preconditioners_init, errors_init]
+            perform_step = state.count % preconditioning_compute_steps == 0
+            new_preconditioners, errors = efficient_cond(
+                perform_step, _internal_inverse_pth_root_all, init_state
+            )
+
+        new_local_stats_flat = _add_error_into_local_stats(
+            new_local_stats_flat, errors, inverse_failure_threshold
+        )
+        new_local_stats = jax.tree_unflatten(treedef, new_local_stats_flat)
+        errors = errors.reshape((-1, 1, 1))
+        predicate = jnp.logical_or(
+            jnp.isnan(errors), errors >= inverse_failure_threshold
+        ).astype(new_preconditioners.dtype)
+        # TODO(rohananil): Check for numerical instabilities.
+        new_conditional_preconditioners = (
+            predicate * global_stats.preconditioners
+            + (1.0 - predicate) * new_preconditioners
+        )
+        new_global_stats = GlobalShardedParameterStats(
+            new_stacked_padded_statistics,
+            new_conditional_preconditioners,
+            global_stats.exponents,
+        )
+        new_shampoo_state = ShampooState(
+            count=state.count + 1,
+            stats=ShardedShampooStats(new_global_stats, new_local_stats),
+        )
+        return updates, new_shampoo_state
+
+    def init_fn(params):
+        """Initialise the optimiser's state."""
+
+        def _init(param):
+            preconditioner = Preconditioner(
+                param, block_size, best_effort_shape_interpretation
+            )
+            statistics = []
+            preconditioners = []
+            if not _skip_preconditioning(param):
+                shapes = preconditioner.shapes_for_preconditioners()
+                statistics = [
+                    matrix_epsilon * jnp.eye(s[0], dtype=jnp.float32) for s in shapes
+                ]
+                preconditioners = [jnp.eye(s[0], dtype=jnp.float32) for s in shapes]
+
+            diagonal_statistics = []
+            if _graft_type_has_diagonal_statistics():
+                diagonal_statistics = jnp.zeros_like(param)
+
+            diagonal_momentum = _quantize_momentum([])
+            momentum = _quantize_momentum(jnp.zeros_like(param))
+            if _graft_type_has_diagonal_momentum_states():
+                diagonal_momentum = _quantize_momentum(jnp.zeros_like(param))
+
+            return ParameterStats(
+                _quantize_diagonal_statistics(diagonal_statistics),
+                _maybe_quantize_statistics(statistics),
+                _maybe_quantize_preconditioners(preconditioners),
+                diagonal_momentum,
+                momentum,
+                init_training_metrics(len(statistics)),
+            )
+
+        return ShampooState(
+            count=jnp.zeros([], jnp.int32), stats=jax.tree_map(_init, params)
+        )
+
+    def _skip_preconditioning(param):
+        return len(param.shape) < 1 or any(
+            [s > skip_preconditioning_dim_size_gt for s in param.shape]
+        )
+
+    def _compute_stats(grad, state, param, step):
+        """Compute per-parameter statistics."""
+        preconditioner = Preconditioner(
+            param, block_size, best_effort_shape_interpretation
+        )
+        new_statistics = [[]] * len(state.statistics)
+        w1 = beta2
+        w2 = beta2 if beta2 == 1.0 else (1.0 - beta2)
+        if not _skip_preconditioning(param):
+
+            def compute_updated_statistics():
+                new_stats = preconditioner.statistics_from_grad(grad)
+                new_stats_accumulators = []
+                for stat, stat_accumulator in zip(new_stats, state.statistics):
+                    new_stats_accumulators.append(
+                        w1 * _to_float(stat_accumulator) + w2 * stat
+                    )
+                return _maybe_quantize_statistics(new_stats_accumulators)
+
+            if statistics_compute_steps > 1:
+                perform_step = step % statistics_compute_steps == 0
+                init_state = state.statistics
+                new_statistics = list(
+                    efficient_cond(perform_step, compute_updated_statistics, init_state)
+                )
+            else:
+                new_statistics = compute_updated_statistics()
+        return ParameterStats(
+            state.diagonal_statistics,
+            new_statistics,
+            state.preconditioners,
+            state.diagonal_momentum,
+            state.momentum,
+            state.training_metrics,
+        )
+
+    def _matrix_inverse_pth_root_vmap(xs, ps):
+        mi_pth_root = functools.partial(
+            matrix_inverse_pth_root, ridge_epsilon=matrix_epsilon, precision=precision
+        )
+        return jax.vmap(mi_pth_root)(xs, ps)
+
+    def _quantized_matrix_inverse_pth_root_vmap(qxs, qds, qbs, ps):
+        def _quantized_to_float(qx, qd, qb):
+            qv = QuantizedValue(qx, qd, qb, qx.dtype, True, list(qx.shape))
+            return qv.to_float()
+
+        def matrix_inverse_pth_root_wrapper(qx, qd, qb, p):
+            v = _quantized_to_float(qx, qd, qb)
+            preconditioner, error = matrix_inverse_pth_root(
+                v, p, ridge_epsilon=matrix_epsilon, precision=precision
+            )
+            qp = QuantizedValue.from_float_value(preconditioner, qx.dtype, True)
+            return qp.quantized, qp.diagonal, qp.bucket_size, error
+
+        return jax.vmap(matrix_inverse_pth_root_wrapper)(qxs, qds, qbs, ps)
+
+    def _matrix_inverse_pth_root_pjit(xs, ps, statistics_partition_spec=None):
+        # Partition the concatenated statistics matrix across all cores.
+        pspec_for_partition = preconditioner_partition_spec
+        partitioned_xs = pjit.with_sharding_constraint(xs, pspec_for_partition)
+        if preconditioner_partition_spec:
+            partitioned_ps_spec = pjit.PartitionSpec(preconditioner_partition_spec[0])
+        else:
+            partitioned_ps_spec = None
+        partitioned_ps = pjit.with_sharding_constraint(ps, partitioned_ps_spec)
+        # Run matrix inverse pth root on each shard.
+        partitioned_preconditioners, partitioned_errors = _matrix_inverse_pth_root_vmap(
+            partitioned_xs, partitioned_ps
+        )
+        # Reshard output to have the same PSpec as input. This is required to avoid
+        # vmap seeing the full set of statistics.
+        partitioned_preconditioners = pjit.with_sharding_constraint(
+            partitioned_preconditioners, pspec_for_partition
+        )
+        # Recombine the outputs at each core.
+        preconditioners = pjit.with_sharding_constraint(
+            partitioned_preconditioners, statistics_partition_spec
+        )
+        errors = pjit.with_sharding_constraint(partitioned_errors, pjit.PartitionSpec())
+        return preconditioners, errors
+
+    def _pmap_compute_preconditioners(
+        states,
+        step,
+        statistics,
+        num_statistics_per_state,
+        original_shapes,
+        exponents,
+        max_size,
+        prev_preconditioners,
+    ):
+        """Computes preconditioners for given statistics in states in PMAP mode.
+
+        Args:
+          states: A list of optimizer states.
+          step: Current step number
+          statistics: A list of statistics for all variables (for every dim)
+          num_statistics_per_state: Number of statistis per state to reconstruct
+            output states.
+          original_shapes: A list of shapes of the statistics.
+          exponents: Exponent power to use for inverse-pth roots.
+          max_size: Maximum dim of the statistics to pad.
+          prev_preconditioners: Previously available preconditioner.
+
+        Returns:
+          New optimizer states after computing the preconditioner.
+        """
+        num_devices = lax.psum(1, batch_axis_name)
+        num_statistics = len(statistics)
+        # Pad statistics and exponents to next multiple of num_devices.
+        packed_statistics = [pad_square_matrix(stat, max_size) for stat in statistics]
+        to_pad = -num_statistics % num_devices
+        packed_statistics.extend(
+            [jnp.eye(max_size, dtype=packed_statistics[0].dtype) for _ in range(to_pad)]
+        )
+        exponents.extend([1 for _ in range(to_pad)])
+
+        if not packed_statistics:
+            return states
+
+        all_statistics = batch(packed_statistics, num_devices)
+        all_exponents = batch(exponents, num_devices)
+
+        def _internal_inverse_pth_root_all():
+            current_replica = lax.axis_index(batch_axis_name)
+            preconditioners, errors = _matrix_inverse_pth_root_vmap(
+                all_statistics[current_replica], all_exponents[current_replica]
+            )
+            preconditioners = jax.lax.all_gather(preconditioners, batch_axis_name)
+            errors = jax.lax.all_gather(errors, batch_axis_name)
+            preconditioners_flat = unbatch(preconditioners)
+            errors_flat = unbatch(errors)
+            return preconditioners_flat, errors_flat
+
+        if preconditioning_compute_steps == 1:
+            preconditioners_flat, errors_flat = _internal_inverse_pth_root_all()
+        else:
+            # Passing statistics instead of preconditioners as they are similarly
+            # shaped tensors. Note statistics will be ignored as we are passing in
+            # a large init value for error.
+            preconditioners_init = packed_statistics
+            errors_init = [inverse_failure_threshold] * len(packed_statistics)
+            init_state = [preconditioners_init, errors_init]
+            perform_step = step % preconditioning_compute_steps == 0
+            preconditioners_flat, errors_flat = efficient_cond(
+                perform_step, _internal_inverse_pth_root_all, init_state
+            )
+
+        def _skip(error):
+            condition = jnp.logical_or(
+                jnp.isnan(error), error >= inverse_failure_threshold
+            )
+            return condition.astype(error.dtype)
+
+        def _select_preconditioner(error, new_p, old_p):
+            return lax.cond(
+                _skip(error), lambda _: old_p, lambda _: new_p, operand=None
+            )
+
+        new_preconditioners_flat = []
+        new_errors_flat = []
+        for p, shape, prev_p, error in zip(
+            preconditioners_flat, original_shapes, prev_preconditioners, errors_flat
+        ):
+            new_preconditioners_flat.append(
+                _select_preconditioner(error, p[: shape[0], : shape[1]], prev_p)
+            )
+            new_errors_flat.append(error)
+
+        assert len(states) == len(num_statistics_per_state)
+        assert len(new_preconditioners_flat) == num_statistics
+        assert len(new_errors_flat) == num_statistics
+
+        # Add back empty preconditioners so we that we can set the optimizer state.
+        preconditioners_for_states = []
+        idx = 0
+        errors_for_states = []
+        for num_statistics, state in zip(num_statistics_per_state, states):
+            if num_statistics == 0:
+                preconditioners_for_states.append([])
+                errors_for_states.append(jnp.array(0, jnp.float32))
+            else:
+                preconditioners_for_state = new_preconditioners_flat[
+                    idx : idx + num_statistics
+                ]
+                assert len(state.statistics) == len(preconditioners_for_state)
+                preconditioners_for_states.append(preconditioners_for_state)
+
+                errors_for_state = jnp.stack(
+                    new_errors_flat[idx : idx + num_statistics]
+                )
+                assert len(state.statistics) == len(errors_for_state)
+                errors_for_states.append(errors_for_state)
+
+                idx += num_statistics
+        new_states = []
+        for state, new_preconditioners, new_errors in zip(
+            states, preconditioners_for_states, errors_for_states
+        ):
+            if state.statistics:
+                new_errors = jnp.where(
+                    jnp.logical_and(
+                        new_errors > 0.0, new_errors != inverse_failure_threshold
+                    ),
+                    new_errors,
+                    state.training_metrics.inverse_pth_root_errors,
+                )
+            new_training_metrics = TrainingMetrics(new_errors)
+            new_states.append(
+                ParameterStats(
+                    state.diagonal_statistics,
+                    state.statistics,
+                    new_preconditioners,
+                    state.diagonal_momentum,
+                    state.momentum,
+                    new_training_metrics,
+                )
+            )
+
+        return new_states
+
+    def _pmap_quantized_compute_preconditioners(
+        states,
+        step,
+        statistics,
+        num_statistics_per_state,
+        original_shapes,
+        exponents,
+        max_size,
+        prev_preconditioners,
+    ):
+        """Computes preconditioners for given statistics in states in PMAP mode.
+
+        For quantization, each statistic is represented by three values:
+          quantized matrix, diagonal, and bucket sizes, we run inverse pth-roots
+          without ever recreating the original matrix in f32.
+
+        Args:
+          states: A list of optimizer states.
+          step: Current step number
+          statistics: A list of statistics for all variables (for every dim)
+          num_statistics_per_state: Number of statistis per state to reconstruct
+            output states.
+          original_shapes: A list of shapes of the statistics.
+          exponents: Exponent power to use for inverse-pth roots.
+          max_size: Maximum dim of the statistics to pad.
+          prev_preconditioners: Previously available preconditioner.
+
+        Returns:
+          New optimizer states after computing the preconditioner.
+        """
+        num_devices = lax.psum(1, batch_axis_name)
+        num_statistics = len(statistics)
+        quantized_dtype = quantized_dtype_for_second_moment_statistics_buffers()
+        # Complexity here is around: shapes needing be statically shaped,
+        # our custom quantization type requires a different type of packing.
+
+        # Parallel tensors:
+        # quantized [dxd]
+        # diagonals [d] f32
+        # bucket_sizes [d] f32
+        packed_quantized_statistics = [
+            pad_square_matrix(stat.quantized, max_size) for stat in statistics
+        ]
+        packed_quantized_diagonals = [
+            pad_vector(stat.diagonal, max_size) for stat in statistics
+        ]
+        packed_quantized_bucket_sizes = [
+            pad_vector(stat.bucket_size, max_size) for stat in statistics
+        ]
+
+        to_pad = -num_statistics % num_devices
+        padded_eye = jnp.eye(max_size, dtype=jnp.float32)
+        quantized_eye = QuantizedValue.from_float_value(
+            padded_eye, quantized_dtype, True
+        )
+        packed_quantized_statistics.extend(
+            [quantized_eye.quantized for _ in range(to_pad)]
+        )
+        packed_quantized_diagonals.extend(
+            [quantized_eye.diagonal for _ in range(to_pad)]
+        )
+        packed_quantized_bucket_sizes.extend(
+            [quantized_eye.bucket_size for _ in range(to_pad)]
+        )
+        exponents.extend([1 for _ in range(to_pad)])
+
+        if not packed_quantized_statistics:
+            return states
+
+        all_quantized_statistics = batch(packed_quantized_statistics, num_devices)
+        all_quantized_diagonals = batch(packed_quantized_diagonals, num_devices)
+        all_quantized_bucket_sizes = batch(packed_quantized_bucket_sizes, num_devices)
+        all_exponents = batch(exponents, num_devices)
+
+        def _internal_inverse_pth_root_all():
+            current_replica = lax.axis_index(batch_axis_name)
+            (
+                quantized_preconditioners,
+                quantized_diagonals,
+                quantized_bucket_sizes,
+                errors,
+            ) = _quantized_matrix_inverse_pth_root_vmap(
+                all_quantized_statistics[current_replica],
+                all_quantized_diagonals[current_replica],
+                all_quantized_bucket_sizes[current_replica],
+                all_exponents[current_replica],
+            )
+            quantized_preconditioners = jax.lax.all_gather(
+                quantized_preconditioners, batch_axis_name
+            )
+            quantized_diagonals = jax.lax.all_gather(
+                quantized_diagonals, batch_axis_name
+            )
+            quantized_bucket_sizes = jax.lax.all_gather(
+                quantized_bucket_sizes, batch_axis_name
+            )
+            errors = jax.lax.all_gather(errors, batch_axis_name)
+            quantized_preconditioners_flat = unbatch(quantized_preconditioners)
+            quantized_diagonals_flat = unbatch(quantized_diagonals)
+            quantized_bucket_sizes_flat = unbatch(quantized_bucket_sizes)
+            errors_flat = unbatch(errors)
+            return (
+                quantized_preconditioners_flat,
+                quantized_diagonals_flat,
+                quantized_bucket_sizes_flat,
+                errors_flat,
+            )
+
+        if preconditioning_compute_steps == 1:
+            (
+                quantized_preconditioners_flat,
+                quantized_diagonals_flat,
+                quantized_bucket_sizes_flat,
+                errors_flat,
+            ) = _internal_inverse_pth_root_all()
+        else:
+            # Passing statistics instead of preconditioners as they are similarly
+            # shaped tensors. Note statistics will be ignored as we are passing in
+            # a large init value for error.
+            quantized_preconditioners_init = packed_quantized_statistics
+            quantized_diagonals_init = packed_quantized_diagonals
+            quantized_bucket_sizes_init = packed_quantized_bucket_sizes
+            errors_init = [inverse_failure_threshold] * len(
+                quantized_preconditioners_init
+            )
+            init_state = [
+                quantized_preconditioners_init,
+                quantized_diagonals_init,
+                quantized_bucket_sizes_init,
+                errors_init,
+            ]
+            perform_step = step % preconditioning_compute_steps == 0
+            (
+                quantized_preconditioners_flat,
+                quantized_diagonals_flat,
+                quantized_bucket_sizes_flat,
+                errors_flat,
+            ) = efficient_cond(perform_step, _internal_inverse_pth_root_all, init_state)
+
+        def _skip(error):
+            condition = jnp.logical_or(
+                jnp.isnan(error), error >= inverse_failure_threshold
+            )
+            return condition.astype(error.dtype)
+
+        def _select_preconditioner(error, new_p, old_p):
+            return lax.cond(
+                _skip(error), lambda _: old_p, lambda _: new_p, operand=None
+            )
+
+        new_quantized_preconditioners_flat = []
+        new_quantized_diagonals_flat = []
+        new_quantized_bucket_sizes_flat = []
+        new_errors_flat = []
+        for p, d, b, shape, prev_p, error in zip(
+            quantized_preconditioners_flat,
+            quantized_diagonals_flat,
+            quantized_bucket_sizes_flat,
+            original_shapes,
+            prev_preconditioners,
+            errors_flat,
+        ):
+            new_quantized_preconditioners_flat.append(
+                _select_preconditioner(
+                    error, p[: shape[0], : shape[1]], prev_p.quantized
+                )
+            )
+            new_quantized_diagonals_flat.append(
+                _select_preconditioner(error, d[: shape[0]], prev_p.diagonal)
+            )
+            new_quantized_bucket_sizes_flat.append(
+                _select_preconditioner(error, b[: shape[0]], prev_p.bucket_size)
+            )
+            new_errors_flat.append(error)
+
+        assert len(states) == len(num_statistics_per_state)
+        assert len(new_quantized_preconditioners_flat) == num_statistics
+        assert len(new_quantized_diagonals_flat) == num_statistics
+        assert len(new_quantized_bucket_sizes_flat) == num_statistics
+
+        # Add back empty preconditioners so we that we can set the optimizer state.
+        preconditioners_for_states = []
+        errors_for_states = []
+        idx = 0
+        for num_statistics, state in zip(num_statistics_per_state, states):
+            if num_statistics == 0:
+                preconditioners_for_states.append([])
+                errors_for_states.append(jnp.array(0, jnp.float32))
+            else:
+                quantized_preconditioners_for_state = (
+                    new_quantized_preconditioners_flat[idx : idx + num_statistics]
+                )
+                quantized_diagonals_for_state = new_quantized_diagonals_flat[
+                    idx : idx + num_statistics
+                ]
+                quantized_bucket_sizes_for_state = new_quantized_bucket_sizes_flat[
+                    idx : idx + num_statistics
+                ]
+                errors_for_state = jnp.stack(
+                    new_errors_flat[idx : idx + num_statistics]
+                )
+
+                assert len(state.statistics) == len(quantized_preconditioners_for_state)
+                assert len(state.statistics) == len(quantized_diagonals_for_state)
+                assert len(state.statistics) == len(quantized_bucket_sizes_for_state)
+                assert len(state.statistics) == len(errors_for_state)
+
+                quantized_preconditioners = []
+                for qv, qd, qb in zip(
+                    quantized_preconditioners_for_state,
+                    quantized_diagonals_for_state,
+                    quantized_bucket_sizes_for_state,
+                ):
+                    quantized_preconditioners.append(
+                        QuantizedValue(qv, qd, qb, qv.dtype, True, list(qv.shape))
+                    )
+                preconditioners_for_states.append(quantized_preconditioners)
+                errors_for_states.append(errors_for_state)
+                idx += num_statistics
+        new_states = []
+        for state, new_preconditioners, new_errors in zip(
+            states, preconditioners_for_states, errors_for_states
+        ):
+            if state.statistics:
+                new_errors = jnp.where(
+                    jnp.logical_and(
+                        new_errors > 0.0, new_errors != inverse_failure_threshold
+                    ),
+                    new_errors,
+                    state.training_metrics.inverse_pth_root_errors,
+                )
+            new_training_metrics = TrainingMetrics(new_errors)
+            new_states.append(
+                ParameterStats(
+                    state.diagonal_statistics,
+                    state.statistics,
+                    new_preconditioners,
+                    state.diagonal_momentum,
+                    state.momentum,
+                    new_training_metrics,
+                )
+            )
+
+        return new_states
+
+    def _pjit_compute_preconditioners(
+        states,
+        step,
+        statistics,
+        num_statistics_per_state,
+        original_shapes,
+        exponents,
+        max_size,
+        prev_preconditioners,
+    ):
+        """Computes preconditioners for given statistics in states in PJIT mode.
+
+        Args:
+          states: A list of optimizer states.
+          step: Current step number
+          statistics: A list of statistics for all variables (for every dim)
+          num_statistics_per_state: Number of statistis per state to reconstruct
+            output states.
+          original_shapes: A list of shapes of the statistics.
+          exponents: Exponent power to use for inverse-pth roots.
+          max_size: Maximum dim of the statistics to pad.
+          prev_preconditioners: Previously available preconditioner.
+
+        Returns:
+          New optimizer states after computing the preconditioner.
+        """
+        num_statistics = len(statistics)
+        to_pad = -num_statistics % num_devices_for_pjit
+        padded_statistics = [pad_square_matrix(stat, max_size) for stat in statistics]
+        padded_statistics.extend(
+            [jnp.eye(max_size, dtype=padded_statistics[0].dtype) for _ in range(to_pad)]
+        )
+        exponents.extend([1 for _ in range(to_pad)])
+        all_statistics = jnp.stack(padded_statistics)
+        all_exponents = jnp.stack(exponents)
+
+        def _internal_inverse_pth_root_all():
+            preconditioners, errors = _matrix_inverse_pth_root_pjit(
+                all_statistics, all_exponents
+            )
+            b1 = preconditioners.shape[0]
+
+            def split(batched_values):
+                return [
+                    jnp.squeeze(v)
+                    for v in jnp.split(batched_values, indices_or_sections=b1, axis=0)
+                ]
+
+            return split(preconditioners), split(errors)
+
+        if preconditioning_compute_steps == 1:
+            preconditioners_flat, errors_flat = _internal_inverse_pth_root_all()
+        else:
+            # Passing statistics instead of preconditioners as they are similarly
+            # shaped tensors. Note statistics will be ignored as we are passing in
+            # a large init value for error.
+            preconditioners_init = padded_statistics
+            errors_init = [inverse_failure_threshold] * len(padded_statistics)
+            init_state = [preconditioners_init, errors_init]
+            perform_step = step % preconditioning_compute_steps == 0
+            preconditioners_flat, errors_flat = efficient_cond(
+                perform_step, _internal_inverse_pth_root_all, init_state
+            )
+
+        def _skip(error):
+            condition = jnp.logical_or(
+                jnp.isnan(error), error >= inverse_failure_threshold
+            )
+            return condition.astype(error.dtype)
+
+        def _select_preconditioner(error, new_p, old_p):
+            return lax.cond(
+                _skip(error), lambda _: old_p, lambda _: new_p, operand=None
+            )
+
+        new_preconditioners_flat = []
+        new_errors_flat = []
+        for p, shape, prev_p, error in zip(
+            preconditioners_flat, original_shapes, prev_preconditioners, errors_flat
+        ):
+            new_preconditioners_flat.append(
+                _select_preconditioner(error, p[: shape[0], : shape[1]], prev_p)
+            )
+            new_errors_flat.append(error)
+
+        assert len(states) == len(num_statistics_per_state)
+        assert len(new_preconditioners_flat) == num_statistics
+
+        # Add back empty preconditioners so we that we can set the optimizer state.
+        preconditioners_for_states = []
+        errors_for_states = []
+        idx = 0
+        for num_statistics, state in zip(num_statistics_per_state, states):
+            if num_statistics == 0:
+                preconditioners_for_states.append([])
+                errors_for_states.append(jnp.array(0, jnp.float32))
+            else:
+                preconditioners_for_state = new_preconditioners_flat[
+                    idx : idx + num_statistics
+                ]
+                assert len(state.statistics) == len(preconditioners_for_state)
+                preconditioners_for_states.append(preconditioners_for_state)
+
+                errors_for_state = jnp.stack(
+                    new_errors_flat[idx : idx + num_statistics]
+                )
+                assert len(state.statistics) == len(errors_for_state)
+                errors_for_states.append(errors_for_state)
+                idx += num_statistics
+
+        new_states = []
+        for state, new_preconditioners, new_errors in zip(
+            states, preconditioners_for_states, errors_for_states
+        ):
+            if state.statistics:
+                new_errors = jnp.where(
+                    jnp.logical_and(
+                        new_errors > 0.0, new_errors != inverse_failure_threshold
+                    ),
+                    new_errors,
+                    state.training_metrics.inverse_pth_root_errors,
+                )
+            new_training_metrics = TrainingMetrics(new_errors)
+            new_states.append(
+                ParameterStats(
+                    state.diagonal_statistics,
+                    state.statistics,
+                    new_preconditioners,
+                    state.diagonal_momentum,
+                    state.momentum,
+                    new_training_metrics,
+                )
+            )
+
+        return new_states
+
+    def _compute_preconditioners(states, params, step):
+        """Computes preconditioners for given statistics in states.
+
+        Args:
+          states: A list of optimizer states.
+          params: A list of params.
+          step: Current step number
+
+        Returns:
+          New optimizer states after computing the preconditioner.
+        """
+        statistics = []
+        num_statistics_per_state = []
+        original_shapes = []
+        exponents = []
+        max_size = 0
+        prev_preconditioners = []
+
+        for state, param in zip(states, params):
+            num_statistics = len(state.statistics)
+            num_statistics_per_state.append(num_statistics)
+            original_shapes_for_state = []
+            if num_statistics > 0:
+                preconditioner = Preconditioner(
+                    param, block_size, best_effort_shape_interpretation
+                )
+                for statistic in state.statistics:
+                    exponents.append(
+                        preconditioner.exponent_for_preconditioner()
+                        if exponent_override == 0
+                        else exponent_override
+                    )
+                    original_shapes_for_state.append(statistic.shape)
+                    max_size = max(max_size, statistic.shape[0])
+
+                statistics.extend(state.statistics)
+                prev_preconditioners.extend(state.preconditioners)
+                original_shapes.extend(original_shapes_for_state)
+
+        if batch_axis_name:
+            # Quantization is only enabled if batch_axis_name is not set.
+            quantized_dtype = quantized_dtype_for_second_moment_statistics_buffers()
+
+            if quantized_dtype == jnp.float32:
+                return _pmap_compute_preconditioners(
+                    states,
+                    step,
+                    statistics,
+                    num_statistics_per_state,
+                    original_shapes,
+                    exponents,
+                    max_size,
+                    prev_preconditioners,
+                )
+            else:
+                return _pmap_quantized_compute_preconditioners(
+                    states,
+                    step,
+                    statistics,
+                    num_statistics_per_state,
+                    original_shapes,
+                    exponents,
+                    max_size,
+                    prev_preconditioners,
+                )
+
+        else:
+            return _pjit_compute_preconditioners(
+                states,
+                step,
+                statistics,
+                num_statistics_per_state,
+                original_shapes,
+                exponents,
+                max_size,
+                prev_preconditioners,
+            )
+
+    def _transform_grad(grad, state, param, step):
+        """Transform per-parameter gradients."""
+        preconditioner = Preconditioner(
+            param, block_size, best_effort_shape_interpretation
+        )
+        sgd_update = grad
+        new_diagonal_statistics = state.diagonal_statistics.to_float()
+        if (
+            graft_type == GraftingType.ADAGRAD
+            or graft_type == GraftingType.ADAGRAD_NORMALIZED
+        ):
+
+            scaled_grad = grad
+            if graft_type == GraftingType.ADAGRAD_NORMALIZED:
+                scaled_grad = grad / (jnp.linalg.norm(grad) + 1e-16)
+
+            new_diagonal_statistics = state.diagonal_statistics.to_float() + jnp.square(
+                scaled_grad
+            )
+            adagrad_update = scaled_grad / (
+                jnp.sqrt(new_diagonal_statistics) + diagonal_epsilon
+            )
+            grafting_update = adagrad_update
+        elif (
+            graft_type == GraftingType.RMSPROP
+            or graft_type == GraftingType.RMSPROP_NORMALIZED
+        ):
+
+            scaled_grad = grad
+            if graft_type == GraftingType.RMSPROP_NORMALIZED:
+                scaled_grad = grad / (jnp.linalg.norm(grad) + 1e-16)
+
+            w1 = beta2
+            w2 = beta2 if beta2 == 1.0 else (1.0 - beta2)
+
+            new_diagonal_statistics = (
+                w1 * state.diagonal_statistics.to_float() + w2 * jnp.square(scaled_grad)
+            )
+            rmsprop_update = scaled_grad / (
+                jnp.sqrt(new_diagonal_statistics) + diagonal_epsilon
+            )
+
+            if clip_by_scaled_gradient_norm:
+                scaled_grad_norm = jnp.linalg.norm(rmsprop_update) / (
+                    jnp.sqrt(float(rmsprop_update.size))
+                )
+                clipping_denom = jnp.maximum(
+                    1.0, scaled_grad_norm / clip_by_scaled_gradient_norm
+                )
+                rmsprop_update /= clipping_denom
+
+            grafting_update = rmsprop_update
+        elif graft_type == GraftingType.SGD:
+            grafting_update = sgd_update
+        else:
+            grafting_update = jnp.ones_like(sgd_update) * jnp.sign(sgd_update)
+
+        precond_grad = grad
+        if not _skip_preconditioning(param):
+            precond_grad = preconditioner.preconditioned_grad(
+                precond_grad, _maybe_dequantize_preconditioners(state.preconditioners)
+            )
+        else:
+            precond_grad = grafting_update
+
+        grafting_update_norm = jnp.linalg.norm(grafting_update)
+        precond_grad_norm = jnp.linalg.norm(precond_grad)
+
+        multiplier = grafting_update_norm / (precond_grad_norm + 1e-16)
+        shampoo_update = precond_grad * multiplier
+
+        shampoo_update_with_wd = shampoo_update
+        grafting_update_with_wd = grafting_update
+        if weight_decay != 0:
+            shampoo_update_with_wd = shampoo_update + weight_decay * param
+            grafting_update_with_wd = grafting_update + weight_decay * param
+
+        w = (1.0 - beta1) if moving_average_for_momentum else 1.0
+
+        shampoo_update_with_wd_momentum = (
+            state.momentum.to_float() * beta1 + w * shampoo_update_with_wd
+        )
+
+        if _graft_type_has_diagonal_momentum_states():
+            grafting_update_with_wd_momentum = (
+                state.diagonal_momentum.to_float() * beta1 + w * grafting_update_with_wd
+            )
+        else:
+            # Share the momentum buffer
+            grafting_update_with_wd_momentum = (
+                state.momentum.to_float() * beta1 + w * grafting_update_with_wd
+            )
+
+        run_shampoo = (step >= start_preconditioning_step).astype(
+            grafting_update_with_wd_momentum.dtype
+        )
+
+        momentum_update = (
+            run_shampoo * shampoo_update_with_wd_momentum
+            + (1.0 - run_shampoo) * grafting_update_with_wd_momentum
+        )
+
+        wd_update = (
+            run_shampoo * shampoo_update_with_wd
+            + (1.0 - run_shampoo) * grafting_update_with_wd
+        )
+
+        nesterov_momentum_update = momentum_update
+        if nesterov:
+            nesterov_momentum_update = w * wd_update + beta1 * momentum_update
+
+        lr = learning_rate
+        if callable(learning_rate):
+            lr = learning_rate(step)
+        transformed_update = -1.0 * lr * nesterov_momentum_update
+
+        new_diagonal_momentum = grafting_update_with_wd_momentum
+        new_momentum = shampoo_update_with_wd_momentum
+        if not _graft_type_has_diagonal_momentum_states():
+            new_diagonal_momentum = []
+            new_momentum = momentum_update
+
+        param_stats = ParameterStats(
+            _quantize_diagonal_statistics(new_diagonal_statistics),
+            state.statistics,
+            state.preconditioners,
+            _quantize_momentum(new_diagonal_momentum),
+            _quantize_momentum(new_momentum),
+            state.training_metrics,
+        )
+
+        return transformed_update, param_stats
+
+    def update_fn(grads, state, params):
+        """Transform the input gradient and update all statistics.
+
+        Args:
+          grads: the gradient tensors for the parameters.
+          state: a named tuple containing the state of the optimizer
+          params: the parameters that should be updated.
+
+        Returns:
+          A tuple containing the new parameters and the new optimizer state.
+        """
+        params_flat, treedef = jax.tree_flatten(params)
+        stats_flat = treedef.flatten_up_to(state.stats)
+        grads_flat = treedef.flatten_up_to(grads)
+
+        new_stats_flat = jax.tree_multimap(
+            lambda g, s, p: _compute_stats(g, s, p, state.count),
+            grads_flat,
+            stats_flat,
+            params_flat,
+        )
+        new_stats_flat = _compute_preconditioners(
+            new_stats_flat, params_flat, state.count
+        )
+        outputs = jax.tree_multimap(
+            lambda g, s, p: _transform_grad(g, s, p, state.count),
+            grads_flat,
+            new_stats_flat,
+            params_flat,
+        )
+        updates_flat, new_stats_flat = list(zip(*outputs)) if outputs else ((), ())
+
+        updates = jax.tree_unflatten(treedef, updates_flat)
+        new_stats = jax.tree_unflatten(treedef, new_stats_flat)
+
+        new_state = ShampooState(count=state.count + 1, stats=new_stats)
+        return updates, new_state
+
+    if shard_optimizer_states:
+        # Hijacks the init_fn signature so we can return an OptState with
+        # appropriate init_fns.
+        def _init_fns(unused_params):
+            return InitFnState(
+                init_fn=sharded_init_fn,
+                pspec_fn=sharded_init_partition_spec_fn,
+                shape_and_dtype_fn=sharded_init_shape_and_dtype_fn,
+            )
+
+        return optax.GradientTransformation(_init_fns, sharded_update_fn)
+    else:
+        return optax.GradientTransformation(init_fn, update_fn)

encode_dataset.ipynb

@@ -0,0 +1,371 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "d0b72877",
+   "metadata": {},
+   "source": [
+    "# Pre-encoding a dataset for DALLE·mini"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ba7b31e6",
+   "metadata": {},
+   "source": [
+    "This notebook shows how to pre-encode images to token sequences using JAX, VQGAN and a dataset in the [`webdataset` format](https://webdataset.github.io/webdataset/).\n",
+    "\n",
+    "Adapt it to your own dataset and image encoder.\n",
+    "\n",
+    "At the end you should have a dataset of pairs:\n",
+    "* a caption defined as a string\n",
+    "* an encoded image defined as a list of int."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3b59489e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from tqdm.notebook import tqdm\n",
+    "\n",
+    "import torchvision.transforms as T\n",
+    "\n",
+    "import webdataset as wds\n",
+    "\n",
+    "import jax\n",
+    "import braceexpand\n",
+    "from pathlib import Path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c7c4c1e6",
+   "metadata": {},
+   "source": [
+    "## Configuration Parameters"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "1265dbfe",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "shards = \"my_images/shard-{0000..0008}.tar\"  # defined using braceexpand format as used by webdataset\n",
+    "encoded_output = Path(\"encoded_data\")  # where we will save our encoded data\n",
+    "\n",
+    "VQGAN_REPO, VQGAN_COMMIT_ID = (\n",
+    "    \"dalle-mini/vqgan_imagenet_f16_16384\",\n",
+    "    \"85eb5d3b51a1c62a0cc8f4ccdee9882c0d0bd384\",\n",
+    ")\n",
+    "\n",
+    "# good defaults for a TPU v3-8\n",
+    "batch_size = 128  # Per device\n",
+    "num_workers = 8  # For parallel processing\n",
+    "total_bs = batch_size * jax.device_count()  # You can use a smaller size while testing\n",
+    "save_frequency = 128  # Number of batches to create a new file (180MB for f16 and 720MB for f8 per file)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "cd956ec6-7d98-4d4d-a454-f80fe857eadd",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['XXX/shard-0000.tar',\n",
+       " 'XXX/shard-0001.tar',\n",
+       " 'XXX/shard-0002.tar',\n",
+       " 'XXX/shard-0003.tar',\n",
+       " 'XXX/shard-0004.tar',\n",
+       " 'XXX/shard-0005.tar',\n",
+       " 'XXX/shard-0006.tar',\n",
+       " 'XXX/shard-0007.tar',\n",
+       " 'XXX/shard-0008.tar']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "shards = list(\n",
+    "    braceexpand.braceexpand(shards)\n",
+    ")  # better display for tqdm with known length"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "75dba8e2",
+   "metadata": {},
+   "source": [
+    "## Load data"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a1e8fb95",
+   "metadata": {},
+   "source": [
+    "We load data using `webdataset`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ef5de9e",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "ds = (\n",
+    "    wds.WebDataset(shards, handler=wds.warn_and_continue)\n",
+    "    .decode(\"rgb\", handler=wds.warn_and_continue)\n",
+    "    .to_tuple(\"jpg\", \"txt\")  # assumes image is in `jpg` and caption in `txt`\n",
+    "    .batched(total_bs)  # load in batch per worker (faster)\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "90981824",
+   "metadata": {},
+   "source": [
+    "Note:\n",
+    "* you can also shuffle shards and items using `shardshuffle` and `shuffle` if necessary.\n",
+    "* you may need to resize images in your pipeline (with `map_dict` for example), we assume they are already set to 256x256.\n",
+    "* you can also filter out some items using `select`."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "129c377d",
+   "metadata": {},
+   "source": [
+    "We can now inspect our data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "8cac98cb",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "%%time\n",
+    "images, captions = next(iter(ds))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "cd268fbf",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "images.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "5acfc4d8",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "captions[:10]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c24693c0",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "T.ToPILImage()(images[0].permute(2, 0, 1))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3059ffb1",
+   "metadata": {},
+   "source": [
+    "Finally we create our dataloader."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c227c551",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dl = (\n",
+    "    wds.WebLoader(ds, batch_size=None, num_workers=8).unbatched().batched(total_bs)\n",
+    ")  # avoid partial batch at the end of each worker"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a354472b",
+   "metadata": {},
+   "source": [
+    "## Image encoder\n",
+    "\n",
+    "We'll use a VQGAN trained with Taming Transformers and converted to a JAX model."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47a8b818",
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "from vqgan_jax.modeling_flax_vqgan import VQModel\n",
+    "from flax.jax_utils import replicate\n",
+    "\n",
+    "vqgan = VQModel.from_pretrained(\"flax-community/vqgan_f16_16384\")\n",
+    "vqgan_params = replicate(vqgan.params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "62ad01c3",
+   "metadata": {},
+   "source": [
+    "## Encoding"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "20357f74",
+   "metadata": {},
+   "source": [
+    "Encoding is really simple using `shard` to automatically distribute batches across devices and `pmap`."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "322a4619",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from flax.training.common_utils import shard\n",
+    "from functools import partial\n",
+    "\n",
+    "\n",
+    "@partial(jax.pmap, axis_name=\"batch\")\n",
+    "def p_encode(batch, params):\n",
+    "    # Not sure if we should `replicate` params, does not seem to have any effect\n",
+    "    _, indices = vqgan.encode(batch, params=params)\n",
+    "    return indices"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ff6c10d4",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "\n",
+    "def encode_dataset(dataloader, output_dir, save_frequency):\n",
+    "    output_dir.mkdir(parents=True, exist_ok=True)\n",
+    "    all_captions = []\n",
+    "    all_encoding = []\n",
+    "    n_file = 1\n",
+    "    for idx, (images, captions) in enumerate(tqdm(dataloader)):\n",
+    "        images = images.numpy()\n",
+    "        n = len(images) // 8 * 8\n",
+    "        if n != len(images):\n",
+    "            # get the max number of images we can (multiple of 8)\n",
+    "            print(f\"Different sizes {n} vs {len(images)}\")\n",
+    "            images = images[:n]\n",
+    "            captions = captions[:n]\n",
+    "        if not len(captions):\n",
+    "            print(f\"No images/captions in batch...\")\n",
+    "            continue\n",
+    "        images = shard(images)\n",
+    "        encoded = p_encode(images, vqgan_params)\n",
+    "        encoded = encoded.reshape(-1, encoded.shape[-1])\n",
+    "        all_captions.extend(captions)\n",
+    "        all_encoding.extend(encoded.tolist())\n",
+    "\n",
+    "        # save files\n",
+    "        if (idx + 1) % save_frequency == 0:\n",
+    "            print(f\"Saving file {n_file}\")\n",
+    "            batch_df = pd.DataFrame.from_dict(\n",
+    "                {\"caption\": all_captions, \"encoding\": all_encoding}\n",
+    "            )\n",
+    "            batch_df.to_parquet(f\"{output_dir}/{n_file:03d}.parquet\")\n",
+    "            all_captions = []\n",
+    "            all_encoding = []\n",
+    "            n_file += 1\n",
+    "\n",
+    "    if len(all_captions):\n",
+    "        print(f\"Saving final file {n_file}\")\n",
+    "        batch_df = pd.DataFrame.from_dict(\n",
+    "            {\"caption\": all_captions, \"encoding\": all_encoding}\n",
+    "        )\n",
+    "        batch_df.to_parquet(f\"{output_dir}/{n_file:03d}.parquet\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7704863d",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "encode_dataset(dl, output_dir=encoded_output, save_frequency=save_frequency)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "8953dd84",
+   "metadata": {},
+   "source": [
+    "----"
+   ]
+  }
+ ],
+ "metadata": {
+  "interpreter": {
+   "hash": "db471c52d602b4f5f40ecaf278e88ccfef85c29d0a1a07185b0d51fc7acf4e26"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}

inference_pipeline.ipynb

@@ -0,0 +1,557 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "colab_type": "text",
+    "id": "view-in-github"
+   },
+   "source": [
+    "<a href=\"https://colab.research.google.com/github/borisdayma/dalle-mini/blob/main/tools/inference/inference_pipeline.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "118UKH5bWCGa"
+   },
+   "source": [
+    "# DALL·E mini - Inference pipeline\n",
+    "\n",
+    "*Generate images from a text prompt*\n",
+    "\n",
+    "<img src=\"https://github.com/borisdayma/dalle-mini/blob/main/img/logo.png?raw=true\" width=\"200\">\n",
+    "\n",
+    "This notebook illustrates [DALL·E mini](https://github.com/borisdayma/dalle-mini) inference pipeline.\n",
+    "\n",
+    "Just want to play? Use directly [the app](https://www.craiyon.com/).\n",
+    "\n",
+    "For more understanding of the model, refer to [the report](https://wandb.ai/dalle-mini/dalle-mini/reports/DALL-E-mini--Vmlldzo4NjIxODA)."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "dS8LbaonYm3a"
+   },
+   "source": [
+    "## 🛠️ Installation and set-up"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "uzjAM2GBYpZX"
+   },
+   "outputs": [],
+   "source": [
+    "# Install required libraries\n",
+    "!pip install -q dalle-mini\n",
+    "!pip install -q git+https://github.com/patil-suraj/vqgan-jax.git"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "ozHzTkyv8cqU"
+   },
+   "source": [
+    "We load required models:\n",
+    "* DALL·E mini for text to encoded images\n",
+    "* VQGAN for decoding images\n",
+    "* CLIP for scoring predictions"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "K6CxW2o42f-w"
+   },
+   "outputs": [],
+   "source": [
+    "# Model references\n",
+    "\n",
+    "# dalle-mega\n",
+    "DALLE_MODEL = \"dalle-mini/dalle-mini/mega-1-fp16:latest\"  # can be wandb artifact or 🤗 Hub or local folder or google bucket\n",
+    "DALLE_COMMIT_ID = None\n",
+    "\n",
+    "# if the notebook crashes too often you can use dalle-mini instead by uncommenting below line\n",
+    "# DALLE_MODEL = \"dalle-mini/dalle-mini/mini-1:v0\"\n",
+    "\n",
+    "# VQGAN model\n",
+    "VQGAN_REPO = \"dalle-mini/vqgan_imagenet_f16_16384\"\n",
+    "VQGAN_COMMIT_ID = \"e93a26e7707683d349bf5d5c41c5b0ef69b677a9\""
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "Yv-aR3t4Oe5v"
+   },
+   "outputs": [],
+   "source": [
+    "import jax\n",
+    "import jax.numpy as jnp\n",
+    "\n",
+    "# check how many devices are available\n",
+    "jax.local_device_count()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "92zYmvsQ38vL"
+   },
+   "outputs": [],
+   "source": [
+    "# Load models & tokenizer\n",
+    "from dalle_mini import DalleBart, DalleBartProcessor\n",
+    "from vqgan_jax.modeling_flax_vqgan import VQModel\n",
+    "from transformers import CLIPProcessor, FlaxCLIPModel\n",
+    "\n",
+    "# Load dalle-mini\n",
+    "model, params = DalleBart.from_pretrained(\n",
+    "    DALLE_MODEL, revision=DALLE_COMMIT_ID, dtype=jnp.float16, _do_init=False\n",
+    ")\n",
+    "\n",
+    "# Load VQGAN\n",
+    "vqgan, vqgan_params = VQModel.from_pretrained(\n",
+    "    VQGAN_REPO, revision=VQGAN_COMMIT_ID, _do_init=False\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "o_vH2X1tDtzA"
+   },
+   "source": [
+    "Model parameters are replicated on each device for faster inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "wtvLoM48EeVw"
+   },
+   "outputs": [],
+   "source": [
+    "from flax.jax_utils import replicate\n",
+    "\n",
+    "params = replicate(params)\n",
+    "vqgan_params = replicate(vqgan_params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "0A9AHQIgZ_qw"
+   },
+   "source": [
+    "Model functions are compiled and parallelized to take advantage of multiple devices."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "sOtoOmYsSYPz"
+   },
+   "outputs": [],
+   "source": [
+    "from functools import partial\n",
+    "\n",
+    "# model inference\n",
+    "@partial(jax.pmap, axis_name=\"batch\", static_broadcasted_argnums=(3, 4, 5, 6))\n",
+    "def p_generate(\n",
+    "    tokenized_prompt, key, params, top_k, top_p, temperature, condition_scale\n",
+    "):\n",
+    "    return model.generate(\n",
+    "        **tokenized_prompt,\n",
+    "        prng_key=key,\n",
+    "        params=params,\n",
+    "        top_k=top_k,\n",
+    "        top_p=top_p,\n",
+    "        temperature=temperature,\n",
+    "        condition_scale=condition_scale,\n",
+    "    )\n",
+    "\n",
+    "\n",
+    "# decode image\n",
+    "@partial(jax.pmap, axis_name=\"batch\")\n",
+    "def p_decode(indices, params):\n",
+    "    return vqgan.decode_code(indices, params=params)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "HmVN6IBwapBA"
+   },
+   "source": [
+    "Keys are passed to the model on each device to generate unique inference per device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "4CTXmlUkThhX"
+   },
+   "outputs": [],
+   "source": [
+    "import random\n",
+    "\n",
+    "# create a random key\n",
+    "seed = random.randint(0, 2**32 - 1)\n",
+    "key = jax.random.PRNGKey(seed)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BrnVyCo81pij"
+   },
+   "source": [
+    "## 🖍 Text Prompt"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "rsmj0Aj5OQox"
+   },
+   "source": [
+    "Our model requires processing prompts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "YjjhUychOVxm"
+   },
+   "outputs": [],
+   "source": [
+    "from dalle_mini import DalleBartProcessor\n",
+    "\n",
+    "processor = DalleBartProcessor.from_pretrained(DALLE_MODEL, revision=DALLE_COMMIT_ID)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "BQ7fymSPyvF_"
+   },
+   "source": [
+    "Let's define some text prompts."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "x_0vI9ge1oKr"
+   },
+   "outputs": [],
+   "source": [
+    "prompts = [\n",
+    "    \"sunset over a lake in the mountains\",\n",
+    "    \"the Eiffel tower landing on the moon\",\n",
+    "]"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "XlZUG3SCLnGE"
+   },
+   "source": [
+    "Note: we could use the same prompt multiple times for faster inference."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "VKjEZGjtO49k"
+   },
+   "outputs": [],
+   "source": [
+    "tokenized_prompts = processor(prompts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "-CEJBnuJOe5z"
+   },
+   "source": [
+    "Finally we replicate the prompts onto each device."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "lQePgju5Oe5z"
+   },
+   "outputs": [],
+   "source": [
+    "tokenized_prompt = replicate(tokenized_prompts)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "phQ9bhjRkgAZ"
+   },
+   "source": [
+    "## 🎨 Generate images\n",
+    "\n",
+    "We generate images using dalle-mini model and decode them with the VQGAN."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "d0wVkXpKqnHA"
+   },
+   "outputs": [],
+   "source": [
+    "# number of predictions per prompt\n",
+    "n_predictions = 8\n",
+    "\n",
+    "# We can customize generation parameters (see https://huggingface.co/blog/how-to-generate)\n",
+    "gen_top_k = None\n",
+    "gen_top_p = None\n",
+    "temperature = None\n",
+    "cond_scale = 10.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "SDjEx9JxR3v8"
+   },
+   "outputs": [],
+   "source": [
+    "from flax.training.common_utils import shard_prng_key\n",
+    "import numpy as np\n",
+    "from PIL import Image\n",
+    "from tqdm.notebook import trange\n",
+    "\n",
+    "print(f\"Prompts: {prompts}\\n\")\n",
+    "# generate images\n",
+    "images = []\n",
+    "for i in trange(max(n_predictions // jax.device_count(), 1)):\n",
+    "    # get a new key\n",
+    "    key, subkey = jax.random.split(key)\n",
+    "    # generate images\n",
+    "    encoded_images = p_generate(\n",
+    "        tokenized_prompt,\n",
+    "        shard_prng_key(subkey),\n",
+    "        params,\n",
+    "        gen_top_k,\n",
+    "        gen_top_p,\n",
+    "        temperature,\n",
+    "        cond_scale,\n",
+    "    )\n",
+    "    # remove BOS\n",
+    "    encoded_images = encoded_images.sequences[..., 1:]\n",
+    "    # decode images\n",
+    "    decoded_images = p_decode(encoded_images, vqgan_params)\n",
+    "    decoded_images = decoded_images.clip(0.0, 1.0).reshape((-1, 256, 256, 3))\n",
+    "    for decoded_img in decoded_images:\n",
+    "        img = Image.fromarray(np.asarray(decoded_img * 255, dtype=np.uint8))\n",
+    "        images.append(img)\n",
+    "        display(img)\n",
+    "        print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "tw02wG9zGmyB"
+   },
+   "source": [
+    "## 🏅 Optional: Rank images by CLIP score\n",
+    "\n",
+    "We can rank images according to CLIP.\n",
+    "\n",
+    "**Note: your session may crash if you don't have a subscription to Colab Pro.**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "RGjlIW_f6GA0"
+   },
+   "outputs": [],
+   "source": [
+    "# CLIP model\n",
+    "CLIP_REPO = \"openai/clip-vit-base-patch32\"\n",
+    "CLIP_COMMIT_ID = None\n",
+    "\n",
+    "# Load CLIP\n",
+    "clip, clip_params = FlaxCLIPModel.from_pretrained(\n",
+    "    CLIP_REPO, revision=CLIP_COMMIT_ID, dtype=jnp.float16, _do_init=False\n",
+    ")\n",
+    "clip_processor = CLIPProcessor.from_pretrained(CLIP_REPO, revision=CLIP_COMMIT_ID)\n",
+    "clip_params = replicate(clip_params)\n",
+    "\n",
+    "# score images\n",
+    "@partial(jax.pmap, axis_name=\"batch\")\n",
+    "def p_clip(inputs, params):\n",
+    "    logits = clip(params=params, **inputs).logits_per_image\n",
+    "    return logits"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "FoLXpjCmGpju"
+   },
+   "outputs": [],
+   "source": [
+    "from flax.training.common_utils import shard\n",
+    "\n",
+    "# get clip scores\n",
+    "clip_inputs = clip_processor(\n",
+    "    text=prompts * jax.device_count(),\n",
+    "    images=images,\n",
+    "    return_tensors=\"np\",\n",
+    "    padding=\"max_length\",\n",
+    "    max_length=77,\n",
+    "    truncation=True,\n",
+    ").data\n",
+    "logits = p_clip(shard(clip_inputs), clip_params)\n",
+    "\n",
+    "# organize scores per prompt\n",
+    "p = len(prompts)\n",
+    "logits = np.asarray([logits[:, i::p, i] for i in range(p)]).squeeze()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "4AAWRm70LgED"
+   },
+   "source": [
+    "Let's now display images ranked by CLIP score."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "zsgxxubLLkIu"
+   },
+   "outputs": [],
+   "source": [
+    "for i, prompt in enumerate(prompts):\n",
+    "    print(f\"Prompt: {prompt}\\n\")\n",
+    "    for idx in logits[i].argsort()[::-1]:\n",
+    "        display(images[idx * p + i])\n",
+    "        print(f\"Score: {jnp.asarray(logits[i][idx], dtype=jnp.float32):.2f}\\n\")\n",
+    "    print()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "oZT9i3jCjir0"
+   },
+   "source": [
+    "## 🪄 Optional: Save your Generated Images as W&B Tables\n",
+    "\n",
+    "W&B Tables is an interactive 2D grid with support to rich media logging. Use this to save the generated images on W&B dashboard and share with the world."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "id": "-pSiv6Vwjkn0"
+   },
+   "outputs": [],
+   "source": [
+    "import wandb\n",
+    "\n",
+    "# Initialize a W&B run.\n",
+    "project = 'dalle-mini-tables-colab'\n",
+    "run = wandb.init(project=project)\n",
+    "\n",
+    "# Initialize an empty W&B Tables.\n",
+    "columns = [\"captions\"] + [f\"image_{i+1}\" for i in range(n_predictions)]\n",
+    "gen_table = wandb.Table(columns=columns)\n",
+    "\n",
+    "# Add data to the table.\n",
+    "for i, prompt in enumerate(prompts):\n",
+    "    # If CLIP scores exist, sort the Images\n",
+    "    if logits is not None:\n",
+    "        idxs = logits[i].argsort()[::-1]\n",
+    "        tmp_imgs = images[i::2]\n",
+    "        tmp_imgs = [tmp_imgs[idx] for idx in idxs]\n",
+    "    else:\n",
+    "        tmp_imgs = images[i::2]\n",
+    "\n",
+    "    # Add the data to the table.\n",
+    "    gen_table.add_data(prompt, *[wandb.Image(img) for img in tmp_imgs])\n",
+    "\n",
+    "# Log the Table to W&B dashboard.\n",
+    "wandb.log({\"Generated Images\": gen_table})\n",
+    "\n",
+    "# Close the W&B run.\n",
+    "run.finish()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "id": "Ck2ZnHwVjnRd"
+   },
+   "source": [
+    "Click on the link above to check out your generated images."
+   ]
+  }
+ ],
+ "metadata": {
+  "accelerator": "GPU",
+  "colab": {
+   "collapsed_sections": [],
+   "include_colab_link": true,
+   "machine_shape": "hm",
+   "name": "DALL·E mini - Inference pipeline.ipynb",
+   "provenance": []
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}

modeling.py

@@ -0,0 +1,1909 @@
+# coding=utf-8
+# Copyright 2021-2022 The Fairseq Authors and The Google Flax Team Authors And The HuggingFace Inc. team and & DALL·E Mini team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+""" DalleBart model. """
+
+import math
+from functools import partial
+from typing import Any, Dict, Optional, Tuple
+
+import flax
+import flax.linen as nn
+import jax
+import jax.numpy as jnp
+from einops import rearrange
+from flax.core.frozen_dict import unfreeze
+from flax.linen import combine_masks, make_causal_mask
+from flax.linen import partitioning as nn_partitioning
+from flax.linen.linear import PrecisionLike
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax import custom_jvp, lax
+from jax.random import PRNGKey
+from transformers.generation_flax_utils import FlaxSampleOutput
+from transformers.modeling_flax_outputs import (
+    FlaxBaseModelOutput,
+    FlaxBaseModelOutputWithPastAndCrossAttentions,
+    FlaxCausalLMOutputWithCrossAttentions,
+    FlaxSeq2SeqLMOutput,
+)
+from transformers.modeling_flax_utils import ACT2FN
+from transformers.models.bart.modeling_flax_bart import (
+    FlaxBartAttention,
+    FlaxBartForConditionalGeneration,
+    FlaxBartForConditionalGenerationModule,
+    FlaxBartModule,
+)
+from transformers.utils import logging
+
+from .configuration import DalleBartConfig
+from .utils import PretrainedFromWandbMixin
+
+logger = logging.get_logger(__name__)
+
+remat = nn_partitioning.remat
+
+
+def smelu(beta: Any = 1.0):
+    """
+    Implementation of "Real World Large Scale Recommendation Systems Reproducibility and Smooth Activations"
+    https://arxiv.org/abs/2202.06499
+    """
+
+    @custom_jvp
+    @jax.jit
+    def _smelu(x: Any) -> Any:
+        x = jnp.where(x <= -beta, 0.0, x)
+        return jnp.where(x >= beta, x, jnp.square(x + beta) / (4 * beta))
+
+    _smelu.defjvps(
+        lambda g, ans, x: lax.select(
+            x == -beta,
+            lax.full_like(g, 0),
+            lax.select(x == beta, lax.full_like(g, 1), g),
+        )
+    )
+    return _smelu
+
+
+ACT2FN.update({"smelu": smelu()})
+
+# deepnet initialization
+def deepnet_init(gain=1):
+    init = jax.nn.initializers.glorot_normal()
+
+    def _init(*args, **kwargs):
+        return gain * init(*args, **kwargs)
+
+    return _init
+
+
+# deepnet gain
+deepnet_gain = {
+    "encoder": {
+        "alpha": lambda config: 0.81
+        * (config.encoder_layers**4 * config.decoder_layers) ** 0.0625,
+        "beta": lambda config: 0.87
+        * (config.encoder_layers**4 * config.decoder_layers) ** -0.0625,
+    },
+    "decoder": {
+        "alpha": lambda config: (3 * config.decoder_layers) ** 0.25,
+        "beta": lambda config: (12 * config.decoder_layers) ** -0.25,
+    },
+}
+
+
+class RMSNorm(nn.Module):
+    """
+    From "Root Mean Square Layer Normalization" by https://arxiv.org/abs/1910.07467
+
+    Adapted from flax.linen.LayerNorm
+    """
+
+    epsilon: float = 1e-6
+    dtype: Any = jnp.float32
+    param_dtype: Any = jnp.float32
+    use_scale: bool = True
+    scale_init: Any = jax.nn.initializers.ones
+
+    @nn.compact
+    def __call__(self, x):
+        reduction_axes = (-1,)
+        feature_axes = (-1,)
+
+        rms_sq = self._compute_rms_sq(x, reduction_axes)
+
+        return self._normalize(
+            self,
+            x,
+            rms_sq,
+            reduction_axes,
+            feature_axes,
+            self.dtype,
+            self.param_dtype,
+            self.epsilon,
+            self.use_scale,
+            self.scale_init,
+        )
+
+    def _compute_rms_sq(self, x, axes):
+        x = jnp.asarray(x, jnp.promote_types(jnp.float32, jnp.result_type(x)))
+        rms_sq = jnp.mean(jax.lax.square(x), axes)
+        return rms_sq
+
+    def _normalize(
+        self,
+        mdl,
+        x,
+        rms_sq,
+        reduction_axes,
+        feature_axes,
+        dtype,
+        param_dtype,
+        epsilon,
+        use_scale,
+        scale_init,
+    ):
+        reduction_axes = nn.normalization._canonicalize_axes(x.ndim, reduction_axes)
+        feature_axes = nn.normalization._canonicalize_axes(x.ndim, feature_axes)
+        stats_shape = list(x.shape)
+        for axis in reduction_axes:
+            stats_shape[axis] = 1
+        rms_sq = rms_sq.reshape(stats_shape)
+        feature_shape = [1] * x.ndim
+        reduced_feature_shape = []
+        for ax in feature_axes:
+            feature_shape[ax] = x.shape[ax]
+            reduced_feature_shape.append(x.shape[ax])
+        mul = lax.rsqrt(rms_sq + epsilon)
+        if use_scale:
+            scale = mdl.param(
+                "scale", scale_init, reduced_feature_shape, param_dtype
+            ).reshape(feature_shape)
+            mul *= scale
+        y = mul * x
+        return jnp.asarray(y, dtype)
+
+
+def norm(type, *args, **kwargs):
+    if type == "rmsnorm":
+        return RMSNorm(*args, **kwargs)
+    elif type == "layernorm":
+        return nn.LayerNorm(*args, **kwargs)
+    else:
+        raise ValueError(f"Unknown norm type {type}")
+
+
+def dot_product_attention_weights(
+    query: Any,
+    key: Any,
+    bias: Optional[Any] = None,
+    mask: Optional[Any] = None,
+    embed_pos: Optional[Any] = None,
+    broadcast_dropout: bool = True,
+    dropout_rng: Optional[PRNGKey] = None,
+    dropout_rate: float = 0.0,
+    deterministic: bool = False,
+    dtype: Any = jnp.float32,
+    precision: PrecisionLike = None,
+    sinkhorn_iters: int = 1,
+    is_encoder: bool = False,
+):
+    """
+    Computes dot-product attention weights given query and key.
+    mask is included into the bias.
+
+    Adapted from flax.linen.attention.dot_product_attention_weights"
+    """
+    assert query.ndim == key.ndim, "q, k must have same rank."
+    assert query.shape[:-3] == key.shape[:-3], "q, k batch dims must match."
+    assert query.shape[-2] == key.shape[-2], "q, k num_heads must match."
+    assert query.shape[-1] == key.shape[-1], "q, k depths must match."
+
+    # calculate attention matrix
+    depth = query.shape[-1]
+    query = query / jnp.sqrt(depth).astype(dtype)
+    # attn weight shape is (batch..., num_heads, q_length, kv_length)
+    attn_weights = jnp.einsum("...qhd,...khd->...hqk", query, key, precision=precision)
+
+    # apply attention bias: masking, dropout, proximity bias, etc.
+    if bias is not None:
+        attn_weights = attn_weights + bias
+
+    # add relative position
+    if embed_pos is not None:
+        attn_weights = attn_weights + embed_pos
+
+    # normalize the attention weights
+    if not is_encoder or sinkhorn_iters == 1:
+        # sinkhorn does not work for causal (leaks info of future tokens into past)
+        attn_weights = jax.nn.softmax(attn_weights).astype(dtype)
+    else:
+        # adapted from https://github.com/lucidrains/sinkhorn-transformer
+        for i in range(sinkhorn_iters):
+            # when causal, some attn_weights have been set to -inf through bias
+            if i % 2 == 0:
+                attn_weights -= jax.nn.logsumexp(attn_weights, axis=-1, keepdims=True)
+            else:
+                attn_weights -= jax.nn.logsumexp(attn_weights, axis=-2, keepdims=True)
+            if mask is not None:
+                attn_weights = jnp.where(mask, attn_weights, -jnp.inf)
+        attn_weights = jnp.exp(attn_weights).astype(dtype)
+
+    # apply attention dropout
+    if not deterministic and dropout_rate > 0.0:
+        keep_prob = 1.0 - dropout_rate
+        if broadcast_dropout:
+            # dropout is broadcast across the batch + head dimensions
+            dropout_shape = tuple([1] * (key.ndim - 2)) + attn_weights.shape[-2:]
+            keep = jax.random.bernoulli(dropout_rng, keep_prob, dropout_shape)
+        else:
+            keep = jax.random.bernoulli(dropout_rng, keep_prob, attn_weights.shape)
+        multiplier = keep.astype(attn_weights.dtype) / jnp.asarray(
+            keep_prob, dtype=dtype
+        )
+        attn_weights = attn_weights * multiplier
+
+    return attn_weights
+
+
+class FlaxBartAttention(FlaxBartAttention):
+    """
+    Edits:
+    - causal mask is used only in decoder and considers image_length
+    - scale attention heads per NormFormer paper
+    """
+
+    is_encoder: bool = False
+    q_length: int = None
+    k_length: int = None
+
+    def setup(self) -> None:
+        self.head_dim = self.embed_dim // self.num_heads
+        if self.head_dim * self.num_heads != self.embed_dim:
+            raise ValueError(
+                f"embed_dim must be divisible by num_heads (got `embed_dim`: {self.embed_dim}"
+                f" and `num_heads`: {self.num_heads})."
+            )
+
+        dense = partial(
+            nn.Dense,
+            self.embed_dim,
+            use_bias=self.bias,
+            dtype=self.dtype,
+        )
+
+        gain = deepnet_gain["encoder" if self.is_encoder else "decoder"]["beta"](
+            self.config
+        )
+
+        self.q_proj = dense(
+            kernel_init=deepnet_init()
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std)
+        )
+        self.k_proj = dense(
+            kernel_init=deepnet_init()
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std)
+        )
+        self.v_proj = dense(
+            kernel_init=deepnet_init(gain)
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std)
+        )
+        self.out_proj = dense(
+            kernel_init=deepnet_init(gain)
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std)
+        )
+        self.dropout_layer = nn.Dropout(rate=self.dropout)
+
+        if self.config.use_head_scale:
+            self.head_scale = self.param(
+                "head_scale", jax.nn.initializers.ones, (1, 1, self.num_heads, 1)
+            )
+
+        if self.config.use_cosine_attention:
+            self.tau = self.param(
+                "tau",
+                jax.nn.initializers.constant(self.config.tau_init),
+                (1, self.num_heads, 1, 1),
+            )
+
+        if self.config.use_swin_position_embeddings:
+            self.rel_bias = nn.Embed(
+                self.q_length,
+                self.k_length * self.num_heads,
+                embedding_init=deepnet_init()
+                if self.config.use_deepnet_scaling
+                else jax.nn.initializers.normal(self.config.init_std),
+            )
+
+        if self.causal:
+            # used only in decoder
+            self.causal_mask = make_causal_mask(
+                jnp.ones((1, self.config.image_length), dtype="bool"), dtype="bool"
+            )
+
+    def __call__(
+        self,
+        hidden_states: jnp.ndarray,
+        key_value_states: Optional[jnp.ndarray] = None,
+        attention_mask: Optional[jnp.ndarray] = None,
+        init_cache: bool = False,
+        deterministic: bool = True,
+    ) -> Tuple[jnp.ndarray]:
+        """Input shape: Batch x Time x Channel"""
+
+        # if key_value_states are provided this layer is used as a cross-attention layer
+        # for the decoder
+        is_cross_attention = key_value_states is not None
+        batch_size = hidden_states.shape[0]
+
+        # get query proj
+        query_states = self.q_proj(hidden_states)
+        # get key, value proj
+        if is_cross_attention:
+            # cross_attentions
+            key_states = self.k_proj(key_value_states)
+            value_states = self.v_proj(key_value_states)
+        else:
+            # self_attention
+            key_states = self.k_proj(hidden_states)
+            value_states = self.v_proj(hidden_states)
+
+        query_states = self._split_heads(query_states)
+        key_states = self._split_heads(key_states)
+        value_states = self._split_heads(value_states)
+
+        # handle cache prepare causal attention mask
+        if self.causal:
+            query_length, key_length = query_states.shape[1], key_states.shape[1]
+            if self.has_variable("cache", "cached_key"):
+                mask_shift = self.variables["cache"]["cache_index"]
+                max_decoder_length = self.variables["cache"]["cached_key"].shape[1]
+                causal_mask = lax.dynamic_slice(
+                    self.causal_mask,
+                    (0, 0, mask_shift, 0),
+                    (1, 1, query_length, max_decoder_length),
+                )
+            else:
+                causal_mask = self.causal_mask[:, :, :query_length, :key_length]
+            causal_mask = jnp.broadcast_to(
+                causal_mask, (batch_size,) + causal_mask.shape[1:]
+            )
+
+        # combine masks if needed
+        if attention_mask is not None and self.causal:
+            attention_mask = jnp.broadcast_to(
+                jnp.expand_dims(attention_mask, axis=(-3, -2)), causal_mask.shape
+            )
+            attention_mask = combine_masks(attention_mask, causal_mask)
+        elif self.causal:
+            attention_mask = causal_mask
+        elif attention_mask is not None:
+            attention_mask = jnp.expand_dims(attention_mask, axis=(-3, -2))
+
+        # During fast autoregressive decoding, we feed one position at a time,
+        # and cache the keys and values step by step.
+        if self.causal and (self.has_variable("cache", "cached_key") or init_cache):
+            key_states, value_states, attention_mask = self._concatenate_to_cache(
+                key_states, value_states, query_states, attention_mask
+            )
+
+        # Convert the boolean attention mask to an attention bias.
+        if attention_mask is not None:
+            # attention mask in the form of attention bias
+            attention_bias = lax.select(
+                attention_mask > 0,
+                jnp.full(attention_mask.shape, 0.0).astype(self.dtype),
+                jnp.full(attention_mask.shape, -jnp.inf).astype(self.dtype),
+            )
+        else:
+            attention_bias = None
+
+        dropout_rng = None
+        if not deterministic and self.dropout > 0.0:
+            dropout_rng = self.make_rng("dropout")
+
+        if self.config.use_cosine_attention:
+            # normalize q and k
+            query_states = query_states / (
+                jnp.linalg.norm(query_states, axis=-1, keepdims=True) + 1e-8
+            )
+            key_states = key_states / (
+                jnp.linalg.norm(key_states, axis=-1, keepdims=True) + 1e-8
+            )
+
+        # relative position embeddings
+        if self.config.use_swin_position_embeddings:
+            position_ids = jnp.arange(self.q_length)
+            embed_pos = self.rel_bias(position_ids)
+            embed_pos = rearrange(embed_pos, "q (k h) -> 1 h q k", h=self.num_heads)
+        else:
+            embed_pos = None
+
+        attn_weights = dot_product_attention_weights(
+            query_states,
+            key_states,
+            bias=attention_bias,
+            mask=attention_mask,
+            embed_pos=embed_pos,
+            dropout_rng=dropout_rng,
+            dropout_rate=self.dropout,
+            broadcast_dropout=True,
+            deterministic=deterministic,
+            dtype=self.dtype,
+            precision=None,
+            sinkhorn_iters=self.config.sinkhorn_iters,
+            is_encoder=self.is_encoder,
+        )
+        if self.config.use_cosine_attention:
+            # divide by tau
+            attn_weights = attn_weights / jnp.maximum(self.tau, 0.01)
+
+        attn_output = jnp.einsum("...hqk,...khd->...qhd", attn_weights, value_states)
+        if self.config.use_head_scale:
+            # per Normformer
+            attn_output = attn_output * self.head_scale
+        attn_output = self._merge_heads(attn_output)
+        attn_output = self.out_proj(attn_output)
+
+        return attn_output, attn_weights
+
+
+class GLU(nn.Module):
+    """From "GLU Variants Improve Transformer" by https://arxiv.org/abs/2002.05202"""
+
+    config: DalleBartConfig
+    ffn_dim: int
+    embed_dim: int
+    dtype: jnp.dtype = jnp.float32
+    is_encoder: bool = False
+
+    @nn.compact
+    def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+
+        gain = deepnet_gain["encoder" if self.is_encoder else "decoder"]["beta"](
+            self.config
+        )
+
+        if self.config.ln_positions in ["normformer", "cogview", "preln"]:
+            x = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=self.config.force_ln_scale,
+            )(x)
+        w = nn.Dense(
+            self.ffn_dim,
+            dtype=self.dtype,
+            use_bias=self.config.use_bias,
+            kernel_init=deepnet_init(gain)
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std),
+        )(x)
+        w = ACT2FN[self.config.activation_function](w)
+        v = nn.Dense(
+            self.ffn_dim,
+            dtype=self.dtype,
+            use_bias=self.config.use_bias,
+            kernel_init=deepnet_init(gain)
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std),
+        )(x)
+        x = w * v
+        if self.config.ln_positions in ["normformer"]:
+            x = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=self.config.force_ln_scale,
+            )(x)
+        x = nn.Dropout(rate=self.config.activation_dropout)(
+            x, deterministic=deterministic
+        )
+
+        x = nn.Dense(
+            self.embed_dim,
+            dtype=self.dtype,
+            use_bias=self.config.use_bias,
+            kernel_init=deepnet_init(gain)
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std),
+        )(x)
+        if self.config.ln_positions in ["swinv2", "cogview"]:
+            x = norm(self.config.ln_type, dtype=self.dtype, epsilon=1e-05)(x)
+        x = nn.Dropout(rate=self.config.dropout)(x, deterministic=deterministic)
+        return x
+
+
+class FFN(nn.Module):
+    """Simple FFN layer"""
+
+    config: DalleBartConfig
+    ffn_dim: int
+    embed_dim: int
+    dtype: jnp.dtype = jnp.float32
+    is_encoder: bool = False
+
+    @nn.compact
+    def __call__(self, x: jnp.ndarray, deterministic: bool = True) -> jnp.ndarray:
+
+        gain = deepnet_gain["encoder" if self.is_encoder else "decoder"]["beta"](
+            self.config
+        )
+        if self.config.ln_positions in ["normformer", "cogview", "preln"]:
+            x = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=self.config.force_ln_scale,
+            )(x)
+        x = nn.Dense(
+            self.ffn_dim,
+            dtype=self.dtype,
+            use_bias=self.config.use_bias,
+            kernel_init=deepnet_init(gain)
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std),
+        )(x)
+        x = ACT2FN[self.config.activation_function](x)
+        if self.config.ln_positions in ["normformer"]:
+            x = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=self.config.force_ln_scale,
+            )(x)
+        x = nn.Dropout(rate=self.config.activation_dropout)(
+            x, deterministic=deterministic
+        )
+        x = nn.Dense(
+            self.embed_dim,
+            dtype=self.dtype,
+            use_bias=self.config.use_bias,
+            kernel_init=deepnet_init(gain)
+            if self.config.use_deepnet_scaling
+            else jax.nn.initializers.normal(self.config.init_std),
+        )(x)
+        if self.config.ln_positions in ["swinv2", "cogview"]:
+            x = norm(self.config.ln_type, dtype=self.dtype, epsilon=1e-05)(x)
+        x = nn.Dropout(rate=self.config.dropout)(x, deterministic=deterministic)
+        return x
+
+
+class FlaxBartEncoderLayer(nn.Module):
+    """
+    Edits:
+    - no bias
+    - use custom FlaxBartAttention
+    """
+
+    config: DalleBartConfig
+    dtype: jnp.dtype = jnp.float32
+    add_norm: bool = False
+    use_scale: bool = True
+
+    @nn.compact
+    def __call__(
+        self,
+        hidden_states: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+        output_attentions: bool = True,
+        deterministic: bool = True,
+    ) -> Tuple[jnp.ndarray]:
+
+        if self.config.use_scan:
+            hidden_states = hidden_states[0]
+
+        res_gain = (
+            deepnet_gain["encoder"]["alpha"](self.config)
+            if self.config.use_deepnet_scaling
+            else 1
+        )
+
+        embed_dim = self.config.d_model
+        residual = hidden_states
+        if self.config.ln_positions in ["normformer", "cogview", "preln"]:
+            hidden_states = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=self.config.force_ln_scale,
+            )(hidden_states)
+        hidden_states, attn_weights = FlaxBartAttention(
+            config=self.config,
+            embed_dim=embed_dim,
+            num_heads=self.config.encoder_attention_heads,
+            dropout=self.config.attention_dropout,
+            bias=self.config.use_bias,
+            dtype=self.dtype,
+            is_encoder=True,
+            q_length=self.config.max_text_length,
+            k_length=self.config.max_text_length,
+        )(hidden_states=hidden_states, attention_mask=attention_mask)
+
+        if self.config.ln_positions in ["normformer", "swinv2", "cogview"]:
+            hidden_states = norm(self.config.ln_type, dtype=self.dtype, epsilon=1e-05)(
+                hidden_states
+            )
+        hidden_states = nn.Dropout(rate=self.config.dropout)(
+            hidden_states, deterministic=deterministic
+        )
+        hidden_states = residual * res_gain + hidden_states
+        if self.config.ln_positions in ["postln"]:
+            hidden_states = norm(self.config.ln_type, dtype=self.dtype, epsilon=1e-05)(
+                hidden_states
+            )
+
+        residual = hidden_states
+        ff_block = (
+            GLU(
+                config=self.config,
+                ffn_dim=self.config.encoder_ffn_dim,
+                embed_dim=embed_dim,
+                dtype=self.dtype,
+                is_encoder=True,
+            )
+            if self.config.use_glu
+            else FFN(
+                config=self.config,
+                ffn_dim=self.config.encoder_ffn_dim,
+                embed_dim=embed_dim,
+                dtype=self.dtype,
+                is_encoder=True,
+            )
+        )
+        hidden_states = ff_block(hidden_states, deterministic=deterministic)
+        hidden_states = residual * res_gain + hidden_states
+        if self.add_norm:
+            use_scale = self.use_scale or self.config.force_ln_scale
+            hidden_states = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=use_scale,
+            )(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights,)
+
+        if self.config.use_scan:
+            outputs = (outputs, None)
+
+        return outputs
+
+
+class FlaxBartDecoderLayer(nn.Module):
+    """
+    Edits:
+    - no bias
+    - use custom FlaxBartAttention
+    """
+
+    config: DalleBartConfig
+    dtype: jnp.dtype = jnp.float32
+    add_norm: bool = False
+    use_scale: bool = True
+
+    @nn.compact
+    def __call__(
+        self,
+        hidden_states: jnp.ndarray,
+        attention_mask: jnp.ndarray,
+        encoder_hidden_states: Optional[jnp.ndarray] = None,
+        encoder_attention_mask: Optional[jnp.ndarray] = None,
+        init_cache: bool = False,
+        output_attentions: bool = True,
+        deterministic: bool = True,
+    ) -> Tuple[jnp.ndarray]:
+
+        if self.config.use_scan:
+            hidden_states = hidden_states[0]
+
+        res_gain = (
+            deepnet_gain["decoder"]["alpha"](self.config)
+            if self.config.use_deepnet_scaling
+            else 1
+        )
+
+        embed_dim = self.config.d_model
+        residual = hidden_states
+
+        # Self Attention
+        if self.config.ln_positions in ["normformer", "cogview", "preln"]:
+            hidden_states = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=self.config.force_ln_scale,
+            )(hidden_states)
+        hidden_states, attn_weights = FlaxBartAttention(
+            config=self.config,
+            embed_dim=embed_dim,
+            num_heads=self.config.decoder_attention_heads,
+            dropout=self.config.attention_dropout,
+            causal=True,
+            bias=self.config.use_bias,
+            dtype=self.dtype,
+            is_encoder=False,
+            q_length=self.config.image_length,
+            k_length=self.config.image_length,
+        )(
+            hidden_states=hidden_states,
+            attention_mask=attention_mask,
+            init_cache=init_cache,
+        )
+
+        if self.config.ln_positions in ["normformer", "swinv2", "cogview"]:
+            hidden_states = norm(self.config.ln_type, dtype=self.dtype, epsilon=1e-05)(
+                hidden_states
+            )
+        hidden_states = nn.Dropout(rate=self.config.dropout)(
+            hidden_states, deterministic=deterministic
+        )
+        hidden_states = residual * res_gain + hidden_states
+        if self.config.ln_positions in ["postln"]:
+            hidden_states = norm(self.config.ln_type, dtype=self.dtype, epsilon=1e-05)(
+                hidden_states
+            )
+
+        # Cross Attention
+        cross_attn_weights = None
+        if encoder_hidden_states is not None:
+            residual = hidden_states
+            if self.config.ln_positions in ["normformer", "cogview", "preln"]:
+                hidden_states = norm(
+                    self.config.ln_type,
+                    dtype=self.dtype,
+                    epsilon=1e-05,
+                    use_scale=self.config.force_ln_scale,
+                )(hidden_states)
+            hidden_states, cross_attn_weights = FlaxBartAttention(
+                config=self.config,
+                embed_dim=embed_dim,
+                num_heads=self.config.decoder_attention_heads,
+                dropout=self.config.attention_dropout,
+                bias=self.config.use_bias,
+                dtype=self.dtype,
+                is_encoder=False,
+                q_length=self.config.image_length,
+                k_length=self.config.max_text_length,
+            )(
+                hidden_states=hidden_states,
+                key_value_states=encoder_hidden_states,
+                attention_mask=encoder_attention_mask,
+            )
+            if self.config.ln_positions in ["normformer", "swinv2", "cogview"]:
+                hidden_states = norm(
+                    self.config.ln_type, dtype=self.dtype, epsilon=1e-05
+                )(hidden_states)
+            hidden_states = nn.Dropout(rate=self.config.dropout)(
+                hidden_states, deterministic=deterministic
+            )
+            hidden_states = residual * res_gain + hidden_states
+            if self.config.ln_positions in ["postln"]:
+                hidden_states = norm(
+                    self.config.ln_type, dtype=self.dtype, epsilon=1e-05
+                )(hidden_states)
+
+        # Feed forward
+        residual = hidden_states
+        ff_block = (
+            GLU(
+                config=self.config,
+                ffn_dim=self.config.decoder_ffn_dim,
+                embed_dim=embed_dim,
+                dtype=self.dtype,
+                is_encoder=False,
+            )
+            if self.config.use_glu
+            else FFN(
+                config=self.config,
+                ffn_dim=self.config.decoder_ffn_dim,
+                embed_dim=embed_dim,
+                dtype=self.dtype,
+                is_encoder=False,
+            )
+        )
+        hidden_states = ff_block(hidden_states, deterministic=deterministic)
+        hidden_states = residual * res_gain + hidden_states
+        if self.add_norm:
+            use_scale = self.use_scale or self.config.force_ln_scale
+            hidden_states = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=use_scale,
+            )(hidden_states)
+
+        outputs = (hidden_states,)
+
+        if output_attentions:
+            outputs += (attn_weights, cross_attn_weights)
+
+        if self.config.use_scan:
+            outputs = (outputs, None)
+
+        return outputs
+
+
+class FlaxBartEncoderLayerCollection(nn.Module):
+    config: DalleBartConfig
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    """
+    Edits:
+    - use custom FlaxBartEncoderLayer
+    - allow Gradient Checkpointing (nn.remat)
+    """
+
+    @nn.compact
+    def __call__(
+        self,
+        hidden_states,
+        attention_mask,
+        deterministic: bool = True,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ):
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+
+        n_layers = self.config.encoder_layers
+        layer = (
+            remat(
+                FlaxBartEncoderLayer,
+                static_argnums=(2, 3),
+                prevent_cse=not self.config.use_scan,
+            )
+            if self.config.gradient_checkpointing
+            else FlaxBartEncoderLayer
+        )
+
+        if self.config.use_scan:
+            # all blocks are the same so we use nn.scan
+            assert not output_attentions, "cannot scan with output_attentions"
+            assert not output_hidden_states, "cannot scan with output_hidden_states"
+            hidden_states = (hidden_states,)
+            # we use a scale on all norms (even last layer) to allow scanning
+            hidden_states, _ = nn.scan(
+                layer,
+                variable_axes={"params": 0, "cache": 0},
+                split_rngs={"params": True, "dropout": True},
+                in_axes=(nn.broadcast, nn.broadcast, nn.broadcast),
+                length=n_layers,
+            )(
+                self.config,
+                dtype=self.dtype,
+                add_norm=self.config.ln_positions == "postln",
+                name="FlaxBartEncoderLayers",
+            )(
+                hidden_states,
+                attention_mask,
+                output_attentions,
+                deterministic,
+            )
+            hidden_states = hidden_states[0]
+        else:
+            for i in range(n_layers):
+                if output_hidden_states:
+                    all_hidden_states += (hidden_states,)
+                # final layernorm on the output of the last layer
+                # or every 6 layers for Swin v2
+                add_norm = self.config.ln_positions == "postln" or (
+                    self.config.ln_positions == "swinv2"
+                    and ((i + 1) % 6 == 0)
+                    and (i != n_layers - 1)
+                )
+                # we don't need to scale the norm for the last layer
+                use_scale = i != n_layers - 1
+                layer_outputs = layer(
+                    self.config,
+                    dtype=self.dtype,
+                    add_norm=add_norm,
+                    use_scale=use_scale,
+                    name=f"FlaxBartEncoderLayer_{i}",
+                )(
+                    hidden_states,
+                    attention_mask,
+                    output_attentions,
+                    deterministic,
+                )
+                hidden_states = layer_outputs[0]
+                if output_attentions:
+                    all_self_attns += (layer_outputs[1],)
+
+            # add hidden states from the last layer
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+        outputs = [
+            hidden_states,
+            all_hidden_states,
+            all_self_attns,
+        ]
+
+        if not return_dict:
+            return tuple(v for v in outputs if v is not None)
+
+        return FlaxBaseModelOutput(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+        )
+
+
+class FlaxBartDecoderLayerCollection(nn.Module):
+    config: DalleBartConfig
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    """
+    Edits:
+    - use custom FlaxBartDecoderLayer
+    - allow Gradient Checkpointing (nn.remat)
+    """
+
+    @nn.compact
+    def __call__(
+        self,
+        hidden_states,
+        attention_mask,
+        encoder_hidden_states: Optional[jnp.ndarray] = None,
+        encoder_attention_mask: Optional[jnp.ndarray] = None,
+        deterministic: bool = True,
+        init_cache: bool = False,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+    ):
+        # decoder layers
+        all_hidden_states = () if output_hidden_states else None
+        all_self_attns = () if output_attentions else None
+        all_cross_attentions = (
+            () if (output_attentions and encoder_hidden_states is not None) else None
+        )
+
+        n_layers = self.config.decoder_layers
+        layer = (
+            remat(
+                FlaxBartDecoderLayer,
+                static_argnums=(4, 5, 6),
+                prevent_cse=not self.config.use_scan,
+            )
+            if self.config.gradient_checkpointing
+            else FlaxBartDecoderLayer
+        )
+
+        if self.config.use_scan:
+            # all blocks are the same so we use nn.scan
+            assert not output_attentions, "cannot scan with output_attentions"
+            assert not output_hidden_states, "cannot scan with output_hidden_states"
+            hidden_states = (hidden_states,)
+            # we use a scale on all norms (even last layer) to allow scanning
+            hidden_states, _ = nn.scan(
+                layer,
+                variable_axes={"params": 0, "cache": 0},
+                split_rngs={"params": True, "dropout": True},
+                in_axes=(
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                    nn.broadcast,
+                ),
+                length=n_layers,
+            )(
+                self.config,
+                dtype=self.dtype,
+                add_norm=self.config.ln_positions == "postln",
+                name="FlaxBartDecoderLayers",
+            )(
+                hidden_states,
+                attention_mask,
+                encoder_hidden_states,
+                encoder_attention_mask,
+                init_cache,
+                output_attentions,
+                deterministic,
+            )
+            hidden_states = hidden_states[0]
+
+        else:
+            for i in range(n_layers):
+                if output_hidden_states:
+                    all_hidden_states += (hidden_states,)
+                # final layernorm on the output of the last layer
+                # or every 6 layers for Swin v2
+                add_norm = self.config.ln_positions == "postln" or (
+                    self.config.ln_positions == "swinv2"
+                    and ((i + 1) % 6 == 0)
+                    and (i != n_layers - 1)
+                )
+                # we don't need to scale the norm for the last layer
+                use_scale = i != n_layers - 1
+                layer_outputs = layer(
+                    self.config,
+                    dtype=self.dtype,
+                    add_norm=add_norm,
+                    use_scale=use_scale,
+                    name=f"FlaxBartDecoderLayer_{i}",
+                )(
+                    hidden_states,
+                    attention_mask,
+                    encoder_hidden_states,
+                    encoder_attention_mask,
+                    init_cache,
+                    output_attentions,
+                    deterministic,
+                )
+
+                hidden_states = layer_outputs[0]
+                if output_attentions:
+                    all_self_attns += (layer_outputs[1],)
+
+                    if encoder_hidden_states is not None:
+                        all_cross_attentions += (layer_outputs[2],)
+
+            # add hidden states from the last decoder layer
+            if output_hidden_states:
+                all_hidden_states += (hidden_states,)
+
+        outputs = [
+            hidden_states,
+            all_hidden_states,
+            all_self_attns,
+            all_cross_attentions,
+        ]
+
+        if not return_dict:
+            return tuple(v for v in outputs if v is not None)
+
+        return FlaxBaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=hidden_states,
+            hidden_states=all_hidden_states,
+            attentions=all_self_attns,
+            cross_attentions=all_cross_attentions,
+        )
+
+
+class FlaxBartEncoder(nn.Module):
+    config: DalleBartConfig
+    embed_tokens: nn.Embed
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    """
+    Edits:
+    - offset set to 0 (no padding token)
+    - use max_text_length instead of max_position_embeddings
+    - use custom FlaxBartEncoderLayerCollection
+    - embed_tokens cannot be None (issue at compile time)
+    """
+
+    def setup(self):
+        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+        embed_dim = self.config.d_model
+        self.padding_idx = self.config.pad_token_id
+        self.embed_scale = math.sqrt(embed_dim) if self.config.scale_embedding else 1.0
+
+        # Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
+        # and adjust num_embeddings appropriately. Other models don't have this hack
+        self.offset = 0
+        if self.config.use_absolute_position_embeddings:
+            self.embed_positions = nn.Embed(
+                self.config.max_text_length + self.offset,  # image length for BOS
+                embed_dim,
+                embedding_init=jax.nn.initializers.normal(self.config.init_std),
+            )
+        self.layers = FlaxBartEncoderLayerCollection(self.config, self.dtype)
+        self.layernorm_embedding = norm(
+            self.config.ln_type, dtype=self.dtype, epsilon=1e-05
+        )
+
+        # postln is already applied in every layer
+        if self.config.use_final_ln_encoder and self.config.ln_positions != "postln":
+            self.final_ln = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=self.config.force_ln_scale,
+            )
+        else:
+            self.final_ln = None
+
+    def __call__(
+        self,
+        input_ids,
+        attention_mask,
+        position_ids,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+        deterministic: bool = True,
+    ):
+        input_shape = input_ids.shape
+        input_ids = input_ids.reshape(-1, input_shape[-1])
+
+        hidden_states = self.embed_tokens(input_ids) * self.embed_scale
+
+        if self.config.use_absolute_position_embeddings:
+            embed_pos = self.embed_positions(position_ids + self.offset)
+            hidden_states = hidden_states + embed_pos
+
+        hidden_states = self.layernorm_embedding(hidden_states)
+        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+        outputs = self.layers(
+            hidden_states,
+            attention_mask,
+            deterministic=deterministic,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.final_ln is None:
+            final_output = outputs[0]
+        else:
+            final_output = self.final_ln(outputs[0])
+
+        if not return_dict:
+            return (final_output,) + outputs[1:]
+
+        return FlaxBaseModelOutput(
+            last_hidden_state=final_output,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+        )
+
+
+class FlaxBartDecoder(nn.Module):
+    config: DalleBartConfig
+    embed_tokens: nn.Embed
+    dtype: jnp.dtype = jnp.float32  # the dtype of the computation
+    """
+    Edits:
+    - offset set to 0 (no padding token)
+    - use image_length instead of max_position_embeddings
+    - use custom FlaxBartDecoderLayerCollection
+    - embed_tokens cannot be None (issue at compile time)
+    """
+
+    def setup(self):
+        self.dropout_layer = nn.Dropout(rate=self.config.dropout)
+
+        embed_dim = self.config.d_model
+        self.padding_idx = self.config.pad_token_id
+        self.embed_scale = (
+            math.sqrt(self.config.d_model) if self.config.scale_embedding else 1.0
+        )
+
+        # Bart is set up so that if padding_idx is specified then offset the embedding ids by 2
+        # and adjust num_embeddings appropriately. Other models don't have this hack
+        self.offset = 0
+        if self.config.use_absolute_position_embeddings:
+            self.embed_positions = nn.Embed(
+                self.config.image_length + self.offset,  # image length for BOS
+                embed_dim,
+                embedding_init=jax.nn.initializers.normal(self.config.init_std),
+            )
+
+        self.layers = FlaxBartDecoderLayerCollection(self.config, self.dtype)
+        self.layernorm_embedding = norm(
+            self.config.ln_type, dtype=self.dtype, epsilon=1e-05
+        )
+
+        # postln is already applied in every layer
+        if self.config.use_final_ln_decoder and self.config.ln_positions != "postln":
+            self.final_ln = norm(
+                self.config.ln_type,
+                dtype=self.dtype,
+                epsilon=1e-05,
+                use_scale=self.config.force_ln_scale,
+            )
+
+    def __call__(
+        self,
+        input_ids,
+        attention_mask,
+        position_ids,
+        encoder_hidden_states: Optional[jnp.ndarray] = None,
+        encoder_attention_mask: Optional[jnp.ndarray] = None,
+        init_cache: bool = False,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+        deterministic: bool = True,
+    ):
+        input_shape = input_ids.shape
+        input_ids = input_ids.reshape(-1, input_shape[-1])
+
+        hidden_states = self.embed_tokens(input_ids) * self.embed_scale
+
+        if self.config.use_absolute_position_embeddings:
+            embed_pos = self.embed_positions(position_ids + self.offset)
+            hidden_states = hidden_states + embed_pos
+
+        hidden_states = self.layernorm_embedding(hidden_states)
+        hidden_states = self.dropout_layer(hidden_states, deterministic=deterministic)
+
+        outputs = self.layers(
+            hidden_states,
+            attention_mask,
+            encoder_hidden_states,
+            encoder_attention_mask,
+            deterministic=deterministic,
+            init_cache=init_cache,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+        )
+
+        if self.final_ln is None:
+            final_output = outputs[0]
+        else:
+            final_output = self.final_ln(outputs[0])
+
+        if not return_dict:
+            return (final_output,) + outputs[1:]
+
+        return FlaxBaseModelOutputWithPastAndCrossAttentions(
+            last_hidden_state=final_output,
+            hidden_states=outputs.hidden_states,
+            attentions=outputs.attentions,
+            cross_attentions=outputs.cross_attentions,
+        )
+
+
+class FlaxBartModule(FlaxBartModule):
+    """
+    Edits
+    - use custom FlaxBartEncoder & FlaxBartDecoder
+    - use separate embeddings for Encoder & Decoder
+    """
+
+    def setup(self):
+        encoder_embed_tokens = nn.Embed(
+            self.config.encoder_vocab_size,
+            self.config.d_model,
+            embedding_init=jax.nn.initializers.normal(self.config.init_std),
+        )
+        decoder_embed_tokens = nn.Embed(
+            self.config.image_vocab_size + 1,  # image vocab size + 1 for BOS
+            self.config.d_model,
+            embedding_init=jax.nn.initializers.normal(self.config.init_std),
+        )
+
+        self.encoder = FlaxBartEncoder(
+            self.config, dtype=self.dtype, embed_tokens=encoder_embed_tokens
+        )
+        self.decoder = FlaxBartDecoder(
+            self.config, dtype=self.dtype, embed_tokens=decoder_embed_tokens
+        )
+
+
+class FlaxBartForConditionalGenerationModule(FlaxBartForConditionalGenerationModule):
+    """
+    Edits:
+    - no bias
+    - lm_head set to image_vocab_size + 1 (for BOS)
+    - uses custom FlaxBartModule
+    """
+
+    def setup(self):
+        self.model = FlaxBartModule(config=self.config, dtype=self.dtype)
+        self.lm_head = nn.Dense(
+            self.config.image_vocab_size
+            + 1,  # image vocab size + 1 for BOS to have same size as decoder inputs (for sharding)
+            use_bias=False,
+            dtype=self.dtype,
+            kernel_init=jax.nn.initializers.normal(self.config.init_std),
+        )
+
+    def __call__(
+        self,
+        input_ids,
+        attention_mask,
+        decoder_input_ids,
+        decoder_attention_mask,
+        position_ids,
+        decoder_position_ids,
+        output_attentions: bool = False,
+        output_hidden_states: bool = False,
+        return_dict: bool = True,
+        deterministic: bool = True,
+    ):
+        outputs = self.model(
+            input_ids=input_ids,
+            attention_mask=attention_mask,
+            decoder_input_ids=decoder_input_ids,
+            decoder_attention_mask=decoder_attention_mask,
+            position_ids=position_ids,
+            decoder_position_ids=decoder_position_ids,
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            deterministic=deterministic,
+        )
+
+        hidden_states = outputs[0]
+
+        if self.config.tie_word_embeddings:
+            shared_embedding = self.model.variables["params"]["shared"]["embedding"]
+            lm_logits = self.lm_head.apply(
+                {"params": {"kernel": shared_embedding.T}}, hidden_states
+            )
+        else:
+            lm_logits = self.lm_head(hidden_states)
+
+        if not return_dict:
+            output = (lm_logits,) + outputs[1:]
+            return output
+
+        return FlaxSeq2SeqLMOutput(
+            logits=lm_logits,
+            decoder_hidden_states=outputs.decoder_hidden_states,
+            decoder_attentions=outputs.decoder_attentions,
+            cross_attentions=outputs.cross_attentions,
+            encoder_last_hidden_state=outputs.encoder_last_hidden_state,
+            encoder_hidden_states=outputs.encoder_hidden_states,
+            encoder_attentions=outputs.encoder_attentions,
+        )
+
+
+@flax.struct.dataclass
+class SampleState:
+    cur_len: jnp.ndarray
+    sequences: jnp.ndarray
+    running_token: jnp.ndarray
+    is_sent_finished: jnp.ndarray
+    prng_key: jnp.ndarray
+    model_kwargs: Dict[str, jnp.ndarray]
+    model_kwargs_uncond: Dict[str, jnp.ndarray]
+
+
+class DalleBart(PretrainedFromWandbMixin, FlaxBartForConditionalGeneration):
+    """
+    Edits:
+    - renamed from FlaxBartForConditionalGeneration
+    - uses custom FlaxBartForConditionalGenerationModule
+    - no bias in decode method
+    - custom prepare_inputs_for_generation using "max_length - 1" to avoid issues
+      related to position embedding during model.generate()
+    - custom generate method to allow super conditions
+    - num_params property
+    - unscan function
+    """
+
+    module_class = FlaxBartForConditionalGenerationModule
+    config_class = DalleBartConfig
+
+    def num_params(self, params=None):
+        if params is None:
+            params = self.params
+        num_params = jax.tree_map(
+            lambda param: param.size, flatten_dict(unfreeze(params))
+        ).values()
+        return sum(list(num_params))
+
+    def unscan(self, params):
+        if self.config.use_scan:
+            self.config.use_scan = False
+            params = flatten_dict(params)
+            scanned_keys = [k for k in params.keys() if "layers" in k]
+            for k in scanned_keys:
+                v = params[k]
+                name_idx = k.index("layers") + 1
+                for i in range(len(v)):
+                    new_k = (
+                        *k[:name_idx],
+                        f"{k[name_idx][:-1]}_{i}",
+                        *k[name_idx + 1 :],
+                    )
+                    params[new_k] = v[i]
+                del params[k]
+            params = unflatten_dict(params)
+        return params
+
+    def decode(
+        self,
+        decoder_input_ids,
+        encoder_outputs,
+        encoder_attention_mask: Optional[jnp.ndarray] = None,
+        decoder_attention_mask: Optional[jnp.ndarray] = None,
+        decoder_position_ids: Optional[jnp.ndarray] = None,
+        past_key_values: dict = None,
+        output_attentions: Optional[bool] = None,
+        output_hidden_states: Optional[bool] = None,
+        return_dict: Optional[bool] = None,
+        train: bool = False,
+        params: dict = None,
+        dropout_rng: PRNGKey = None,
+    ):
+        output_attentions = (
+            output_attentions
+            if output_attentions is not None
+            else self.config.output_attentions
+        )
+        output_hidden_states = (
+            output_hidden_states
+            if output_hidden_states is not None
+            else self.config.output_hidden_states
+        )
+        return_dict = (
+            return_dict if return_dict is not None else self.config.return_dict
+        )
+
+        encoder_hidden_states = encoder_outputs[0]
+        if encoder_attention_mask is None:
+            batch_size, sequence_length = encoder_hidden_states.shape[:2]
+            encoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+        batch_size, sequence_length = decoder_input_ids.shape
+        if decoder_attention_mask is None:
+            decoder_attention_mask = jnp.ones((batch_size, sequence_length))
+
+        if decoder_position_ids is None:
+            if past_key_values is not None:
+                raise ValueError(
+                    "Make sure to provide `decoder_position_ids` when passing `past_key_values`."
+                )
+
+            decoder_position_ids = jnp.broadcast_to(
+                jnp.arange(sequence_length)[None, :], (batch_size, sequence_length)
+            )
+
+        # Handle any PRNG if needed
+        rngs = {}
+        if dropout_rng is not None:
+            rngs["dropout"] = dropout_rng
+
+        inputs = {"params": params or self.params}
+
+        # if past_key_values are passed then cache is already initialized a private flag init_cache has to be
+        # passed down to ensure cache is used. It has to be made sure that cache is marked as mutable so that
+        # it can be changed by FlaxBartAttention module
+        if past_key_values:
+            inputs["cache"] = past_key_values
+            mutable = ["cache"]
+        else:
+            mutable = False
+
+        def _decoder_forward(
+            module,
+            decoder_input_ids,
+            decoder_attention_mask,
+            decoder_position_ids,
+            **kwargs,
+        ):
+            decoder_module = module._get_decoder_module()
+            outputs = decoder_module(
+                decoder_input_ids,
+                decoder_attention_mask,
+                decoder_position_ids,
+                **kwargs,
+            )
+            hidden_states = outputs[0]
+
+            if self.config.tie_word_embeddings:
+                shared_embedding = module.model.variables["params"]["shared"][
+                    "embedding"
+                ]
+                lm_logits = module.lm_head.apply(
+                    {"params": {"kernel": shared_embedding.T}}, hidden_states
+                )
+            else:
+                lm_logits = module.lm_head(hidden_states)
+
+            return lm_logits, outputs
+
+        outputs = self.module.apply(
+            inputs,
+            decoder_input_ids=jnp.array(decoder_input_ids, dtype="i4"),
+            decoder_attention_mask=jnp.array(decoder_attention_mask, dtype="i4"),
+            decoder_position_ids=jnp.array(decoder_position_ids, dtype="i4"),
+            encoder_hidden_states=encoder_hidden_states,
+            encoder_attention_mask=jnp.array(encoder_attention_mask, dtype="i4"),
+            output_attentions=output_attentions,
+            output_hidden_states=output_hidden_states,
+            return_dict=return_dict,
+            deterministic=not train,
+            rngs=rngs,
+            mutable=mutable,
+            method=_decoder_forward,
+        )
+
+        if past_key_values is None:
+            lm_logits, decoder_outputs = outputs
+        else:
+            (lm_logits, decoder_outputs), past = outputs
+
+        if return_dict:
+            outputs = FlaxCausalLMOutputWithCrossAttentions(
+                logits=lm_logits,
+                hidden_states=decoder_outputs.hidden_states,
+                attentions=decoder_outputs.attentions,
+                cross_attentions=decoder_outputs.cross_attentions,
+            )
+        else:
+            outputs = (lm_logits,) + decoder_outputs[1:]
+
+        # add updated cache to model output
+        if past_key_values is not None and return_dict:
+            outputs["past_key_values"] = unfreeze(past["cache"])
+            return outputs
+        elif past_key_values is not None and not return_dict:
+            outputs = outputs[:1] + (unfreeze(past["cache"]),) + outputs[1:]
+
+        return outputs
+
+    def prepare_inputs_for_generation(
+        self,
+        decoder_input_ids,
+        max_length,
+        attention_mask: Optional[jnp.DeviceArray] = None,
+        decoder_attention_mask: Optional[jnp.DeviceArray] = None,
+        encoder_outputs=None,
+        **kwargs,
+    ):
+        # initializing the cache
+        batch_size, seq_length = decoder_input_ids.shape
+
+        past_key_values = self.init_cache(batch_size, max_length - 1, encoder_outputs)
+        # Note that usually one would have to put 0's in the attention_mask for x > input_ids.shape[-1] and x < cache_length.
+        # But since the decoder uses a causal mask, those positions are masked anyways.
+        # Thus we can create a single static attention_mask here, which is more efficient for compilation
+        extended_attention_mask = jnp.ones((batch_size, max_length - 1), dtype="i4")
+        if decoder_attention_mask is not None:
+            position_ids = decoder_attention_mask.cumsum(axis=-1) - 1
+            extended_attention_mask = lax.dynamic_update_slice(
+                extended_attention_mask, decoder_attention_mask, (0, 0)
+            )
+        else:
+            position_ids = jnp.broadcast_to(
+                jnp.arange(seq_length, dtype="i4")[None, :], (batch_size, seq_length)
+            )
+
+        return {
+            "past_key_values": past_key_values,
+            "encoder_outputs": encoder_outputs,
+            "encoder_attention_mask": attention_mask,
+            "decoder_attention_mask": extended_attention_mask,
+            "decoder_position_ids": position_ids,
+        }
+
+    def generate(
+        self,
+        input_ids: jnp.ndarray,
+        attention_mask: Optional[jnp.ndarray] = None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        bos_token_id: Optional[int] = None,
+        eos_token_id: Optional[int] = None,
+        decoder_start_token_id: Optional[int] = None,
+        do_sample: Optional[bool] = None,
+        prng_key: Optional[jnp.ndarray] = None,
+        top_k: Optional[int] = None,
+        top_p: Optional[float] = None,
+        temperature: Optional[float] = None,
+        num_beams: Optional[int] = None,
+        no_repeat_ngram_size: Optional[int] = None,
+        min_length: Optional[int] = None,
+        forced_bos_token_id: Optional[int] = None,
+        forced_eos_token_id: Optional[int] = None,
+        length_penalty: Optional[float] = None,
+        early_stopping: Optional[bool] = None,
+        trace: bool = True,
+        params: Optional[Dict[str, jnp.ndarray]] = None,
+        condition_scale: Optional[float] = 1.0,
+        input_ids_uncond: Optional[jnp.ndarray] = None,
+        attention_mask_uncond: Optional[jnp.ndarray] = None,
+        **model_kwargs,
+    ):
+        """Edit: Allow super conditioning."""
+
+        # set init values
+        max_length = max_length if max_length is not None else self.config.max_length
+        bos_token_id = (
+            bos_token_id if bos_token_id is not None else self.config.bos_token_id
+        )
+        pad_token_id = (
+            pad_token_id if pad_token_id is not None else self.config.pad_token_id
+        )
+        eos_token_id = (
+            eos_token_id if eos_token_id is not None else self.config.eos_token_id
+        )
+        decoder_start_token_id = (
+            decoder_start_token_id
+            if decoder_start_token_id
+            else self.config.decoder_start_token_id
+        )
+        prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
+
+        if decoder_start_token_id is None and self.config.is_encoder_decoder:
+            raise ValueError(
+                "`decoder_start_token_id` has to be defined for encoder-decoder generation."
+            )
+
+        do_sample = do_sample if do_sample is not None else self.config.do_sample
+        num_beams = num_beams if num_beams is not None else self.config.num_beams
+
+        if self.config.is_encoder_decoder:
+            # add encoder_outputs to model_kwargs
+            if model_kwargs.get("encoder_outputs") is None:
+                model_kwargs_input = dict(model_kwargs)
+                model_kwargs = self._prepare_encoder_decoder_kwargs_for_generation(
+                    input_ids,
+                    params,
+                    {"attention_mask": attention_mask, **model_kwargs_input},
+                )
+                if condition_scale != 1.0:
+                    assert (
+                        input_ids_uncond is not None
+                    ), "`input_ids_uncond` has to be defined for super conditioning."
+                    assert (
+                        do_sample is True
+                    ), "`do_sample` has to be True for super conditioning."
+                    assert (
+                        num_beams == 1
+                    ), "`num_beams` has to be 1 for super conditioning."
+                    model_kwargs_uncond = (
+                        self._prepare_encoder_decoder_kwargs_for_generation(
+                            input_ids_uncond,
+                            params,
+                            {
+                                "attention_mask": attention_mask_uncond,
+                                **model_kwargs_input,
+                            },
+                        )
+                    )
+                else:
+                    model_kwargs_uncond = None
+            # prepare decoder_input_ids for generation
+            input_ids = (
+                jnp.ones((input_ids.shape[0], 1), dtype="i4") * decoder_start_token_id
+            )
+
+        if not do_sample and num_beams == 1:
+            logits_processor = self._get_logits_processor(
+                no_repeat_ngram_size,
+                min_length,
+                max_length,
+                eos_token_id,
+                forced_bos_token_id,
+                forced_eos_token_id,
+            )
+            return self._greedy_search(
+                input_ids,
+                max_length,
+                pad_token_id,
+                eos_token_id,
+                logits_processor=logits_processor,
+                trace=trace,
+                params=params,
+                model_kwargs=model_kwargs,
+            )
+        elif do_sample and num_beams == 1:
+            logits_warper = self._get_logits_warper(
+                top_k=top_k, top_p=top_p, temperature=temperature
+            )
+            logits_processor = self._get_logits_processor(
+                no_repeat_ngram_size,
+                min_length,
+                max_length,
+                eos_token_id,
+                forced_bos_token_id,
+                forced_eos_token_id,
+            )
+            return self._sample(
+                input_ids,
+                max_length,
+                pad_token_id,
+                eos_token_id,
+                prng_key,
+                logits_warper=logits_warper,
+                logits_processor=logits_processor,
+                trace=trace,
+                params=params,
+                model_kwargs=model_kwargs,
+                condition_scale=condition_scale,
+                model_kwargs_uncond=model_kwargs_uncond,
+            )
+        elif not do_sample and num_beams > 1:
+            # broadcast input_ids & encoder_outputs
+            input_ids = self._expand_to_num_beams(input_ids, num_beams=num_beams)
+
+            if "encoder_outputs" in model_kwargs:
+                model_kwargs["encoder_outputs"][
+                    "last_hidden_state"
+                ] = self._expand_to_num_beams(
+                    model_kwargs["encoder_outputs"]["last_hidden_state"],
+                    num_beams=num_beams,
+                )
+
+            if "attention_mask" in model_kwargs:
+                model_kwargs["attention_mask"] = self._expand_to_num_beams(
+                    model_kwargs["attention_mask"], num_beams=num_beams
+                )
+
+            logits_processor = self._get_logits_processor(
+                no_repeat_ngram_size,
+                min_length,
+                max_length,
+                eos_token_id,
+                forced_bos_token_id,
+                forced_eos_token_id,
+            )
+
+            return self._beam_search(
+                input_ids,
+                max_length,
+                pad_token_id,
+                eos_token_id,
+                length_penalty=length_penalty,
+                early_stopping=early_stopping,
+                logits_processor=logits_processor,
+                trace=trace,
+                params=params,
+                model_kwargs=model_kwargs,
+            )
+        else:
+            raise NotImplementedError("`Beam sampling is currently not implemented.")
+
+    def _sample(
+        self,
+        input_ids: None,
+        max_length: Optional[int] = None,
+        pad_token_id: Optional[int] = None,
+        eos_token_id: Optional[int] = None,
+        prng_key: Optional[jnp.ndarray] = None,
+        logits_processor=None,
+        logits_warper=None,
+        trace: bool = True,
+        params: Optional[Dict[str, jnp.ndarray]] = None,
+        model_kwargs: Optional[Dict[str, jnp.ndarray]] = None,
+        condition_scale: float = 1.0,
+        model_kwargs_uncond: Optional[Dict[str, jnp.ndarray]] = None,
+    ):
+        # init values
+        max_length = max_length if max_length is not None else self.config.max_length
+        pad_token_id = (
+            pad_token_id if pad_token_id is not None else self.config.pad_token_id
+        )
+        eos_token_id = (
+            eos_token_id if eos_token_id is not None else self.config.eos_token_id
+        )
+        prng_key = prng_key if prng_key is not None else jax.random.PRNGKey(0)
+
+        batch_size, cur_len = input_ids.shape
+
+        eos_token_id = jnp.array(eos_token_id)
+        pad_token_id = jnp.array(pad_token_id)
+        cur_len = jnp.array(cur_len)
+
+        # per batch-item holding current token in loop.
+        sequences = jnp.full((batch_size, max_length), pad_token_id, dtype=jnp.int32)
+        sequences = lax.dynamic_update_slice(sequences, input_ids, (0, 0))
+
+        # per batch-item state bit indicating if sentence has finished.
+        is_sent_finished = jnp.zeros((batch_size,), dtype=jnp.bool_)
+
+        # For Seq2Seq generation, we only need to use the decoder instead of the whole model in generation loop
+        # and pass it the `encoder_outputs`, which are part of the `model_kwargs`.
+        model = self.decode if self.config.is_encoder_decoder else self
+
+        # initialize model specific kwargs
+        model_kwargs = self.prepare_inputs_for_generation(
+            input_ids, max_length, **model_kwargs
+        )
+        if condition_scale != 1.0:
+            model_kwargs_uncond = self.prepare_inputs_for_generation(
+                input_ids, max_length, **model_kwargs_uncond
+            )
+
+        # initialize state
+        state = SampleState(
+            cur_len=cur_len,
+            sequences=sequences,
+            running_token=input_ids,
+            is_sent_finished=is_sent_finished,
+            prng_key=prng_key,
+            model_kwargs=model_kwargs,
+            model_kwargs_uncond=model_kwargs_uncond,
+        )
+
+        def sample_search_cond_fn(state):
+            """state termination condition fn."""
+            has_reached_max_length = state.cur_len == max_length
+            all_sequence_finished = jnp.all(state.is_sent_finished)
+            finish_generation = jnp.logical_or(
+                has_reached_max_length, all_sequence_finished
+            )
+            return ~finish_generation
+
+        def sample_search_body_fn(state):
+            """state update fn."""
+            prng_key, prng_key_next = jax.random.split(state.prng_key)
+            model_outputs = model(
+                state.running_token, params=params, **state.model_kwargs
+            )
+
+            logits = model_outputs.logits[:, -1]
+
+            # perform super conditioning
+            # Source: @RiversHaveWings - https://twitter.com/RiversHaveWings/status/1478093658716966912?s=20&t=xdm-wZ61Wf7OLnE_NJHZ1w
+            if condition_scale != 1.0:
+                model_outputs_uncond = model(
+                    state.running_token, params=params, **state.model_kwargs_uncond
+                )
+                logits_uncond = model_outputs_uncond.logits[:, -1]
+                logits = logits_uncond + condition_scale * (logits - logits_uncond)
+            else:
+                model_outputs_uncond = None
+
+            # apply min_length, ...
+            logits = logits_processor(state.sequences, logits, state.cur_len)
+            # apply top_k, top_k, temperature
+            logits = logits_warper(logits, logits, state.cur_len)
+
+            next_token = jax.random.categorical(prng_key, logits, axis=-1)
+
+            next_is_sent_finished = state.is_sent_finished | (
+                next_token == eos_token_id
+            )
+            next_token = (
+                next_token * ~next_is_sent_finished
+                + pad_token_id * next_is_sent_finished
+            )
+            next_token = next_token[:, None]
+
+            next_sequences = lax.dynamic_update_slice(
+                state.sequences, next_token, (0, state.cur_len)
+            )
+            next_model_kwargs = self.update_inputs_for_generation(
+                model_outputs, state.model_kwargs
+            )
+            next_model_kwargs_uncond = (
+                self.update_inputs_for_generation(
+                    model_outputs_uncond, state.model_kwargs_uncond
+                )
+                if condition_scale != 1.0
+                else None
+            )
+
+            return SampleState(
+                cur_len=state.cur_len + 1,
+                sequences=next_sequences,
+                running_token=next_token,
+                is_sent_finished=next_is_sent_finished,
+                model_kwargs=next_model_kwargs,
+                model_kwargs_uncond=next_model_kwargs_uncond,
+                prng_key=prng_key_next,
+            )
+
+        # The very first prompt often has sequence length > 1, so run outside of `lax.while_loop` to comply with TPU
+        if input_ids.shape[1] > 1:
+            state = sample_search_body_fn(state)
+
+        if not trace:
+            state = self._run_loop_in_debug(
+                sample_search_cond_fn, sample_search_body_fn, state
+            )
+        else:
+            state = lax.while_loop(sample_search_cond_fn, sample_search_body_fn, state)
+
+        return FlaxSampleOutput(sequences=state.sequences)

partitions.py

@@ -0,0 +1,76 @@
+import re
+
+from flax.core.frozen_dict import freeze
+from flax.traverse_util import flatten_dict, unflatten_dict
+from jax.experimental import PartitionSpec as P
+
+# utils adapted from https://github.com/google-research/google-research/blob/master/flax_models/t5x/partitions.py
+# Sentinels
+_unmatched = object()
+
+# For specifying empty leaf dict `{}`
+empty_dict = object()
+
+
+def _match(qs, ks):
+    """Return True if regexes in qs match any window of strings in tuple ks."""
+    # compile regexes and force complete match
+    qts = tuple(map(lambda x: re.compile(x + "$"), qs))
+    for i in range(len(ks) - len(qs) + 1):
+        matches = [x.match(y) for x, y in zip(qts, ks[i:])]
+        if matches and all(matches):
+            return True
+    return False
+
+
+def _replacement_rules(rules):
+    def replace(key, val):
+        for rule, replacement in rules:
+            if _match(rule, key):
+                return replacement
+        return val
+
+    return replace
+
+
+def _get_partition_rules():
+    return [
+        # embeddings
+        (("embed_positions", "embedding"), P("mp", None)),
+        (("embed_tokens", "embedding"), P("mp", None)),
+        (("rel_bias", "embedding"), P(None, "mp")),
+        # attention
+        (("(q_proj|k_proj|v_proj)", "kernel"), P(None, "mp")),
+        (("out_proj", "kernel"), P("mp", None)),
+        # FFN
+        (("Dense_0", "kernel"), P(None, "mp")),
+        (("GLU.*", "Dense_1", "kernel"), P(None, "mp")),
+        (("GLU.*", "Dense_2", "kernel"), P("mp", None)),
+        (("FFN.*", "Dense_1", "kernel"), P("mp", None)),
+        # layer norms
+        (("(bias|scale)",), None),
+        (("lm_head", "kernel"), P(None, "mp")),
+        # head scale and tau
+        (("(head_scale|tau)",), None),
+    ]
+
+
+def set_partitions(in_dict, use_scan):
+    rules = _get_partition_rules()
+    replace = _replacement_rules(rules)
+    initd = {k: _unmatched for k in flatten_dict(in_dict)}
+    result = {k: replace(k, v) for k, v in initd.items()}
+    for k, v in result.items():
+        if v == _unmatched:
+            print(f"Unmatched -> {k}")
+    l = list(result.keys())
+    if use_scan:
+        # add None dimension to layers
+        result = {
+            k: (P(*(None,) + v) if v is not None else None)
+            if any(x in k for x in ["FlaxBartEncoderLayers", "FlaxBartDecoderLayers"])
+            else v
+            for k, v in result.items()
+        }
+    assert _unmatched not in result.values(), "Incomplete partition spec."
+    return freeze(unflatten_dict(result))

processor.py

@@ -0,0 +1,60 @@
+""" DalleBart processor """
+
+from typing import List
+
+import jax.numpy as jnp
+
+from .configuration import DalleBartConfig
+from .text import TextNormalizer
+from .tokenizer import DalleBartTokenizer
+from .utils import PretrainedFromWandbMixin
+
+
+class DalleBartProcessorBase:
+    def __init__(
+        self, tokenizer: DalleBartTokenizer, normalize_text: bool, max_text_length: int
+    ):
+        self.tokenizer = tokenizer
+        self.normalize_text = normalize_text
+        self.max_text_length = max_text_length
+        if normalize_text:
+            self.text_processor = TextNormalizer()
+        # create unconditional tokens
+        uncond = self.tokenizer(
+            "",
+            return_tensors="jax",
+            padding="max_length",
+            truncation=True,
+            max_length=self.max_text_length,
+        ).data
+        self.input_ids_uncond = uncond["input_ids"]
+        self.attention_mask_uncond = uncond["attention_mask"]
+
+    def __call__(self, text: List[str] = None):
+        # check that text is not a string
+        assert not isinstance(text, str), "text must be a list of strings"
+
+        if self.normalize_text:
+            text = [self.text_processor(t) for t in text]
+        res = self.tokenizer(
+            text,
+            return_tensors="jax",
+            padding="max_length",
+            truncation=True,
+            max_length=self.max_text_length,
+        ).data
+        # tokens used only with super conditioning
+        n = len(text)
+        res["input_ids_uncond"] = jnp.repeat(self.input_ids_uncond, n, axis=0)
+        res["attention_mask_uncond"] = jnp.repeat(self.attention_mask_uncond, n, axis=0)
+        return res
+
+    @classmethod
+    def from_pretrained(cls, *args, **kwargs):
+        tokenizer = DalleBartTokenizer.from_pretrained(*args, **kwargs)
+        config = DalleBartConfig.from_pretrained(*args, **kwargs)
+        return cls(tokenizer, config.normalize_text, config.max_text_length)
+
+
+class DalleBartProcessor(PretrainedFromWandbMixin, DalleBartProcessorBase):
+    pass

pypi_release.yml

@@ -0,0 +1,31 @@
+# This workflow uses actions that are not certified by GitHub.
+# They are provided by a third-party and are governed by
+# separate terms of service, privacy policy, and support
+# documentation.
+
+name: Upload Python Package
+
+on:
+  release:
+    types: [published]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v3
+      - name: Set up Python
+        uses: actions/setup-python@v3
+        with:
+          python-version: "3.x"
+      - name: Install dependencies
+        run: |
+          python -m pip install --upgrade pip
+          pip install build
+      - name: Build package
+        run: python -m build
+      - name: Publish package
+        uses: pypa/gh-action-pypi-publish@27b31702a0e7fc50959f5ad993c78deac1bdfc29
+        with:
+          user: __token__
+          password: ${{ secrets.PYPI_API_TOKEN }}

quantization_utils.py

@@ -0,0 +1,124 @@
+# coding=utf-8
+# Copyright 2022 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""Helper routines for quantization."""
+
+from typing import Any
+
+import chex
+import jax.numpy as jnp
+from flax import struct
+
+
+# pylint:disable=no-value-for-parameter
+@struct.dataclass
+class QuantizedValue:
+    """State associated with quantized value."""
+
+    quantized: chex.Array
+    diagonal: chex.Array  # Diagonal (if extract_diagonal is set)
+    bucket_size: chex.Array
+    quantized_dtype: jnp.dtype = struct.field(
+        pytree_node=False
+    )  # Dtype for the quantized value.
+    extract_diagonal: bool = struct.field(pytree_node=False)  # In case its centered.
+    shape: Any = struct.field(pytree_node=False)  # Shape of the tensor.
+
+    @classmethod
+    def from_float_value(cls, fvalue, quantized_dtype, extract_diagonal=False):
+        if isinstance(fvalue, list) and not fvalue:
+            return QuantizedValue([], [], [], quantized_dtype, extract_diagonal, [])
+        quantized, diagonal_fvalue, bucket_size = QuantizedValue.quantize(
+            fvalue, quantized_dtype, extract_diagonal
+        )
+        return QuantizedValue(
+            quantized,
+            diagonal_fvalue,
+            bucket_size,
+            quantized_dtype,
+            extract_diagonal,
+            list(quantized.shape),
+        )
+
+    # Quantization is from Lingvo JAX optimizers.
+    # We extend it for int16 quantization of PSD matrices.
+    @classmethod
+    def quantize(cls, fvalue, quantized_dtype, extract_diagonal=False):
+        """Returns quantized value and the bucket."""
+        if quantized_dtype == jnp.float32:
+            return fvalue, [], []
+        elif quantized_dtype == jnp.bfloat16:
+            return fvalue.astype(jnp.bfloat16), [], []
+
+        float_dtype = fvalue.dtype
+        if quantized_dtype == jnp.int8:
+            # value -128 is not used.
+            num_buckets = jnp.array(127.0, dtype=float_dtype)
+        elif quantized_dtype == jnp.int16:
+            # value -32768 is not used.
+            num_buckets = jnp.array(32767.0, dtype=float_dtype)
+        else:
+            raise ValueError(f"Quantized dtype {quantized_dtype} not supported.")
+        # max value is mapped to num_buckets
+
+        if extract_diagonal and fvalue.ndim != 2:
+            raise ValueError(
+                f"Input array {fvalue} must be 2D to work with extract_diagonal."
+            )
+
+        diagonal_fvalue = []
+        if extract_diagonal:
+            diagonal_fvalue = jnp.diag(fvalue)
+            # Remove the diagonal entries.
+            fvalue = fvalue - jnp.diag(diagonal_fvalue)
+
+        # TODO(rohananil): Extend this by making use of information about the blocks
+        # SM3 style which will be useful for diagonal statistics
+        # We first decide the scale.
+        if fvalue.ndim < 1:
+            raise ValueError(
+                f"Input array {fvalue} must have a strictly positive number of "
+                "dimensions."
+            )
+
+        max_abs = jnp.max(jnp.abs(fvalue), axis=0)
+        bucket_size = max_abs / num_buckets
+        bs_expanded = bucket_size[jnp.newaxis, Ellipsis]
+        # To avoid divide by 0.0
+        bs_nonzero = jnp.where(
+            bs_expanded > 0.0, bs_expanded, jnp.ones_like(bs_expanded)
+        )
+        ratio = fvalue / bs_nonzero
+        # We use rounding to remove bias.
+        quantized = jnp.round(ratio)
+        return quantized.astype(quantized_dtype), diagonal_fvalue, bucket_size
+
+    def to_float(self):
+        """Returns the float value."""
+        if isinstance(self.quantized, list) and not self.quantized:
+            return self.quantized
+
+        if self.quantized_dtype == jnp.float32:
+            return self.quantized
+
+        if self.quantized_dtype == jnp.bfloat16:
+            return self.quantized.astype(jnp.float32)
+
+        float_dtype = self.bucket_size.dtype
+        bucket_size = self.bucket_size[jnp.newaxis, Ellipsis]
+        val = self.quantized.astype(float_dtype) * bucket_size
+        if self.extract_diagonal:
+            val += jnp.diag(self.diagonal)
+        return val

run_infer_notebook.sh

@@ -0,0 +1,2 @@
+#!/bin/bash
+jupyter notebook --ip 0.0.0.0 --no-browser --allow-root

sm3.py

@@ -0,0 +1,176 @@
+# coding=utf-8
+# Copyright 2022 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# An implementation of SM3 from:
+#
+# Memory-Efficient Adaptive Optimization, https://arxiv.org/pdf/1901.11150.pdf
+# Rohan Anil, Vineet Gupta, Tomer Koren, Yoram Singer
+#
+# Author: Rohan Anil (rohananil at google dot com)
+#
+
+"""SM3 Implementation."""
+
+import functools
+from typing import Any, NamedTuple
+
+import chex
+import jax
+import jax.numpy as jnp
+import optax
+
+from .quantization_utils import QuantizedValue
+
+
+class SM3State(NamedTuple):
+    count: chex.Array
+    stats: Any
+
+
+# Per parameter optimizer state used in data-parallel training.
+class ParameterStats(NamedTuple):
+    """State associated to each parameter of the model being trained."""
+
+    diagonal_statistics: chex.Array  # Accumulator for diagonal preconditioner
+    diagonal_momentum: QuantizedValue  # Momentum for the diagonal preconditioner
+
+
+def sm3(
+    learning_rate, beta1=0.9, beta2=0.999, diagonal_epsilon=1e-10, normalize_grads=False
+):
+    """SM3 optimizer.
+
+    Memory-Efficient Adaptive Optimization, Rohan Anil, Vineet Gupta, Tomer Koren,
+      Yoram Singer
+
+    https://arxiv.org/abs/1901.11150
+
+    Args:
+      learning_rate: the step size used to update the parameters.
+      beta1: momentum parameter.
+      beta2: second moment averaging parameter.
+      diagonal_epsilon: epsilon for sm3
+      normalize_grads: Whether to normalize grads. Author finds it useful when
+        grads are high variance.
+
+    Returns:
+      a GradientTransformation.
+    """
+
+    def _quantize_momentum(momentum_statistics):
+        return QuantizedValue.from_float_value(momentum_statistics, jnp.int8)
+
+    def init_fn(params):
+        """Initialise the optimiser's state."""
+
+        def _init(param):
+            accumulators = [jnp.zeros([s]) for s in param.shape]
+            momentum = _quantize_momentum(jnp.zeros_like(param))
+            return ParameterStats(accumulators, momentum)
+
+        return SM3State(
+            count=jnp.zeros([], jnp.int32), stats=jax.tree_map(_init, params)
+        )
+
+    def _get_expanded_shape(shape, i):
+        rank = len(shape)
+        # Replaces a `shape` of [M, N, K] with 1 in all dimensions except for i.
+        # For eg: i = 1 returns [1, N, 1].
+        return [1] * i + [shape[i]] + [1] * (rank - i - 1)
+
+    def _moving_averages(grad, accumulators):
+        w = (1.0 - beta2) if beta2 != 1.0 else 1.0
+        if grad.ndim < 2:
+            return beta2 * accumulators[0] + w * grad**2
+        else:
+            min_accumulator = functools.reduce(jnp.minimum, accumulators)
+            return beta2 * min_accumulator + w * grad**2
+
+    def _moving_averages_momentum(grad, momentum):
+        w = (1.0 - beta1) if beta1 != 1.0 else 1.0
+        return beta1 * momentum.to_float() + w * grad
+
+    def _sketch_diagonal_statistics(grad, updated_diagonal_statistics):
+        all_diagonal_statistics = []
+        for i in range(grad.ndim):
+            axes = list(range(i)) + list(range(i + 1, grad.ndim))
+            dim_diagonal_statistics = jnp.max(updated_diagonal_statistics, axis=axes)
+            all_diagonal_statistics.append(dim_diagonal_statistics)
+        if grad.ndim == 1:
+            all_diagonal_statistics[0] = updated_diagonal_statistics
+        return all_diagonal_statistics
+
+    def update_fn(updates, state, params=None):
+        del params
+        stats = state.stats
+        if normalize_grads:
+            updates = jax.tree_map(lambda g: g / (jnp.linalg.norm(g) + 1e-16), updates)
+        # Reshape all vectors into N-d tensors to compute min over them.
+        # [n], [m] -> [n, 1], [1, m]
+        expanded_diagonal_statistics = jax.tree_multimap(
+            lambda grad, state: [  # pylint:disable=g-long-lambda
+                jnp.reshape(
+                    state.diagonal_statistics[i], _get_expanded_shape(grad.shape, i)
+                )
+                for i in range(grad.ndim)
+            ],
+            updates,
+            stats,
+        )
+
+        # Compute new diagonal statistics
+        new_diagonal_statistics = jax.tree_multimap(
+            _moving_averages, updates, expanded_diagonal_statistics
+        )
+
+        # Compute preconditioners (1/sqrt(s)) where s is the statistics.
+        new_preconditioners = jax.tree_map(
+            lambda t: 1.0 / jnp.sqrt(t + diagonal_epsilon), new_diagonal_statistics
+        )
+        preconditioned_grads = jax.tree_multimap(
+            lambda g, p: g * p, updates, new_preconditioners
+        )
+
+        # Compute updated momentum (also handle quantization)
+        updated_momentum = jax.tree_multimap(
+            lambda preconditioned_grad, state: _moving_averages_momentum(  # pylint:disable=g-long-lambda
+                preconditioned_grad, state.diagonal_momentum
+            ),
+            preconditioned_grads,
+            stats,
+        )
+
+        # Update diagonal statistics.
+        updated_diagonal_statistics = jax.tree_multimap(
+            _sketch_diagonal_statistics, updates, new_diagonal_statistics
+        )
+
+        # Update momentum.
+        new_sm3_stats = jax.tree_multimap(
+            lambda momentum, diagonal_stats: ParameterStats(  # pylint:disable=g-long-lambda
+                diagonal_stats, _quantize_momentum(momentum)
+            ),
+            updated_momentum,
+            updated_diagonal_statistics,
+        )
+
+        lr = learning_rate
+        if callable(learning_rate):
+            lr = learning_rate(state.count)
+
+        new_updates = jax.tree_map(lambda pg: -lr * pg, updated_momentum)
+        return new_updates, SM3State(count=state.count + 1, stats=new_sm3_stats)
+
+    return optax.GradientTransformation(init_fn, update_fn)

style.yml

@@ -0,0 +1,20 @@
+name: Lint
+
+on:
+  push:
+    branches: [main]
+  pull_request:
+    branches: [main]
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - uses: psf/black@stable
+      - uses: actions/setup-python@v2
+        with:
+          python-version: 3.9
+      - name: Install requirements
+        run: pip install ".[dev]"
+      - uses: jamescurtin/isort-action@master

sweep.yaml

@@ -0,0 +1,49 @@
+program: train.py
+project: dalle-mini
+method: random
+metric:
+  name: eval/loss
+  goal: minimize
+parameters:
+  optim:
+    value: distributed_shampoo
+  learning_rate:
+    distribution: log_uniform
+    # from exp(min) to exp(max)
+    min: -9.2
+    max: -6.9
+  tokenizer_name:
+    value: boris/dalle-mini-tokenizer
+  config_name:
+    value: ./config/mini
+  dtype:
+    value: bfloat16
+  dataset_repo_or_path:
+    value: ./data
+  per_device_train_batch_size:
+    value: 64
+  per_device_eval_batch_size:
+    value: 64
+  gradient_accumulation_steps:
+    value: 1
+  warmup_steps:
+    value: 1000
+  num_train_epochs:
+    value: 1
+  max_train_samples:
+    value: 1000000
+  logging_steps:
+    value: 40
+  eval_steps:
+    value: 200
+
+command:
+  - python3
+  - ${program}
+  - "--streaming"
+  - "--output_dir"
+  - "./output"
+  - "--overwrite_output_dir"
+  - "--do_train"
+  - "--do_eval"
+  - ${args}

symmetric_matrices.py

@@ -0,0 +1,442 @@
+# coding=utf-8
+# Copyright 2022 The Google Research Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+"""JAX Ops for symmetric matrices used by the Shampoo optimizer."""
+
+import functools
+from typing import Any, List, Optional, Sequence, Union
+
+import jax
+import jax.numpy as jnp
+import numpy as np
+from flax import struct
+from jax import lax
+
+
+@struct.dataclass
+class SlicedSymmetricMatrix:
+    """A symmetric matrix represented by lower-triangular block row slices.
+
+    For example, the symmetric matrix M = [[a, b^T], [b, c]] would be represented
+    by the block rows a and [b, c].
+
+    The matrix may be batched, in which case each entry of block_rows may have
+    dimension greater than 2. The last two dimensions represent the rows and cols.
+    """
+
+    block_rows: List[jnp.ndarray]
+
+
+def product_with_transpose(
+    mat1,
+    mat2,
+    axes,
+    precision=lax.Precision.DEFAULT,
+):
+    """Returns mat1 * mat2^T for two matrices (possibly batched).
+
+    The rows and columns are the last two dimensions for each matrix.
+
+    Args:
+      mat1: First matrix.
+      mat2: Second matrix.
+      axes: The axes over which to apply the product.
+      precision: JAX precision to use for the multiplication.
+    """
+    return jnp.tensordot(a=mat1, b=mat2, axes=axes, precision=precision)
+
+
+@functools.partial(jax.jit, static_argnames=("block_size", "axes", "precision"))
+def sliced_transposed_product(
+    mat,
+    block_size,
+    axes=(-1,),
+    precision=lax.Precision.DEFAULT,
+):
+    """Returns the blocked slices representing a symmetric contraction.
+
+    Specifically, the output is a contraction of the input mat with itself, in the
+    specified axes.
+
+    Args:
+      mat: The matrix for which we will compute a contraction with itself.
+      block_size: The size of row blocks to compute.
+      axes: Axes to use for the contraction.
+      precision: The precision to use in each computation.
+
+    Raises:
+      ValueError: Raised when the specified block size does not evenly divide
+        the number of rows of the input mat.
+    """
+    rank = len(mat.shape)
+
+    def _make_axis_positive(ax):
+        assert -rank <= ax < rank
+        return ax + rank if ax < 0 else ax
+
+    positive_axes = [_make_axis_positive(ax) for ax in axes]
+    assert len(positive_axes) == len(axes)
+    remaining_axes = set(range(rank)) - set(positive_axes)
+    assert len(remaining_axes) == 1
+    remaining_ax = remaining_axes.pop()
+
+    num_rows = mat.shape[remaining_ax]
+    if num_rows % block_size != 0:
+        raise ValueError(
+            "The row dimension must be divisible by block_size. "
+            f"Instead got row dimension={num_rows} and block_size={block_size}."
+        )
+
+    block_rows = []
+    for i in range(num_rows // block_size):
+        start_indices = [0] * rank
+        start_indices[remaining_ax] = i * block_size
+
+        slice_sizes = list(mat.shape)
+        slice_sizes[remaining_ax] = block_size
+
+        slice_sizes_full = list(mat.shape)
+        slice_sizes_full[remaining_ax] = (i + 1) * block_size
+
+        block_rows.append(
+            product_with_transpose(
+                lax.dynamic_slice(
+                    mat, start_indices=start_indices, slice_sizes=slice_sizes
+                ),
+                lax.dynamic_slice(
+                    mat, start_indices=[0] * rank, slice_sizes=slice_sizes_full
+                ),
+                axes=(axes, axes),
+                precision=precision,
+            )
+        )
+
+    return SlicedSymmetricMatrix(block_rows=block_rows)
+
+
+@functools.partial(jax.jit, static_argnames=("block_size", "axes", "precision"))
+def sliced_transposed_product_concat(
+    mat,
+    block_size,
+    axes=(-1,),
+    precision=lax.Precision.DEFAULT,
+):
+    """Returns the concatenated slices representing mat*mat^T.
+
+    Args:
+      mat: The matrix for which we will compute mat*mat^T. It does not need to be
+        square, and may be batched.
+      block_size: The size of row blocks to compute.
+      axes: Axes to use for the contraction.
+      precision: The precision to use in each computation.
+
+    Raises:
+      ValueError: Raised when the specified block size does not evenly divide
+        the number of rows of the input mat.
+    """
+    sliced_symmetric_matrix = sliced_transposed_product(
+        mat=mat, block_size=block_size, axes=axes, precision=precision
+    )
+    return jnp.concatenate(sliced_symmetric_matrix.block_rows, axis=-1)
+
+
+@jax.jit
+def materialize_matrix(symmetric_matrix):
+    """Returns a materialized symmetric matrix.
+
+    Args:
+      symmetric_matrix: the matrix represented by lower-triangular block slices.
+    """
+    block_rows = symmetric_matrix.block_rows
+    block_size = block_rows[0].shape[-2]
+    num_blocks = len(block_rows)
+
+    # Slice the lower-triangular and diagonal blocks into blocks.
+    blocks = [
+        [
+            block_row[Ellipsis, i * block_size : (i + 1) * block_size]
+            for i in range(k + 1)
+        ]
+        for k, block_row in enumerate(block_rows)
+    ]
+
+    # Generate the (off-diagonal) upper-triangular blocks.
+    off_diags = [[] for _ in range(num_blocks - 1)]
+    for k, block_row in enumerate(block_rows[1:]):
+        for i in range(k + 1):
+            off_diags[i].append(
+                jnp.swapaxes(
+                    a=block_row[Ellipsis, i * block_size : (i + 1) * block_size],
+                    axis1=-1,
+                    axis2=-2,
+                )
+            )
+
+    return jnp.block(
+        [row + row_t for row, row_t in zip(blocks[:-1], off_diags)] + [blocks[-1]]
+    )
+
+
+@functools.partial(jax.jit, static_argnames=("num_blocks"))
+def materialize_matrix_from_concat(
+    block_rows_concat,
+    num_blocks=None,
+):
+    """Returns a materialized symmetric matrix from concatenated slices.
+
+    Args:
+      block_rows_concat: The matrix represented as the concatenated
+        lower-triangular blocks.
+      num_blocks: The number of block-rows used to represent the symmetric matrix.
+        If not specified, it is inferred from the shape of block_rows_concat.
+    """
+    if num_blocks is None:
+        num_blocks = find_num_blocks(block_rows_concat)
+
+    block_size = block_rows_concat.shape[-2]
+
+    block_rows = [
+        block_rows_concat[
+            Ellipsis,
+            (k * (k + 1))
+            // 2
+            * block_size : (((k + 1) * (k + 2)) // 2 + 1)
+            * block_size,
+        ]
+        for k in range(num_blocks)
+    ]
+
+    return materialize_matrix(SlicedSymmetricMatrix(block_rows=block_rows))
+
+
+@functools.partial(jax.jit, static_argnames=("alpha", "beta", "axes"))
+def update_sliced_rows(
+    symmetric_matrix,
+    mat,
+    alpha,
+    beta,
+    axes=(-1,),
+):
+    """Implements the blocked equivalent of SYRK.
+
+    Specifically, the symmetric matrix (represented using lower-triangular block
+    rows) is updated using the sliced product of mat.
+
+    Args:
+      symmetric_matrix: The symmetric matrix to update.
+      mat: The matrix to use for the update = mat * mat^T. The number of rows
+        should match that of symmetric_matrix.
+      alpha: The weight for the update.
+      beta: The weight for the original symmetric matrix.
+      axes: Axes to use for the contraction of the update.
+
+    Returns:
+      The updated rows of alpha * mat * mat^T + beta * symmetric_matrix.
+    """
+    block_size = symmetric_matrix.block_rows[0].shape[-2]
+    sym_prod = sliced_transposed_product(mat=mat, block_size=block_size, axes=axes)
+    return SlicedSymmetricMatrix(
+        block_rows=[
+            update * alpha + row * beta
+            for update, row in zip(sym_prod.block_rows, symmetric_matrix.block_rows)
+        ]
+    )
+
+
+def num_blocks_from_total_blocks(total_blocks):
+    """Returns the number of blocks (i.e.
+
+    block rows) from the total blocks.
+
+    This is the inverse of the function x -> x*(x+1)/2.
+
+    For example, the matrix M = [[A, B^T], [B, C]] may be represented using a
+    total of 3 blocks ([A, B, C]). The number of corresponding block rows is 2.
+
+    Args:
+      total_blocks: The total blocks used to represent the matrix.
+    """
+    num_blocks = np.round((np.sqrt(8 * total_blocks + 1) - 1) / 2).astype(np.int32)
+    if (num_blocks * (num_blocks + 1)) / 2 != total_blocks:
+        raise ValueError(
+            f"total_blocks={total_blocks} does not correspond to "
+            "a symmetric matrix. It must have the form total_blocks = x*(x+1)/2."
+        )
+    return num_blocks
+
+
+def find_num_blocks(block_rows_concat):
+    """Returns the number of (row) blocks representing the concatenated matrix.
+
+    For example, an input with dimensions [256, 2560] represents 10 square blocks,
+    which matches 4 lower-triangular block rows (1+2+3+4). So this function will
+    return 4.
+
+    Use ordinary numpy functions here so that the returned value is static.
+
+    Args:
+      block_rows_concat: The concatenated block array.
+
+    Raises:
+      ValueError: When the dimensions of the matrix do not correspond to a lower
+      triangular block representation.
+    """
+    # Compute the number of square blocks used to represent the matrix.
+    total_blocks = block_rows_concat.shape[-1] / block_rows_concat.shape[-2]
+    # Determine the number of block rows by inverting y = x*(x+1)/2.
+    return num_blocks_from_total_blocks(total_blocks)
+
+
+@functools.partial(jax.jit, static_argnames=("block_size"))
+def slice_symmetric_matrix(
+    mat,
+    block_size,
+):
+    """Returns sliced row blocks.
+
+    Args:
+      mat: A symmetric matrix.
+      block_size: The size of the row slices.
+    """
+    num_rows = mat.shape[-2]
+    num_cols = mat.shape[-1]
+    if num_rows != num_cols:
+        raise ValueError("mat is not square.")
+    if num_rows % block_size != 0:
+        raise ValueError(
+            "block size does not evenly divide rows. "
+            f"num_rows={num_rows}, block_size={block_size}"
+        )
+    return SlicedSymmetricMatrix(
+        block_rows=[
+            mat[
+                Ellipsis,
+                i * block_size : (i + 1) * block_size,
+                0 : (i + 1) * block_size,
+            ]
+            for i in range(num_rows // block_size)
+        ]
+    )
+
+
+@functools.partial(jax.jit, static_argnames=("block_size"))
+def slice_symmetric_matrix_concat(
+    mat,
+    block_size,
+):
+    """Returns the concatenated sliced row blocks.
+
+    Args:
+      mat: A symmetric matrix.
+      block_size: The size of the row slices.
+    """
+    sliced_symmetric_matrix = slice_symmetric_matrix(mat=mat, block_size=block_size)
+    return jnp.concatenate(sliced_symmetric_matrix.block_rows, axis=-1)
+
+
+def sliced_matrix_diag(mat):
+    """Returns the diagonal of the symmetric matrix.
+
+    Args:
+      mat: The symmetric matrix represented in concatenated block form.
+    """
+    rows, cols = mat.shape
+    total_blocks = cols // rows
+    num_blocks = num_blocks_from_total_blocks(total_blocks)
+    diags = []
+    for i in range(num_blocks):
+        last_index = rows * ((i + 2) * (i + 1)) // 2
+        first_index = last_index - rows
+        diags.append(jnp.diag(mat[Ellipsis, first_index:last_index]))
+    return jnp.concatenate(diags, axis=-1)
+
+
+def diag_as_concat(diag, block_size):
+    """Returns the representation of a diagonal matrix in symmetric block form.
+
+    Args:
+      diag: The 1D array for the diagonals.
+      block_size: The size of blocks to use. Must divide the length of diag.
+    """
+    assert len(diag.shape) == 1  # diag must be 1D.
+    assert len(diag) % block_size == 0
+    num_diag_blocks = len(diag) // block_size
+    blocks = []
+    for i in range(num_diag_blocks):
+        blocks.append(jnp.zeros(shape=(block_size, block_size * i), dtype=diag.dtype))
+        blocks.append(jnp.diag(diag[i * block_size : (i + 1) * block_size]))
+    return jnp.concatenate(blocks, axis=-1)
+
+
+def row_abs_maxes(mat):
+    """Returns the max of the absolute values of the rows of the full matrix.
+
+    For example the symmetric matrix M = [[1, 6], [6, 2]] is represented using
+    mat = [1, 6, 2] with block_size = 1. In this case the function returns the
+    absolute row maxes of the original symmetric matrix, [6, 6].
+
+    Args:
+      mat: The symmetric matrix represented as the concatenated blocks.
+    """
+    rows, cols = mat.shape
+
+    # Find col and row max for each block.
+    col_maxes = []
+    row_maxes = []
+    for i in range(cols // rows):
+        block = jnp.abs(mat[Ellipsis, i * rows : (i + 1) * rows])
+        col_maxes.append(jnp.max(block, axis=1))
+        row_maxes.append(jnp.max(block, axis=0))
+
+    # global row max from block maxes.
+    num_blocks = num_blocks_from_total_blocks(cols // rows)
+    maxes = []
+    for i in range(num_blocks):
+        maxes.append(
+            jnp.concatenate(
+                row_maxes[(i * (i + 1) // 2) : ((i + 2) * (i + 1) // 2)]
+                + [
+                    col_maxes[((j + 1) * (j + 2)) // 2 - (j - i + 1)]
+                    for j in range(i + 1, num_blocks)
+                ],
+                axis=-1,
+            )
+        )
+
+    return jnp.max(jnp.stack(maxes), axis=0)
+
+
+def times_vector(mat, vec):
+    """Returns the symmetric block-concatenated matrix multiplied by a vector.
+
+    Specifically, each value in the vector is multiplied by a row of the full
+    matrix. That is, the vector is broadcast and multiplied element-wise. Note
+    this would be the transpose of full_mat * vec if full_mat represented the full
+    symmetric matrix.
+
+    Args:
+      mat: The symmetric matrix represented as the concatenated blocks.
+      vec: The vector, having the same dimension as the materialized matrix.
+    """
+    rows, cols = mat.shape
+    num_blocks = num_blocks_from_total_blocks(cols // rows)
+    multiplied = []
+    for i in range(num_blocks):
+        mat_block = mat[
+            Ellipsis, rows * ((i + 1) * i) // 2 : rows * ((i + 1) * (i + 2)) // 2
+        ]
+        vec_block = vec[Ellipsis, rows * i : rows * (i + 1)]
+        multiplied.append(jnp.einsum("...ij,...i->ij", mat_block, vec_block))
+    return jnp.concatenate(multiplied, axis=-1)

sync_to_hub.yml.backup

@@ -0,0 +1,20 @@
+name: Sync to Hugging Face hub - Obsolete to avoid app disruptions
+
+on:
+  push:
+    branches: [main]
+
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push https://boris:$HF_TOKEN@huggingface.co/spaces/dalle-mini/dalle-mini main

sync_to_hub_debug.yml

@@ -0,0 +1,17 @@
+name: Deploy to debug app
+
+on:
+  # to run this workflow manually from the Actions tab
+  workflow_dispatch:
+
+jobs:
+  sync-to-hub-debug:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+        with:
+          fetch-depth: 0
+      - name: Push to hub
+        env:
+          HF_TOKEN: ${{ secrets.HF_TOKEN }}
+        run: git push --force https://boris:$HF_TOKEN@huggingface.co/spaces/dalle-mini/dalle-mini-debug +HEAD:main

text.py

@@ -0,0 +1,262 @@
+"""
+Utilities for processing text.
+"""
+
+import html
+import math
+import random
+import re
+from pathlib import Path
+
+import emoji
+import ftfy
+from huggingface_hub import hf_hub_download
+from unidecode import unidecode
+
+# based on wiki word occurrence
+person_token = [("a person", 282265), ("someone", 121194), ("somebody", 12219)]
+temp_token = "xtokx"  # avoid repeating chars
+
+
+class HashtagProcessor:
+    # Adapted from wordninja library
+    # We use our wikipedia word count + a good heuristic to make it work
+    def __init__(self):
+        wiki_word_frequency = hf_hub_download(
+            "dalle-mini/dalle-mini", filename="enwiki-words-frequency.txt"
+        )
+        self._word_cost = (
+            l.split()[0]
+            for l in Path(wiki_word_frequency).read_text(encoding="utf8").splitlines()
+        )
+        self._word_cost = {
+            str(k): math.log(float(i + 1)) for i, k in enumerate(self._word_cost)
+        }
+        self._max_word = max(len(x) for x in self._word_cost.keys())
+        self._SPLIT_RE = re.compile("[^a-zA-Z0-9']+")
+
+    def __call__(self, s):
+        """Uses dynamic programming to infer the location of spaces in a string without spaces."""
+        l = [self._split(x) for x in self._SPLIT_RE.split(s)]
+        return " ".join([item for sublist in l for item in sublist])
+
+    def _split(self, s):
+        # Find the best match for the i first characters, assuming cost has
+        # been built for the i-1 first characters.
+        # Returns a pair (match_cost, match_length).
+        def best_match(i):
+            candidates = enumerate(reversed(cost[max(0, i - self._max_word) : i]))
+            return min(
+                (c + self._word_cost.get(s[i - k - 1 : i].lower(), 9e999), k + 1)
+                for k, c in candidates
+            )
+
+        # Build the cost array
+        cost = [0]
+        for i in range(1, len(s) + 1):
+            c, k = best_match(i)
+            cost.append(c)
+
+        # Backtrack to recover the minimal-cost string.
+        out = []
+        i = len(s)
+        while i > 0:
+            c, k = best_match(i)
+            assert c == cost[i]
+            newToken = True
+            if not s[i - k : i] == "'":  # ignore a lone apostrophe
+                if len(out) > 0:
+                    # re-attach split 's and split digits
+                    if out[-1] == "'s" or (
+                        s[i - 1].isdigit() and out[-1][0].isdigit()
+                    ):  # digit followed by digit
+                        out[-1] = (
+                            s[i - k : i] + out[-1]
+                        )  # combine current token with previous token
+                        newToken = False
+
+            if newToken:
+                out.append(s[i - k : i])
+
+            i -= k
+
+        return reversed(out)
+
+
+def replace_person_token(t):
+    "Used for CC12M"
+    t = re.sub("<person>([,\s]*(and)*[,\s]*<person>)+", " people ", t)
+    while "<person>" in t:
+        t = t.replace(
+            "<person>", f" {random.choices(*tuple(zip(*person_token)))[0]} ", 1
+        )
+    return t
+
+
+def fix_html(t):
+    # from OpenAI CLIP
+    return html.unescape(html.unescape(t))
+
+
+def replace_punctuation_with_commas(t):
+    return re.sub("[()[\].,|:;?!=+~\-\/{}]", ",", t)
+
+
+def simplify_quotes(t):
+    return re.sub("""['"`]""", ' " ', t)
+
+
+def merge_quotes(t):
+    return re.sub('(\s*"+\s*)+', ' " ', t)
+
+
+def remove_comma_numbers(t):
+    def _f(t):
+        return re.sub("(\d),(\d{3})", r"\1\2", t)
+
+    return _f(_f(t))
+
+
+def pre_process_dot_numbers(t):
+    return re.sub("(\w)\.(\w)", rf"\1{temp_token}dot{temp_token}\2", t)
+
+
+def post_process_dot_numbers(t):
+    return re.sub(f"{temp_token}dot{temp_token}", ".", t)
+
+
+def pre_process_quotes(t):
+    # allows quotes only for 's, 't, 'd, 'm, 'll, 're, 've
+    return re.sub(
+        r"'(?=([stdm]|(ll)|(re)|(ve)|(ll))\b)", rf"{temp_token}quote{temp_token}", t
+    )
+
+
+def post_process_quotes(t):
+    return re.sub(f"{temp_token}quote{temp_token}", "'", t)
+
+
+def pre_process_dates(t):
+    return re.sub("(\d)/(\d)", rf"\1{temp_token}slash{temp_token}\2", t)
+
+
+def post_process_dates(t):
+    return re.sub(f"{temp_token}slash{temp_token}", "/", t)
+
+
+def merge_commas(t):
+    return re.sub("(\s*,+\s*)+", ", ", t)
+
+
+def add_space_after_commas(t):
+    return re.sub(",", ", ", t)
+
+
+def handle_special_chars(t):
+    "Handle special characters"
+    # replace "-" with a space when between words without space
+    t = re.sub("(\w)-(\w)", r"\1 \2", t)
+    # always add space around some characters
+    return re.sub("([%&\/$*])", r" \1 ", t)
+
+
+def expand_hashtags(t, hashtag_processor):
+    "Remove # and try to split words"
+    return re.sub("#(\w+)", lambda m: hashtag_processor(m.group(1)), t)
+
+
+_re_ignore_chars = r"[_#\\]"
+
+
+def ignore_chars(t):
+    "Ignore useless characters"
+    return re.sub(_re_ignore_chars, " ", t)
+
+
+def remove_extra_spaces(t):
+    "Remove extra spaces (including \t and \n)"
+    return re.sub("\s+", " ", t)
+
+
+def remove_repeating_chars(t):
+    "If the same character is present 4+ times (not 3 because of roman 'VIII'), replace with single instance"
+    return re.sub(r"(\D)(\1{3,})", r"\1", t)
+
+
+def remove_urls(t):
+    return re.sub(r"http\S+", "", t)
+
+
+def remove_html_tags(t):
+    return re.sub("<[^<]+?>", " ", t)
+
+
+def remove_first_last_commas(t):
+    t = t.strip()
+    t = t[:-1] if t and t[-1] == "," else t
+    t = t[1:] if t and t[0] == "," else t
+    return t.strip()
+
+
+def remove_wiki_ref(t):
+    t = re.sub(r"\A\s*\[\d+\]", "", t)
+    return re.sub(r"\[\d+\]\s*\Z", "", t)
+
+
+class TextNormalizer:
+    "Normalize text"
+
+    def __init__(self):
+        self._hashtag_processor = HashtagProcessor()
+
+    def __call__(self, t):
+        # fix some characters
+        t = ftfy.fix_text(t)
+        # fix html
+        t = fix_html(t)
+        # decode emojis (would be removed by unidecode)
+        t = emoji.demojize(t)
+        # decode and simplify text: see unidecode library
+        t = unidecode(t)
+        # lower case
+        t = t.lower()
+        # replace <PERSON> (for CC12M)
+        t = replace_person_token(t)
+        # remove wiki reference (for WIT)
+        t = remove_wiki_ref(t)
+        # remove html tags
+        t = remove_html_tags(t)
+        # remove urls
+        t = remove_urls(t)
+        # remove commas in numbers
+        t = remove_comma_numbers(t)
+        # handle dots in numbers and quotes - Part 1
+        t = pre_process_dot_numbers(t)
+        t = pre_process_quotes(t)
+        t = pre_process_dates(t)
+        # handle special characters
+        t = handle_special_chars(t)
+        # handle hashtags
+        t = expand_hashtags(t, self._hashtag_processor)
+        # ignore useless characters
+        t = ignore_chars(t)
+        # simplify quotes
+        t = simplify_quotes(t)
+        # all punctuation becomes commas
+        t = replace_punctuation_with_commas(t)
+        # handle dots in numbers and quotes - Part 2
+        t = post_process_dot_numbers(t)
+        t = post_process_quotes(t)
+        t = post_process_dates(t)
+        # handle repeating characters
+        t = remove_repeating_chars(t)
+        # merge quotes
+        t = merge_quotes(t)
+        # merge commas
+        t = merge_commas(t)
+        # remove multiple spaces
+        t = remove_extra_spaces(t)
+        # remove first and last comma
+        t = remove_first_last_commas(t)
+        # always start with a space
+        return f" {t}"

tokenizer.py

@@ -0,0 +1,8 @@
+""" DalleBart tokenizer """
+from transformers import BartTokenizerFast
+
+from .utils import PretrainedFromWandbMixin
+
+
+class DalleBartTokenizer(PretrainedFromWandbMixin, BartTokenizerFast):
+    pass

train.py

@@ -0,0 +1,1664 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2021-2022 The HuggingFace & DALL·E Mini team. All rights reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""
+Training DALL·E Mini.
+Script adapted from run_summarization_flax.py
+"""
+
+import io
+import logging
+import os
+import sys
+import tempfile
+import time
+from dataclasses import asdict, dataclass, field
+from pathlib import Path
+from typing import Any, Callable, NamedTuple, Optional
+
+import datasets
+import flax
+import jax
+import jax.numpy as jnp
+import jaxlib
+import numpy as np
+import optax
+import transformers
+import wandb
+from datasets import Dataset
+from flax import core, struct, traverse_util
+from flax.core.frozen_dict import FrozenDict, freeze, unfreeze
+from flax.serialization import from_bytes, to_bytes
+from flax.training.common_utils import onehot
+from jax.experimental import PartitionSpec, maps
+from jax.experimental.compilation_cache import compilation_cache as cc
+from jax.experimental.pjit import pjit, with_sharding_constraint
+from scalable_shampoo.distributed_shampoo import GraftingType, distributed_shampoo
+from tqdm import tqdm
+from transformers import HfArgumentParser
+
+import dalle_mini
+from dalle_mini.data import Dataset
+from dalle_mini.model import (
+    DalleBart,
+    DalleBartConfig,
+    DalleBartTokenizer,
+    set_partitions,
+)
+
+try:
+    from google.cloud import storage
+except:
+    storage = None
+
+cc.initialize_cache("./jax_cache", max_cache_size_bytes=10 * 2**30)
+
+logger = logging.getLogger(__name__)
+
+
+@dataclass
+class ModelArguments:
+    """
+    Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
+    """
+
+    model_name_or_path: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The model checkpoint for weights initialization. "
+            "Don't set if you want to train a model from scratch. "
+            "W&B artifact references are supported in addition to the sources supported by `PreTrainedModel`."
+        },
+    )
+    config_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Pretrained config name or path if not the same as model_name_or_path"
+        },
+    )
+    tokenizer_name: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Pretrained tokenizer name or path if not the same as model_name_or_path"
+        },
+    )
+    dtype: Optional[str] = field(
+        default="float32",
+        metadata={
+            "help": "Floating-point format in which the computations will be performed (not the model weights). Choose one of `[float32, float16, bfloat16]`."
+        },
+    )
+    restore_state: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Restore optimizer and training state. Can be True (will retrieve associated wandb artifact), a local directory or a Google bucket path."
+        },
+    )
+    dropout: Optional[float] = field(
+        default=None,
+        metadata={"help": "Dropout rate. Overwrites config."},
+    )
+    activation_dropout: Optional[float] = field(
+        default=None,
+        metadata={"help": "Activation dropout rate. Overwrites config."},
+    )
+    attention_dropout: Optional[float] = field(
+        default=None,
+        metadata={"help": "Attention dropout rate. Overwrites config."},
+    )
+
+    def __post_init__(self):
+        if self.tokenizer_name is None:
+            self.tokenizer_name = self.model_name_or_path
+            assert (
+                self.tokenizer_name is not None
+            ), "Tokenizer name or model name/path needs to be specified"
+        if self.restore_state:
+            assert self.model_name_or_path is not None and (
+                "/model-" in self.model_name_or_path
+            ), "Restoring state only available with W&B artifact reference"
+
+    def get_metadata(self):
+        if self.model_name_or_path is not None and ":" in self.model_name_or_path:
+            if jax.process_index() == 0:
+                artifact = wandb.run.use_artifact(self.model_name_or_path)
+            else:
+                artifact = wandb.Api().artifact(self.model_name_or_path)
+            return artifact.metadata
+        else:
+            return dict()
+
+    def get_opt_state(self):
+        with tempfile.TemporaryDirectory() as tmp_dir:  # avoid multiple artifact copies
+            if self.restore_state is True:
+                # wandb artifact
+                state_artifact = self.model_name_or_path.replace(
+                    "/model-", "/state-", 1
+                )
+                if jax.process_index() == 0:
+                    artifact = wandb.run.use_artifact(state_artifact)
+                else:
+                    artifact = wandb.Api().artifact(state_artifact)
+                if artifact.metadata.get("bucket_path"):
+                    # we will read directly file contents
+                    self.restore_state = artifact.metadata["bucket_path"]
+                else:
+                    artifact_dir = artifact.download(tmp_dir)
+                    self.restore_state = str(Path(artifact_dir) / "opt_state.msgpack")
+
+            if self.restore_state.startswith("gs://"):
+                bucket_path = Path(self.restore_state[5:]) / "opt_state.msgpack"
+                bucket, blob_name = str(bucket_path).split("/", 1)
+                assert (
+                    storage is not None
+                ), 'Could not find google.storage. Install with "pip install google-cloud-storage"'
+                client = storage.Client()
+                bucket = client.bucket(bucket)
+                blob = bucket.blob(blob_name)
+                return blob.download_as_bytes()
+
+            with Path(self.restore_state).open("rb") as f:
+                return f.read()
+
+
+@dataclass
+class DataTrainingArguments:
+    """
+    Arguments pertaining to what data we are going to input our model for training and eval.
+    """
+
+    text_column: Optional[str] = field(
+        default="caption",
+        metadata={
+            "help": "The name of the column in the datasets containing the full texts (for summarization)."
+        },
+    )
+    encoding_column: Optional[str] = field(
+        default="encoding",
+        metadata={
+            "help": "The name of the column in the datasets containing the image encodings."
+        },
+    )
+    dataset_repo_or_path: str = field(
+        default=None,
+        metadata={"help": "The dataset repository containing encoded files."},
+    )
+    train_file: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "The input training data file (glob & braceexpand acceptable)."
+        },
+    )
+    validation_file: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "An optional input evaluation data file (glob & braceexpand acceptable)."
+        },
+    )
+    # data loading should not be a bottleneck so we use "streaming" mode by default
+    streaming: Optional[bool] = field(
+        default=True,
+        metadata={"help": "Whether to stream the dataset."},
+    )
+    use_auth_token: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to use the authentication token for private datasets."
+        },
+    )
+    shard_by_host: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to shard data files by host in multi-host environments."
+        },
+    )
+    blank_caption_prob: Optional[float] = field(
+        default=0.0,
+        metadata={
+            "help": "Probability of removing some captions for classifier-free guidance."
+        },
+    )
+    clip_score_column: Optional[str] = field(
+        default="clip_score",
+        metadata={"help": "Column that containts clip score for filtering."},
+    )
+    min_clip_score: Optional[float] = field(
+        default=None,
+        metadata={"help": "Minimum clip score required."},
+    )
+    max_clip_score: Optional[float] = field(
+        default=None,
+        metadata={"help": "Maximum clip score required."},
+    )
+    filter_column: Optional[str] = field(
+        default=None,
+        metadata={"help": "Column that containts classes to be filtered."},
+    )
+    filter_value: Optional[str] = field(
+        default=None,
+        metadata={"help": "Class value to be kept during filtering."},
+    )
+    multi_eval_ds: Optional[bool] = field(
+        default=False,
+        metadata={
+            "help": "Whether to look for multiple validation datasets (local support only)."
+        },
+    )
+    max_train_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of training examples."
+        },
+    )
+    max_eval_samples: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "For debugging purposes or quicker training, truncate the number of evaluation examples."
+        },
+    )
+    preprocessing_num_workers: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "The number of processes to use for the preprocessing. Not used in streaming mode."
+        },
+    )
+    overwrite_cache: bool = field(
+        default=False,
+        metadata={
+            "help": "Overwrite the cached training and evaluation sets. Not used in streaming mode."
+        },
+    )
+    # default seed of None ensures we don't repeat the same items if script was interrupted during an epoch
+    seed_dataset: int = field(
+        default=None,
+        metadata={
+            "help": "Random seed for the dataset that will be set at the beginning of training."
+        },
+    )
+
+    def __post_init__(self):
+        if self.dataset_repo_or_path is None:
+            raise ValueError("Need a dataset repository or path.")
+
+
+@dataclass
+class TrainingArguments:
+    """
+    Arguments pertaining to training parameters.
+    """
+
+    output_dir: str = field(
+        metadata={
+            "help": "The output directory where the model predictions and checkpoints will be written."
+        },
+    )
+    overwrite_output_dir: bool = field(
+        default=False,
+        metadata={
+            "help": (
+                "Overwrite the content of the output directory. "
+                "Use this to continue training if output_dir points to a checkpoint directory."
+            )
+        },
+    )
+
+    do_train: bool = field(default=False, metadata={"help": "Whether to run training."})
+    do_eval: bool = field(
+        default=False, metadata={"help": "Whether to run eval on the validation set."}
+    )
+
+    per_device_train_batch_size: int = field(
+        default=8,
+        metadata={"help": "Batch size per data parallel device for training."},
+    )
+    per_device_eval_batch_size: Optional[int] = field(
+        default=None,
+        metadata={
+            "help": "Batch size per data parallel device for evaluation. Same as training batch size if not set."
+        },
+    )
+
+    gradient_accumulation_steps: int = field(
+        default=1,
+        metadata={
+            "help": "Number of updates steps to accumulate before performing an update pass."
+        },
+    )
+    gradient_checkpointing: bool = field(
+        default=False, metadata={"help": "Use gradient checkpointing."}
+    )
+
+    learning_rate: float = field(
+        default=5e-5, metadata={"help": "The initial learning rate."}
+    )
+    optim: str = field(
+        default="distributed_shampoo",
+        metadata={
+            "help": 'The optimizer to use. Can be "distributed_shampoo" (default), "adam" or "adafactor"'
+        },
+    )
+    weight_decay: float = field(
+        default=0.0, metadata={"help": "Weight decay applied to parameters."}
+    )
+    beta1: float = field(
+        default=0.9,
+        metadata={"help": "Beta1 for Adam & Distributed Shampoo."},
+    )
+    beta2: float = field(
+        default=0.999,
+        metadata={"help": "Beta2 for for Adam & Distributed Shampoo."},
+    )
+    adam_epsilon: float = field(
+        default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}
+    )
+    max_grad_norm: float = field(
+        default=1.0, metadata={"help": "Max gradient norm for Adafactor."}
+    )
+    block_size: int = field(
+        default=1024,
+        metadata={"help": "Chunked size for large layers with Distributed Shampoo."},
+    )
+    preconditioning_compute_steps: int = field(
+        default=10, metadata={"help": "Number of steps to update preconditioner."}
+    )
+    skip_preconditioning_dim_size_gt: int = field(
+        default=4096,
+        metadata={"help": "Max size for preconditioning with Distributed Shampoo."},
+    )
+    graft_type: str = field(
+        default="rmsprop_normalized",
+        metadata={
+            "help": "The type of grafting to use. Can be 'rmsprop_normalized' (default), 'rmsprop', 'adagrad', 'adagrad_normalized', 'sgd' or 'sqrt_n'"
+        },
+    )
+    nesterov: bool = field(
+        default=False,
+        metadata={"help": "Use Nesterov momentum for Distributed Shampoo."},
+    )
+    optim_quantized: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to quantize optimizer (only supported with Distributed Shampoo)."
+        },
+    )
+    shard_shampoo_across: str = field(
+        default="dp",
+        metadata={
+            "help": "Whether to shard the optimizer across data devices (dp), model devices (mp) or both (2d)."
+        },
+    )
+
+    num_train_epochs: int = field(
+        default=3, metadata={"help": "Total number of training epochs to perform."}
+    )
+
+    warmup_steps: int = field(
+        default=0, metadata={"help": "Linear warmup over warmup_steps."}
+    )
+    lr_decay: str = field(
+        default=None,
+        metadata={
+            "help": "Decay to be used in the learning rate scheduler. Can be None (default), linear or exponential."
+        },
+    )
+    lr_transition_steps: int = field(
+        default=None,
+        metadata={
+            "help": "Number of transition steps associated with learning rate decay when using exponential decay."
+        },
+    )
+    lr_decay_rate: float = field(
+        default=None,
+        metadata={
+            "help": "Decay rate associated with learning rate when using exponential decay."
+        },
+    )
+    lr_staircase: bool = field(
+        default=False,
+        metadata={
+            "help": "Whether to use staircase or continuous learning rate when using exponential decay."
+        },
+    )
+    lr_offset: int = field(
+        default=0,
+        metadata={"help": "Number of steps to offset learning rate and keep it at 0."},
+    )
+    logging_steps: int = field(
+        default=40, metadata={"help": "Log every X updates steps."}
+    )
+    eval_steps: int = field(
+        default=400, metadata={"help": "Run an evaluation every X steps."}
+    )
+    save_steps: int = field(
+        default=4000, metadata={"help": "Save checkpoint every X updates steps."}
+    )
+    log_model: bool = field(
+        default=False,
+        metadata={"help": "Log model to wandb at `save_steps` frequency."},
+    )
+    log_norm_steps: int = field(
+        default=True,
+        metadata={"help": "Log parameters and gradients norm at this frequency."},
+    )
+    log_histogram_steps: int = field(
+        default=False,
+        metadata={
+            "help": "Log parameters and gradients histograms at this frequency. Slows down training."
+        },
+    )
+
+    seed_model: int = field(
+        default=42,
+        metadata={
+            "help": "Random seed for the model that will be set at the beginning of training."
+        },
+    )
+
+    wandb_entity: Optional[str] = field(
+        default=None,
+        metadata={"help": "The wandb entity to use (for teams)."},
+    )
+    wandb_project: str = field(
+        default="dalle-mini",
+        metadata={"help": "The name of the wandb project."},
+    )
+    wandb_job_type: str = field(
+        default="Seq2Seq",
+        metadata={"help": "The name of the wandb job type."},
+    )
+
+    assert_TPU_available: bool = field(
+        default=False,
+        metadata={"help": "Verify that TPU is not in use."},
+    )
+
+    use_vmap_trick: bool = field(
+        default=True,
+        metadata={"help": "Verify that TPU is not in use."},
+    )
+
+    mp_devices: Optional[int] = field(
+        default=1,
+        metadata={
+            "help": "Number of devices required for model parallelism. The other dimension of available devices is used for data parallelism."
+        },
+    )
+
+    dp_devices: int = field(init=False)
+
+    def __post_init__(self):
+        if self.assert_TPU_available:
+            assert (
+                jax.local_device_count() == 8
+            ), "TPUs in use, please check running processes"
+        if self.output_dir.startswith("gs://"):
+            assert (
+                storage is not None
+            ), 'Could not find google.storage. Install with "pip install google-cloud-storage"'
+        assert self.optim in [
+            "distributed_shampoo",
+            "adam",
+            "adafactor",
+        ], f"Selected optimizer not supported: {self.optim}"
+        if self.optim == "adafactor" and self.weight_decay == 0:
+            self.weight_decay = None
+        assert self.graft_type in [
+            "rmsprop_normalized",
+            "rmsprop",
+            "adagrad",
+            "adagrad_normalized",
+            "sgd",
+            "sqrt_n",
+        ], f"Selected graft type not supported: {self.graft_type}"
+        assert self.lr_decay in [
+            None,
+            "linear",
+            "exponential",
+        ], f"Selected learning rate decay not supported: {self.lr_decay}"
+        if self.per_device_eval_batch_size is None:
+            self.per_device_eval_batch_size = self.per_device_train_batch_size
+        if self.log_norm_steps is True:
+            self.log_norm_steps = self.logging_steps
+        if not self.do_train:
+            self.num_train_epochs = 1
+        if (
+            os.path.exists(self.output_dir)
+            and os.listdir(self.output_dir)
+            and self.do_train
+            and not self.overwrite_output_dir
+        ):
+            raise ValueError(
+                f"Output directory ({self.output_dir}) already exists and is not empty."
+                "Use --overwrite_output_dir to overcome."
+            )
+        assert self.shard_shampoo_across in [
+            "dp",
+            "mp",
+            "2d",
+        ], f"Shard shampoo across {self.shard_shampoo_across} not supported."
+        assert (
+            self.mp_devices > 0
+        ), f"Number of devices for model parallelism must be > 0"
+        assert (
+            jax.device_count() % self.mp_devices == 0
+        ), f"Number of available devices ({jax.device_count()} must be divisible by number of devices used for model parallelism ({self.mp_devices})."
+        self.dp_devices = jax.device_count() // self.mp_devices
+
+
+def split_params(data):
+    """Split params between scanned and non-scanned"""
+    flat = traverse_util.flatten_dict(unfreeze(data))
+    split = {"standard": {}, "scanned_encoder": {}, "scanned_decoder": {}}
+    for k, v in flat.items():
+        if "FlaxBartEncoderLayers" in k:
+            split["scanned_encoder"][k] = v
+        elif "FlaxBartDecoderLayers" in k:
+            split["scanned_decoder"][k] = v
+        else:
+            split["standard"][k] = v
+    # remove empty keys
+    split = {k: v for k, v in split.items() if v}
+    for k, v in split.items():
+        split[k] = freeze(traverse_util.unflatten_dict(v))
+    return split
+
+
+def unsplit_params(data):
+    flat = {}
+    for k in ["standard", "scanned_encoder", "scanned_decoder"]:
+        if k in data:
+            flat.update(traverse_util.flatten_dict(unfreeze(data[k])))
+    return freeze(traverse_util.unflatten_dict(flat))
+
+
+class TrainState(struct.PyTreeNode):
+    step: int
+    params: core.FrozenDict[str, Any]
+    opt_state: optax.OptState
+    apply_fn: Callable = struct.field(pytree_node=False)
+    tx: optax.GradientTransformation = struct.field(pytree_node=False)
+    dropout_rng: jnp.ndarray = None
+    epoch: int = 0
+    train_time: float = 0.0  # total time the model trained
+    train_samples: int = 0  # number of samples seen
+
+    def apply_gradients(self, *, grads, **kwargs):
+        grads = split_params(grads)
+        params = split_params(self.params)
+        opt_state = {}
+        # we loop over keys: "standard", "scanned_encoder", "scanned_decoder"
+        for k, param in params.items():
+            update_fn = self.tx[k].update
+            if "scanned" in k:
+                update_fn = jax.vmap(update_fn, in_axes=(0, 0, 0), out_axes=(0, 0))
+            updates, new_opt_state = update_fn(grads[k], self.opt_state[k], param)
+            params[k] = optax.apply_updates(param, updates)
+            opt_state[k] = new_opt_state
+        params = unsplit_params(params)
+
+        return self.replace(
+            step=self.step + 1,
+            params=params,
+            opt_state=freeze(opt_state),
+            **kwargs,
+        )
+
+    @classmethod
+    def create(cls, *, apply_fn, params, tx, **kwargs):
+        opt_state = {}
+        for k, p in split_params(params).items():
+            init_fn = tx[k].init
+            if "scanned" in k:
+                init_fn = jax.vmap(init_fn)
+            opt_state[k] = init_fn(p)
+        return cls(
+            step=0,
+            apply_fn=apply_fn,
+            params=params,
+            tx=tx,
+            opt_state=freeze(opt_state),
+            **kwargs,
+        )
+
+
+def main():
+    # See all possible arguments by passing the --help flag to this script.
+    parser = HfArgumentParser(
+        (ModelArguments, DataTrainingArguments, TrainingArguments)
+    )
+    if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
+        # If we pass only one argument to the script and it's the path to a json file,
+        # let's parse it to get our arguments.
+        model_args, data_args, training_args = parser.parse_json_file(
+            json_file=os.path.abspath(sys.argv[1])
+        )
+    else:
+        model_args, data_args, training_args = parser.parse_args_into_dataclasses()
+
+    # check arguments
+    if training_args.mp_devices > jax.local_device_count():
+        assert (
+            data_args.seed_dataset is not None
+        ), "Seed dataset must be provided when model is split over multiple hosts"
+
+    # Make one log on every process with the configuration for debugging.
+    logging.basicConfig(
+        format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
+        datefmt="%m/%d/%Y %H:%M:%S",
+        level=logging.INFO,
+    )
+    # Setup logging, we only want one process per machine to log things on the screen.
+    logger.setLevel(logging.INFO if jax.process_index() == 0 else logging.ERROR)
+    if jax.process_index() == 0:
+        datasets.utils.logging.set_verbosity_warning()
+        transformers.utils.logging.set_verbosity_info()
+    else:
+        datasets.utils.logging.set_verbosity_error()
+        transformers.utils.logging.set_verbosity_error()
+
+    # Set the verbosity to info of the Transformers logger (on main process only):
+    logger.info(f"Training/evaluation parameters {training_args}")
+
+    # Load dataset
+    dataset = Dataset(
+        **asdict(data_args),
+        do_train=training_args.do_train,
+        do_eval=training_args.do_eval,
+    )
+
+    logger.info(f"Local TPUs: {jax.local_device_count()}")
+    logger.info(f"Global TPUs: {jax.device_count()}")
+
+    # Set up wandb run
+    if jax.process_index() == 0:
+        wandb.init(
+            entity=training_args.wandb_entity,
+            project=training_args.wandb_project,
+            job_type=training_args.wandb_job_type,
+            config=parser.parse_args(),
+        )
+
+    # Set up our new model config
+    config_args = {
+        k: getattr(model_args, k)
+        for k in ["dropout", "activation_dropout", "attention_dropout"]
+        if getattr(model_args, k) is not None
+    }
+    if model_args.config_name:
+        config = DalleBartConfig.from_pretrained(model_args.config_name)
+        config.gradient_checkpointing = training_args.gradient_checkpointing
+        for k, v in config_args.items():
+            setattr(config, k, v)
+    else:
+        config = None
+
+    # Load or create new model
+    if model_args.model_name_or_path:
+        model, params = DalleBart.from_pretrained(
+            model_args.model_name_or_path,
+            config=config,
+            seed=training_args.seed_model,
+            dtype=getattr(jnp, model_args.dtype),
+            _do_init=False,  # we overwrite them with loaded checkpoint
+            gradient_checkpointing=training_args.gradient_checkpointing,
+            **config_args,
+        )
+    else:
+        model = DalleBart(
+            config,
+            seed=training_args.seed_model,
+            dtype=getattr(jnp, model_args.dtype),
+            _do_init=False,
+        )
+        params = None
+    params_shape = model.params_shape_tree
+
+    # get model metadata
+    model_metadata = model_args.get_metadata()
+
+    # get PartitionSpec for model params (required to be a dict)
+    param_spec = set_partitions(params_shape, model.config.use_scan)
+    params_shape = freeze(params_shape)
+    if params is not None:
+        params = freeze(params)
+
+    # Load tokenizer
+    tokenizer = DalleBartTokenizer.from_pretrained(
+        model_args.tokenizer_name, use_fast=True
+    )
+
+    # Preprocessing the datasets.
+    # We need to normalize and tokenize inputs and targets.
+    dataset.preprocess(tokenizer=tokenizer, config=model.config)
+
+    # Initialize our training
+    dropout_rng = jax.random.PRNGKey(training_args.seed_model)
+
+    # Store some constant
+    num_epochs = training_args.num_train_epochs
+    # batch size
+    batch_size_per_node_per_grad_step = (
+        training_args.per_device_train_batch_size
+        * jax.local_device_count()
+        // training_args.mp_devices
+    )
+    batch_size_per_node = (
+        batch_size_per_node_per_grad_step * training_args.gradient_accumulation_steps
+    )
+    batch_size_per_step = batch_size_per_node * jax.process_count()
+    eval_batch_size_per_node = (
+        training_args.per_device_eval_batch_size
+        * jax.local_device_count()
+        // training_args.mp_devices
+    )
+    eval_batch_size_per_step = eval_batch_size_per_node * jax.process_count()
+    len_train_dataset, len_eval_dataset = dataset.length
+    steps_per_epoch = (
+        len_train_dataset // batch_size_per_node
+        if len_train_dataset is not None
+        else None
+    )
+    num_train_steps = (
+        steps_per_epoch * num_epochs if steps_per_epoch is not None else None
+    )
+    num_params = model.num_params(params_shape)
+
+    logger.info("***** Running training *****")
+    logger.info(f"  Num examples = {len_train_dataset}")
+    logger.info(f"  Num Epochs = {num_epochs}")
+    logger.info(
+        f"  Batch size per dp device = {training_args.per_device_train_batch_size}"
+    )
+    logger.info(f"  Number of devices = {jax.device_count()}")
+    logger.info(
+        f"  Gradient accumulation steps = {training_args.gradient_accumulation_steps}"
+    )
+    logger.info(f"  Batch size per update = {batch_size_per_step}")
+    logger.info(f"  Model parameters = {num_params:,}")
+
+    # set up wandb run
+    if jax.process_index() == 0:
+        # set default x-axis as 'train/step'
+        wandb.define_metric("*", step_metric="train/step")
+
+        # add interesting config parameters
+        wandb.config.update(
+            {
+                "len_train_dataset": len_train_dataset,
+                "len_eval_dataset": len_eval_dataset,
+                "batch_size_per_step": batch_size_per_step,
+                "num_params": num_params,
+                "model_config": model.config.to_dict(),
+                "num_devices": jax.device_count(),
+                "versions": {
+                    "jax": jax.__version__,
+                    "jaxlib": jaxlib.__version__,
+                    "flax": flax.__version__,
+                    "transformers": transformers.__version__,
+                    "datasets": datasets.__version__,
+                    "wandb": wandb.__version__,
+                    "dalle_mini": dalle_mini.__version__,
+                },
+            }
+        )
+
+    # Create learning rate schedule
+    def create_learning_rate_fn() -> Callable[[int], jnp.array]:
+        """Create the learning rate function."""
+        warmup_fn = optax.linear_schedule(
+            init_value=0.0,
+            end_value=training_args.learning_rate,
+            transition_steps=training_args.warmup_steps + 1,  # ensure not 0
+        )
+        last_boundary = training_args.warmup_steps
+        # offset step when resuming
+        if training_args.lr_offset:
+            warmup_fn = optax.join_schedules(
+                schedules=[optax.constant_schedule(0.0), warmup_fn],
+                boundaries=[training_args.lr_offset],
+            )
+            last_boundary += training_args.lr_offset
+        if training_args.lr_decay is None:
+            return warmup_fn
+        elif training_args.lr_decay == "linear":
+            assert (
+                num_train_steps is not None
+            ), "linear decay requires knowing the dataset length"
+            decay_fn = optax.linear_schedule(
+                init_value=training_args.learning_rate,
+                end_value=0,
+                transition_steps=num_train_steps - training_args.warmup_steps,
+            )
+        elif training_args.lr_decay == "exponential":
+            decay_fn = optax.exponential_decay(
+                init_value=training_args.learning_rate,
+                transition_steps=training_args.lr_transition_steps,
+                decay_rate=training_args.lr_decay_rate,
+                staircase=training_args.lr_staircase,
+            )
+        schedule_fn = optax.join_schedules(
+            schedules=[warmup_fn, decay_fn],
+            boundaries=[last_boundary],
+        )
+        return schedule_fn
+
+    learning_rate_fn = create_learning_rate_fn()
+
+    # create adam optimizer
+    if training_args.optim == "distributed_shampoo":
+        # parameters from https://github.com/tensorflow/lingvo/blob/03ee9d7cd50764b0424c7c863733c91fc0b053ec/lingvo/jax/optimizers.py#L729
+        graft_type = {
+            "sgd": GraftingType.SGD,
+            "adagrad": GraftingType.ADAGRAD,
+            "rmsprop": GraftingType.RMSPROP,
+            "rmsprop_normalized": GraftingType.RMSPROP_NORMALIZED,
+            "sqrt_n": GraftingType.SQRT_N,
+            "adagrad_normalized": GraftingType.ADAGRAD_NORMALIZED,
+        }[training_args.graft_type]
+        statistics_partition_spec = (
+            PartitionSpec(None, training_args.shard_shampoo_across, None)
+            if training_args.shard_shampoo_across != "2d"
+            else PartitionSpec(None, "dp", "mp")
+        )
+        opt = distributed_shampoo(
+            learning_rate_fn,
+            block_size=training_args.block_size,
+            beta1=training_args.beta1,
+            beta2=training_args.beta2,
+            diagonal_epsilon=1e-10,
+            matrix_epsilon=1e-6,
+            weight_decay=training_args.weight_decay,
+            start_preconditioning_step=max(
+                training_args.preconditioning_compute_steps + 1, 101
+            ),
+            preconditioning_compute_steps=training_args.preconditioning_compute_steps,
+            statistics_compute_steps=1,
+            best_effort_shape_interpretation=True,
+            graft_type=graft_type,
+            nesterov=training_args.nesterov,
+            exponent_override=0,
+            statistics_partition_spec=statistics_partition_spec,
+            preconditioner_partition_spec=PartitionSpec(
+                training_args.shard_shampoo_across, None, None
+            )
+            if training_args.shard_shampoo_across != "2d"
+            else PartitionSpec(
+                "mp" if training_args.mp_devices > training_args.dp_devices else "dp",
+                None,
+                None,
+            ),
+            num_devices_for_pjit=training_args.dp_devices,
+            shard_optimizer_states=True,
+            inverse_failure_threshold=0.1,
+            moving_average_for_momentum=True,
+            skip_preconditioning_dim_size_gt=training_args.skip_preconditioning_dim_size_gt,
+            clip_by_scaled_gradient_norm=None,
+            precision=jax.lax.Precision.HIGHEST,
+            best_effort_memory_usage_reduction=training_args.optim_quantized,
+        )
+        # get the real optimizer and helper functions
+        update_fn = opt.update
+
+        optimizer = {}
+        opt_fn = {}
+        for k, p in split_params(params_shape).items():
+            if "scanned" in k:
+                p = jax.eval_shape(lambda x: jax.tree_map(lambda y: y[0], x), p)
+            optimizer[k] = opt.init(p)
+            opt_fn[k] = NamedTuple("opt_fn", pspec_fn=Any, shape_and_dtype_fn=Any)(
+                optimizer[k].pspec_fn, optimizer[k].shape_and_dtype_fn
+            )
+            optimizer[k] = optax.GradientTransformation(optimizer[k].init_fn, update_fn)
+
+    elif training_args.optim == "adam":
+        optimizer = optax.adamw(
+            learning_rate=learning_rate_fn,
+            b1=training_args.beta1,
+            b2=training_args.beta2,
+            eps=training_args.adam_epsilon,
+            weight_decay=training_args.weight_decay,
+        )
+        optimizer = {k: optimizer for k in split_params(params_shape)}
+
+    elif training_args.optim == "adafactor":
+        # We use the default parameters here to initialize adafactor,
+        # For more details about the parameters please check https://github.com/deepmind/optax/blob/ed02befef9bf81cbbf236be3d2b0e032e9ed4a40/optax/_src/alias.py#L74
+        optimizer = optax.adafactor(
+            learning_rate=learning_rate_fn,
+            clipping_threshold=training_args.max_grad_norm,
+            weight_decay_rate=training_args.weight_decay,
+        )
+        optimizer = {k: optimizer for k in split_params(params_shape)}
+
+    # get PartitionSpec for optimizer state
+    def get_opt_state_spec_and_shape():
+        # get opt_state shape without actual init
+        opt_state_shape = {}
+        for k, p in split_params(params_shape).items():
+            if "scanned" not in k:
+                opt_state_shape[k] = jax.eval_shape(optimizer[k].init, p)
+            else:
+                opt_state_shape[k] = jax.eval_shape(jax.vmap(optimizer[k].init), p)
+
+        if training_args.optim == "adafactor":
+            # factorized state must be replicated (rank different than params)
+            opt_state_spec = {k: None for k in split_params(params_shape)}
+
+        elif training_args.optim in ["adam", "distributed_shampoo"]:
+
+            def _opt_state_spec_per_leaf(x, spec):
+                if isinstance(x, FrozenDict):
+                    # variables with same structure as params
+                    return spec
+                else:
+                    # other variables such as count
+                    return None
+
+            split_spec = split_params(set_partitions(params_shape, False))
+            opt_state_spec = {}
+            for k, p in split_params(params_shape).items():
+                if "scanned" in k:
+                    p = jax.eval_shape(lambda x: jax.tree_map(lambda y: y[0], x), p)
+                if training_args.optim == "adam":
+                    opt_state_spec[k] = jax.tree_map(
+                        _opt_state_spec_per_leaf,
+                        opt_state_shape[k],
+                        split_spec[k],
+                        # return None spec for empty elements
+                        is_leaf=lambda x: isinstance(x, (FrozenDict, optax.EmptyState)),
+                    )
+                elif training_args.optim == "distributed_shampoo":
+                    opt_state_spec[k] = opt_fn[k].pspec_fn(
+                        p,
+                        split_spec[k],
+                        statistics_partition_spec,
+                    )
+                # add dimension for scanned params
+                if "scanned" in k:
+                    opt_state_spec[k] = jax.tree_map(
+                        lambda x: PartitionSpec(*(None,) + x)
+                        if x is not None
+                        else None,
+                        opt_state_spec[k],
+                        is_leaf=lambda x: isinstance(x, PartitionSpec),
+                    )
+
+        else:
+            raise NotImplementedError
+        return freeze(opt_state_spec), freeze(opt_state_shape)
+
+    opt_state_spec, opt_state_shape = get_opt_state_spec_and_shape()
+
+    # create a mesh
+    mesh_shape = (training_args.dp_devices, training_args.mp_devices)
+    devices = np.asarray(jax.devices()).reshape(*mesh_shape)
+    mesh = maps.Mesh(devices, ("dp", "mp"))
+    logger.info(f"  Mesh shape: {mesh_shape}")
+
+    # define state spec
+    state_spec = TrainState(
+        params=param_spec,
+        opt_state=opt_state_spec,
+        dropout_rng=None,
+        step=None,
+        epoch=None,
+        train_time=None,
+        train_samples=None,
+        apply_fn=model.__call__,
+        tx=optimizer,
+    )
+
+    # init params if not available yet
+    def maybe_init_params(params):
+        if params is not None:
+            # model params are correctly loaded
+            return params
+        else:
+            # params have not been initialized yet
+            return model.init_weights(model.key, model.input_shape)
+
+    with mesh:
+        logger.info("  Creating state")
+
+        # restore metadata
+        attr_state = {}
+        keys = ["train_time", "train_samples"]
+        if model_args.restore_state:
+            keys += ["step", "epoch"]
+        attr_state = {k: v for k, v in model_metadata.items() if k in keys}
+
+        if not model_args.restore_state:
+
+            def init_state(params):
+                return TrainState.create(
+                    apply_fn=model.__call__,
+                    tx=optimizer,
+                    params=maybe_init_params(params),
+                    dropout_rng=dropout_rng,
+                    **attr_state,
+                )
+
+            state = pjit(
+                init_state,
+                in_axis_resources=(param_spec,)
+                if model_args.model_name_or_path
+                else None,
+                out_axis_resources=state_spec,
+                donate_argnums=(0,),
+            )(params)
+
+        else:
+            # load opt_state
+            opt_state = from_bytes(opt_state_shape, model_args.get_opt_state())
+
+            def restore_state(params, opt_state):
+                return TrainState(
+                    apply_fn=model.__call__,
+                    tx=optimizer,
+                    params=params,
+                    opt_state=opt_state,
+                    dropout_rng=dropout_rng,
+                    **attr_state,
+                )
+
+            state = pjit(
+                restore_state,
+                in_axis_resources=(
+                    param_spec,
+                    opt_state_spec,
+                ),
+                out_axis_resources=state_spec,
+                donate_argnums=(0, 1),
+            )(params, opt_state)
+
+            # remove opt_state from CPU
+            del opt_state
+
+    # free CPU memory
+    del params, opt_state_spec, opt_state_shape
+
+    # define batch specs
+    batch_spec = PartitionSpec("dp")
+    grad_batch_spec = PartitionSpec(None, "dp")
+
+    # define loss
+    def loss_fn(logits, labels):
+        loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1]))
+        loss = loss.mean()
+        return loss
+
+    # "vmap trick" avoids a crash when mp_devices > 1 (not sure why it happens)
+    # lead to better perf: see https://wandb.ai/dalle-mini/dalle-mini/reports/JAX-pmap-vs-pjit--VmlldzoxNDg1ODA2
+    use_vmap_trick = training_args.use_vmap_trick
+
+    # make grad_param_spec for vmap
+    if use_vmap_trick:
+        grad_param_spec = jax.tree_map(
+            lambda x: PartitionSpec(*("dp",) + (x if x is not None else (None,))),
+            param_spec,
+        )
+
+    # Define gradient update step fn
+    def train_step(state, batch, train_time):
+
+        # get a minibatch (one gradient accumulation slice)
+        def get_minibatch(batch, grad_idx):
+            return jax.tree_map(
+                lambda x: jax.lax.dynamic_index_in_dim(x, grad_idx, keepdims=False),
+                batch,
+            )
+
+        def compute_loss(params, minibatch, dropout_rng):
+            # minibatch has dim (batch_size, ...)
+            minibatch, labels = minibatch.pop("labels")
+            logits = state.apply_fn(
+                **minibatch, params=params, dropout_rng=dropout_rng, train=True
+            )[0]
+            return loss_fn(logits, labels)
+
+        grad_fn = jax.value_and_grad(compute_loss)
+
+        def loss_and_grad(grad_idx, dropout_rng):
+            # minibatch at grad_idx for gradient accumulation (None otherwise)
+            minibatch = (
+                get_minibatch(batch, grad_idx) if grad_idx is not None else batch
+            )
+            # ensure it is sharded properly
+            minibatch = with_sharding_constraint(minibatch, batch_spec)
+            # only 1 single rng per grad step, let us handle larger batch size (not sure why)
+            dropout_rng, _ = jax.random.split(dropout_rng)
+
+            if use_vmap_trick:
+                # "vmap trick", calculate loss and grads independently per dp_device
+                loss, grads = jax.vmap(
+                    grad_fn, in_axes=(None, 0, None), out_axes=(0, 0)
+                )(state.params, minibatch, dropout_rng)
+                # ensure they are sharded correctly
+                loss = with_sharding_constraint(loss, batch_spec)
+                grads = with_sharding_constraint(grads, grad_param_spec)
+                # average across all devices
+                # Note: we could average per device only after gradient accumulation, right before params update
+                loss, grads = jax.tree_map(lambda x: jnp.mean(x, axis=0), (loss, grads))
+            else:
+                # "vmap trick" does not work in multi-hosts and requires too much hbm
+                loss, grads = grad_fn(state.params, minibatch, dropout_rng)
+            # ensure grads are sharded
+            grads = with_sharding_constraint(grads, param_spec)
+            # return loss and grads
+            return loss, grads, dropout_rng
+
+        if training_args.gradient_accumulation_steps == 1:
+            loss, grads, dropout_rng = loss_and_grad(None, state.dropout_rng)
+        else:
+            # create initial state for cumul_minibatch_step loop
+            init_minibatch_step = (
+                0.0,
+                with_sharding_constraint(
+                    jax.tree_map(jnp.zeros_like, state.params), param_spec
+                ),
+                state.dropout_rng,
+            )
+
+            # accumulate gradients
+            def cumul_minibatch_step(grad_idx, cumul_loss_grad_dropout):
+                cumul_loss, cumul_grads, dropout_rng = cumul_loss_grad_dropout
+                loss, grads, dropout_rng = loss_and_grad(grad_idx, dropout_rng)
+                cumul_loss, cumul_grads = jax.tree_map(
+                    jnp.add, (cumul_loss, cumul_grads), (loss, grads)
+                )
+                cumul_grads = with_sharding_constraint(cumul_grads, param_spec)
+                return cumul_loss, cumul_grads, dropout_rng
+
+            # loop over gradients
+            loss, grads, dropout_rng = jax.lax.fori_loop(
+                0,
+                training_args.gradient_accumulation_steps,
+                cumul_minibatch_step,
+                init_minibatch_step,
+            )
+            grads = with_sharding_constraint(grads, param_spec)
+            # sum -> mean
+            loss, grads = jax.tree_map(
+                lambda x: x / training_args.gradient_accumulation_steps, (loss, grads)
+            )
+
+        grads = with_sharding_constraint(grads, param_spec)
+
+        # update state
+        state = state.apply_gradients(
+            grads=grads,
+            dropout_rng=dropout_rng,
+            train_time=train_time,
+            train_samples=state.train_samples + batch_size_per_step,
+        )
+
+        metrics = {
+            "loss": loss,
+            "learning_rate": learning_rate_fn(state.step),
+        }
+
+        def maybe_fn(fn, val, zeros, freq):
+            """Call fn only if it is a logging step"""
+            return jax.lax.cond(
+                state.step % freq == 0,
+                fn,
+                lambda _: zeros,
+                val,
+            )
+
+        if training_args.log_norm_steps:
+            zeros_norm = jax.tree_map(lambda _: jnp.float32(0), state.params)
+
+            def norm(val):
+                return jax.tree_map(lambda x: jnp.linalg.norm(x), val)
+
+            gradients_norm = maybe_fn(
+                norm, grads, zeros_norm, training_args.log_norm_steps
+            )
+            params_norm = maybe_fn(
+                norm, state.params, zeros_norm, training_args.log_norm_steps
+            )
+
+            metrics.update(
+                {
+                    "gradients_norm": gradients_norm,
+                    "params_norm": params_norm,
+                }
+            )
+
+        if training_args.log_histogram_steps:
+            zeros_hist = jax.tree_map(
+                lambda _: jnp.histogram(jnp.zeros(1), density=True), state.params
+            )
+
+            def histogram(val):
+                return jax.tree_map(lambda x: jnp.histogram(x, density=True), val)
+
+            gradients_hist = maybe_fn(
+                histogram, grads, zeros_hist, training_args.log_histogram_steps
+            )
+            params_hist = maybe_fn(
+                histogram, state.params, zeros_hist, training_args.log_histogram_steps
+            )
+
+            metrics.update(
+                {
+                    "params_hist": params_hist,
+                    "gradients_hist": gradients_hist,
+                }
+            )
+
+        return state, metrics
+
+    # Define eval fn
+    eval_model = (
+        model
+        if model_args.dtype == "float32"
+        else DalleBart(
+            model.config,
+            seed=training_args.seed_model,
+            dtype=jnp.float32,
+            _do_init=False,
+        )
+    )
+
+    def eval_step(state, batch):
+        def compute_eval_loss(batch):
+            batch, labels = batch.pop("labels")
+            logits = eval_model(**batch, params=state.params, train=False)[0]
+            return loss_fn(logits, labels)
+
+        if use_vmap_trick:
+            loss = jax.vmap(compute_eval_loss)(batch)
+            # ensure they are sharded correctly
+            loss = with_sharding_constraint(loss, batch_spec)
+            # average across all devices
+            loss = jnp.mean(loss)
+        else:
+            loss = compute_eval_loss(batch)
+
+        return loss
+
+    # Create parallel version of the train and eval step
+    p_train_step = pjit(
+        train_step,
+        in_axis_resources=(
+            state_spec,
+            grad_batch_spec
+            if training_args.gradient_accumulation_steps > 1
+            else batch_spec,
+            None,
+        ),
+        out_axis_resources=(state_spec, None),
+        donate_argnums=(0,),
+    )
+    p_eval_step = pjit(
+        eval_step,
+        in_axis_resources=(state_spec, batch_spec),
+        out_axis_resources=None,
+    )
+
+    # define metrics logger
+    class MetricsLogger:
+        def __init__(self, step):
+            # keep state
+            self.state_dict = {}
+            # estimate speed
+            self.step = step
+            self.time = time.perf_counter()
+            self.offset_time = 0.0
+
+        def update_state_metrics(self, state):
+            """Update internal state metrics (logged at each call to be used as x-axis)"""
+            self.state_dict = {
+                f'train/{k.split("_")[-1]}': state[k]
+                for k in ["step", "epoch", "train_time", "train_samples"]
+            }
+            # timing metrics
+            new_step = int(state["step"])
+            new_time = time.perf_counter()
+            if new_step > self.step:
+                # remove time for eval & save
+                delta_time = new_time - self.time - self.offset_time
+                self.offset_time = 0
+                time_per_step = delta_time / (new_step - self.step)
+                self.step = new_step
+                self.time = new_time
+                self.log_time("train_per_step", time_per_step, offset=False)
+                self.log_time("train_per_log", delta_time, offset=False)
+
+        def log_time(self, key, duration, offset=True):
+            if jax.process_index() == 0:
+                wandb.log({f"time/{key}": duration, **self.state_dict})
+            if offset:
+                self.offset_time += duration
+
+        def log(self, metrics, prefix=None):
+            if jax.process_index() == 0:
+                log_metrics = {}
+                for k, v in metrics.items():
+                    if "_norm" in k:
+                        if self.step % training_args.log_norm_steps == 0:
+                            log_metrics[f"{k}/"] = unfreeze(v)
+                    elif "_hist" in k:
+                        if self.step % training_args.log_histogram_steps == 0:
+                            v = jax.tree_map(lambda x: jax.device_get(x), unfreeze(v))
+                            v = jax.tree_map(
+                                lambda x: wandb.Histogram(np_histogram=x),
+                                v,
+                                is_leaf=lambda x: isinstance(x, tuple),
+                            )
+                            log_metrics[f"{k}/"] = v
+                    else:
+                        if prefix is not None:
+                            k = f"{prefix}/{k}"
+                        log_metrics[k] = v
+                wandb.log({**log_metrics, **self.state_dict})
+
+    # keep local copy of state
+    local_state = {
+        k: jax.device_get(getattr(state, k)).item()
+        for k in ["step", "epoch", "train_time", "train_samples"]
+    }
+    # init variables
+    start_time = time.perf_counter() - local_state["train_time"]
+    train_metrics = None
+    evaluation_ran = False
+    save_model_ran = False
+    metrics_logger = MetricsLogger(local_state["step"])
+    epochs = tqdm(
+        range(local_state["epoch"], num_epochs),
+        desc=f"Epoch ... (1/{num_epochs})",
+        position=0,
+        disable=jax.process_index() > 0,
+    )
+
+    def run_evaluation():
+        # ======================== Evaluating ==============================
+        if training_args.do_eval:
+            start_eval_time = time.perf_counter()
+            # get validation datasets
+            val_datasets = list(
+                dataset.other_eval_datasets.keys()
+                if hasattr(dataset, "other_eval_datasets")
+                else []
+            )
+            val_datasets += ["eval"]
+            for val_dataset in val_datasets:
+                eval_loader = dataset.dataloader(
+                    val_dataset,
+                    eval_batch_size_per_step
+                    * max(1, training_args.mp_devices // jax.local_device_count()),
+                )
+                eval_steps = (
+                    len_eval_dataset // eval_batch_size_per_step
+                    if len_eval_dataset is not None
+                    else None
+                )
+                eval_loss = []
+                for batch in tqdm(
+                    eval_loader,
+                    desc="Evaluating...",
+                    position=2,
+                    leave=False,
+                    total=eval_steps,
+                    disable=jax.process_index() > 0,
+                ):
+                    # need to keep only eval_batch_size_per_node items relevant to the node
+                    batch = jax.tree_map(
+                        lambda x: x.reshape(
+                            (jax.process_count(), eval_batch_size_per_node)
+                            + x.shape[1:]
+                        ),
+                        batch,
+                    )
+                    batch = jax.tree_map(lambda x: x[jax.process_index()], batch)
+
+                    # add dp dimension when using "vmap trick"
+                    if use_vmap_trick:
+                        bs_shape = (
+                            jax.local_device_count() // training_args.mp_devices,
+                            training_args.per_device_eval_batch_size,
+                        )
+                        batch = jax.tree_map(
+                            lambda x: x.reshape(bs_shape + x.shape[1:]), batch
+                        )
+
+                    # freeze batch to pass safely to jax transforms
+                    batch = freeze(batch)
+                    # accumulate losses async
+                    eval_loss.append(p_eval_step(state, batch))
+
+                # get the mean of the loss
+                eval_loss = jnp.stack(eval_loss)
+                eval_loss = jnp.mean(eval_loss)
+                eval_metrics = {"loss": eval_loss}
+
+                # log metrics
+                metrics_logger.log(eval_metrics, prefix=val_dataset)
+
+                # Print metrics and update progress bar
+                desc = f"Epoch... ({epoch + 1}/{num_epochs} | {val_dataset} Loss: {eval_metrics['loss']})"
+                epochs.write(desc)
+                epochs.desc = desc
+
+            # log time
+            metrics_logger.log_time("eval", time.perf_counter() - start_eval_time)
+
+            return eval_metrics
+
+    def run_save_model(state, eval_metrics=None):
+        if jax.process_index() == 0:
+
+            start_save_time = time.perf_counter()
+            output_dir = training_args.output_dir
+            use_bucket = output_dir.startswith("gs://")
+            if use_bucket:
+                bucket_path = Path(output_dir[5:]) / wandb.run.id / f"step_{state.step}"
+                bucket, dir_path = str(bucket_path).split("/", 1)
+                tmp_dir = tempfile.TemporaryDirectory()
+                output_dir = tmp_dir.name
+
+            # save model
+            params = jax.device_get(state.params)
+            model.save_pretrained(
+                output_dir,
+                params=params,
+            )
+
+            # save tokenizer
+            tokenizer.save_pretrained(output_dir)
+
+            # copy to bucket
+            if use_bucket:
+                client = storage.Client()
+                bucket = client.bucket(bucket)
+                for filename in Path(output_dir).glob("*"):
+                    blob_name = str(Path(dir_path) / "model" / filename.name)
+                    blob = bucket.blob(blob_name)
+                    blob.upload_from_filename(str(filename))
+                tmp_dir.cleanup()
+
+            # save state
+            opt_state = jax.device_get(state.opt_state)
+            if use_bucket:
+                blob_name = str(Path(dir_path) / "state" / "opt_state.msgpack")
+                blob = bucket.blob(blob_name)
+                blob.upload_from_file(io.BytesIO(to_bytes(opt_state)))
+            else:
+                with (Path(output_dir) / "opt_state.msgpack").open("wb") as f:
+                    f.write(to_bytes(opt_state))
+
+            # save to W&B
+            if training_args.log_model:
+                # save some space
+                c = wandb.wandb_sdk.wandb_artifacts.get_artifacts_cache()
+                c.cleanup(wandb.util.from_human_size("20GB"))
+
+                metadata = {
+                    k: jax.device_get(getattr(state, k)).item()
+                    for k in ["step", "epoch", "train_time", "train_samples"]
+                }
+                metadata["num_params"] = num_params
+                if eval_metrics is not None:
+                    metadata["eval"] = eval_metrics
+
+                # create model artifact
+                if use_bucket:
+                    metadata["bucket_path"] = f"gs://{bucket_path}/model"
+                artifact = wandb.Artifact(
+                    name=f"model-{wandb.run.id}",
+                    type="DalleBart_model",
+                    metadata=metadata,
+                )
+                if use_bucket:
+                    artifact.add_reference(metadata["bucket_path"])
+                else:
+                    for filename in [
+                        "config.json",
+                        "flax_model.msgpack",
+                        "merges.txt",
+                        "special_tokens_map.json",
+                        "tokenizer.json",
+                        "tokenizer_config.json",
+                        "vocab.json",
+                    ]:
+                        artifact.add_file(
+                            f"{Path(training_args.output_dir) / filename}"
+                        )
+                wandb.run.log_artifact(artifact)
+
+                # create state artifact
+                if use_bucket:
+                    metadata["bucket_path"] = f"gs://{bucket_path}/state"
+                artifact_state = wandb.Artifact(
+                    name=f"state-{wandb.run.id}",
+                    type="DalleBart_state",
+                    metadata=metadata,
+                )
+                if use_bucket:
+                    artifact_state.add_reference(metadata["bucket_path"])
+                else:
+                    artifact_state.add_file(
+                        f"{Path(training_args.output_dir) / 'opt_state.msgpack'}"
+                    )
+                wandb.run.log_artifact(artifact_state)
+            metrics_logger.log_time("save_model", time.perf_counter() - start_save_time)
+
+    logger.info("  Ready to start training")
+    with mesh:
+        for epoch in epochs:
+            state = state.replace(epoch=epoch)
+            local_state["epoch"] = epoch
+            # ======================== Training ================================
+            metrics_logger.update_state_metrics(local_state)
+            metrics_logger.log({})
+
+            if training_args.do_train:
+                # load data - may be replicated on multiple nodes
+                node_groups = max(
+                    1, training_args.mp_devices // jax.local_device_count()
+                )
+                loader_bs = batch_size_per_node * node_groups
+                train_loader = dataset.dataloader(
+                    "train",
+                    loader_bs,
+                    epoch,
+                )
+                # train
+                for batch in tqdm(
+                    train_loader,
+                    desc="Training...",
+                    position=1,
+                    leave=False,
+                    total=steps_per_epoch,
+                    disable=jax.process_index() > 0,
+                ):
+                    # calculate delta time (we have a lag of one step but it's ok)
+                    train_time = time.perf_counter() - start_time
+
+                    # reset control variables
+                    evaluation_ran = False
+                    save_model_ran = False
+
+                    # set correct shape to batch
+                    # - add grad_step dim if gradient_accumulation_steps > 1
+                    bs_shape = (
+                        (batch_size_per_node_per_grad_step * node_groups,)
+                        if not use_vmap_trick
+                        else (
+                            jax.local_device_count()
+                            * node_groups
+                            // training_args.mp_devices,  # local dp devices
+                            training_args.per_device_train_batch_size,
+                        )
+                    )
+                    if training_args.gradient_accumulation_steps > 1:
+                        # reshape data into (gradient_accumulation_steps, batch_per_node, ...)
+                        # to avoid any data redistribution when sharding
+                        bs_shape = (
+                            training_args.gradient_accumulation_steps,
+                        ) + bs_shape
+
+                    # reshape batch
+                    batch = jax.tree_map(
+                        lambda x: x.reshape(bs_shape + x.shape[1:]),
+                        batch,
+                    )
+                    # freeze batch to pass safely to jax transforms
+                    batch = freeze(batch)
+
+                    # train step
+                    state, train_metrics = p_train_step(state, batch, train_time)
+                    local_state["step"] += 1
+                    local_state["train_time"] = train_time
+                    local_state["train_samples"] += batch_size_per_step
+
+                    if (
+                        local_state["step"] % training_args.logging_steps == 0
+                        and jax.process_index() == 0
+                    ):
+                        metrics_logger.update_state_metrics(local_state)
+                        metrics_logger.log(train_metrics, prefix="train")
+
+                    eval_metrics = None
+                    if local_state["step"] % training_args.eval_steps == 0:
+                        eval_metrics = run_evaluation()
+                        evaluation_ran = True
+
+                    if local_state["step"] % training_args.save_steps == 0:
+                        run_save_model(state, eval_metrics)
+                        save_model_ran = True
+
+                # log final train metrics
+                if train_metrics is not None:
+                    metrics_logger.update_state_metrics(local_state)
+                    metrics_logger.log(train_metrics, prefix="train")
+
+                    epochs.write(
+                        f"Epoch... ({epoch + 1}/{num_epochs} | Loss: {train_metrics['loss']}, Learning Rate: {train_metrics['learning_rate']})"
+                    )
+
+            # Final evaluation at the end of each epoch
+            if not evaluation_ran:
+                eval_metrics = run_evaluation()
+
+            # save checkpoint after each epoch
+            if not save_model_ran:
+                run_save_model(state, eval_metrics)
+
+
+if __name__ == "__main__":
+    main()

utils.py

@@ -0,0 +1,27 @@
+import os
+import tempfile
+from pathlib import Path
+
+import wandb
+
+
+class PretrainedFromWandbMixin:
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *model_args, **kwargs):
+        """
+        Initializes from a wandb artifact or delegates loading to the superclass.
+        """
+        with tempfile.TemporaryDirectory() as tmp_dir:  # avoid multiple artifact copies
+            if ":" in pretrained_model_name_or_path and not os.path.isdir(
+                pretrained_model_name_or_path
+            ):
+                # wandb artifact
+                if wandb.run is not None:
+                    artifact = wandb.run.use_artifact(pretrained_model_name_or_path)
+                else:
+                    artifact = wandb.Api().artifact(pretrained_model_name_or_path)
+                pretrained_model_name_or_path = artifact.download(tmp_dir)
+
+            return super(PretrainedFromWandbMixin, cls).from_pretrained(
+                pretrained_model_name_or_path, *model_args, **kwargs
+            )