init spaces

app.py

@@ -0,0 +1,44 @@
+import gradio as gr
+
+import run
+
+title = "Lit GPT: Pythia 160M "
+
+with gr.Blocks(title=title) as interface:
+    with gr.Row():
+        prompt = gr.Textbox(label="Input Text")
+
+        temperature = gr.Slider(
+            0,
+            1,
+            value=0.8,
+            label="Temperature",
+            info="Set the creativity level: Higher values produce more varied results, lower values generate more predictable text.",
+        )
+        top_k = gr.Slider(
+            200,
+            300,
+            value=200,
+            label="Top K",
+            info="Control the randomness: Limits the AI to consider only the top K most likely next words.",
+        )
+        max_new_tokens = gr.Slider(
+            10,
+            500,
+            value=500,
+            label="Max Tokens",
+            info="top most preferable tokens to consider in the sampling process",
+        )
+
+        inputs = [prompt, max_new_tokens, top_k, temperature]
+
+    with gr.Column():
+        outputs = gr.Textbox(label="Generated")
+        button = gr.Button("Generate")
+        button.click(run.generate_from_prompt, inputs=inputs, outputs=outputs)
+
+    # with gr.Row():
+    #     gr.Examples(examples=examples, inputs=inputs, outputs=outputs, fn=generate_dialogue, cache_examples=True,)
+
+
+interface.launch()

requirements.txt

@@ -0,0 +1,5 @@
+torch>=2.1.0
+lightning @ git+https://github.com/Lightning-AI/lightning@6cbe9ceb560d798892bdae9186291acf9bf5d2e3 
+tokenizers           
+sentencepiece
+bitsandbytes==0.41.0 

run.py

@@ -0,0 +1,210 @@
+import sys
+import time
+from pathlib import Path
+from typing import Any, Literal, Optional
+
+import lightning as L
+import torch
+from lightning.fabric.plugins import BitsandbytesPrecision
+from lightning.fabric.strategies import FSDPStrategy
+
+from tsai_gpt.model import GPT, Block, Config
+from tsai_gpt.tokenizer import Tokenizer
+from tsai_gpt.utils import (get_default_supported_precision, gptq_quantization,
+                            load_checkpoint)
+
+L.seed_everything(1234)
+
+
+def multinomial_num_samples_1(probs: torch.Tensor) -> torch.Tensor:
+    if torch._dynamo.is_compiling():
+        # Faster alternative to `torch.multinomial(probs, num_samples=1)` that is also CUDAGraph friendly
+        distribution = torch.empty_like(probs).exponential_(1)
+        return torch.argmax(probs / distribution, dim=-1, keepdim=True)
+    return torch.multinomial(probs, num_samples=1)
+
+
+def sample(
+    logits: torch.Tensor, temperature: float = 1.0, top_k: Optional[int] = None
+) -> torch.Tensor:
+    logits = logits[0, -1]
+    # optionally crop the logits to only the top k options
+    if top_k is not None:
+        v, i = torch.topk(logits, min(top_k, logits.size(-1)))
+        # do not use `torch.where` as in nanogpt because it will repeat top-k collisions
+        logits = torch.full_like(logits, float("-inf")).scatter_(-1, i, v)
+    # optionally scale the logits and sample from a probability distribution
+    if temperature > 0.0:
+        probs = torch.nn.functional.softmax(logits / temperature, dim=-1)
+        return multinomial_num_samples_1(probs)
+    return torch.argmax(logits, dim=-1, keepdim=True)
+
+
+def next_token(
+    model: GPT, input_pos: torch.Tensor, x: torch.Tensor, **kwargs: Any
+) -> torch.Tensor:
+    logits = model(x, input_pos)
+    next = sample(logits, **kwargs)
+    return next.type_as(x)
+
+
+@torch.inference_mode()
+def generate(
+    model: GPT,
+    prompt: torch.Tensor,
+    max_returned_tokens: int,
+    *,
+    temperature: float = 1.0,
+    top_k: Optional[int] = None,
+    eos_id: Optional[int] = None,
+) -> torch.Tensor:
+    """Takes a conditioning sequence (prompt) as input and continues to generate as many tokens as requested.
+
+    The implementation of this function is modified from A. Karpathy's nanoGPT.
+
+    Args:
+        model: The model to use.
+        prompt: Tensor of shape (T) with indices of the prompt sequence.
+        max_returned_tokens: The maximum number of tokens to return (given plus generated).
+        temperature: Scales the predicted logits by 1 / temperature.
+        top_k: If specified, only sample among the tokens with the k highest probabilities.
+        eos_id: If specified, stop generating any more token once the <eos> token is triggered.
+    """
+    T = prompt.size(0)
+    assert max_returned_tokens > T
+    if model.max_seq_length < max_returned_tokens - 1:
+        # rolling the kv cache based on the `input_pos` value would be necessary. However, doing so would introduce a
+        # data dependency on the `input_pos` tensor and impact model compilation. Since this setting is uncommon, we do
+        # not support it to avoid negatively impacting the overall speed
+        raise NotImplementedError(
+            f"max_seq_length {model.max_seq_length} needs to be >= {max_returned_tokens - 1}"
+        )
+
+    device = prompt.device
+    tokens = [prompt]
+    input_pos = torch.tensor([T], device=device)
+    token = next_token(
+        model,
+        torch.arange(0, T, device=device),
+        prompt.view(1, -1),
+        temperature=temperature,
+        top_k=top_k,
+    ).clone()
+    tokens.append(token)
+    for _ in range(2, max_returned_tokens - T + 1):
+        token = next_token(
+            model, input_pos, token.view(1, -1), temperature=temperature, top_k=top_k
+        ).clone()
+        tokens.append(token)
+        if token == eos_id:
+            break
+        input_pos = input_pos.add_(1)
+    return torch.cat(tokens)
+
+
+"""
+quantize (Optional[Literal[&quot;bnb.nf4&quot;, &quot;bnb.nf4, optional): quantization method to use. Defaults to None.
+    - "bnb.nf4", "bnb.nf4-dq", "bnb.fp4", "bnb.fp4-dq": 4-bit quantization bitsandbytes
+    - "bnb.int8": 8-bit quantization bitsandbytes
+    - "gptq.int4": 4-bit quantization GPTQ
+    for more details see: https://github.com/facebookresearch/bitsandbytes, https://github.com/Lightning-AI/lit-gpt/blob/main/tutorials/quantize.md
+strategy (str, optional): Fabric strategy setting. Defaults to "auto".
+devices (int, optional): number of devices to be used. Defaults to 1.
+precision (Optional[str], optional): fabic precision settings. Defaults to None.
+"""
+
+chptk_path: str = "saved_model/last-iter-015000-ckpt.pth"
+tokenizer_path: str = "tokenizer_Llama-2-7b-chat-hf"
+quantize: Optional[
+    Literal["bnb.nf4", "bnb.nf4-dq", "bnb.fp4", "bnb.fp4-dq", "bnb.int8", "gptq.int4"]
+] = None
+strategy: str = "auto"
+devices: int = 1
+precision: Optional[str] = None
+
+precision = precision or get_default_supported_precision(training=False)
+plugins = None
+if quantize is not None:
+    if devices > 1:
+        raise NotImplemented("Multi-GPU quantization is not supported yet.")
+    if quantize.startswith("bnb."):
+        if "mixed" in precision:
+            raise ValueError("Quantization and mixed precision is not supported.")
+        dtype = {"16-true": torch.float16, "bf16-true": torch.bfloat16, "32-true": torch.float32}[
+            precision
+        ]
+        plugins = BitsandbytesPrecision(quantize[4:], dtype)
+        precision = None
+
+if strategy == "fsdp":
+    strategy = FSDPStrategy(auto_wrap_policy={Block}, cpu_offload=False)
+
+fabric = L.Fabric(devices=devices, strategy=strategy, precision=precision, plugins=plugins)
+fabric.launch()
+
+tokenizer = Tokenizer(Path("tokenizer_Llama-2-7b-chat-hf"))
+config = Config.from_name("pythia-160m")
+
+fabric.print(f"Loading model from {chptk_path}", file=sys.stderr)
+t0 = time.perf_counter()
+with fabric.init_module(empty_init=True), gptq_quantization(quantize == "gptq.int4"):
+    model = GPT(config)
+fabric.print(
+    f"Time to instantiate model: {time.perf_counter() - t0:.02f} seconds.", file=sys.stderr
+)
+with fabric.init_tensor():
+    # enable the kv cache
+    model.set_kv_cache(batch_size=1)
+
+model.eval()
+model = fabric.setup_module(model)
+
+t0 = time.perf_counter()
+load_checkpoint(fabric, model, chptk_path)
+fabric.print(
+    f"Time to load the model weights: {time.perf_counter() - t0:.02f} seconds.", file=sys.stderr
+)
+
+
+def generate_from_prompt(
+    prompt: str = "",
+    max_new_tokens: int = 500,
+    top_k: int = 200,
+    temperature: float = 0.8,
+):
+    """Generate text from a prompt using pre-trained model
+
+    Args:
+        prompt (str, optional): Prompt string to be used for generating samples. Defaults to "".
+        num_samples (int, optional): Number of samples to be generated. Defaults to 1.
+        max_new_tokens (int, optional): number of generation steps to take. Defaults to 500.
+        top_k (int, optional): top most preferable tokens to consider in the sampling process. Defaults to 200.
+        temperature (float, optional): Control randomness for sampelling process. Defaults to 0.8.
+    """
+    encoded = tokenizer.encode(prompt, device=fabric.device)
+    prompt_length = encoded.size(0)
+    max_returned_tokens = prompt_length + max_new_tokens
+    with fabric.init_tensor():
+        # set the max_seq_length to limit the memory usage to what we need
+        model.max_seq_length = max_returned_tokens
+
+    num_samples: int = 1
+    for i in range(num_samples):
+        t0 = time.perf_counter()
+        y = generate(model, encoded, max_returned_tokens, temperature=temperature, top_k=top_k)
+        t = time.perf_counter() - t0
+        # for block in model.transformer.h:
+        #     block.attn.kv_cache.reset_parameters()
+        pred = tokenizer.decode(y)
+        fabric.print(pred)
+        tokens_generated = y.size(0) - prompt_length
+        fabric.print(
+            f"Time for inference {i + 1}: {t:.02f} sec total, {tokens_generated / t:.02f} tokens/sec",
+            file=sys.stderr,
+        )
+    if fabric.device.type == "cuda":
+        fabric.print(
+            f"Memory used: {torch.cuda.max_memory_allocated() / 1e9:.02f} GB", file=sys.stderr
+        )
+
+    return pred

saved_model/last-iter-015000-ckpt.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a3a07dec62fbdcda721f26f4581e736995ee0c195f46ce2f9587a20e969a996c
+size 1948052114

tokenizer_Llama-2-7b-chat-hf/generation_config.json

@@ -0,0 +1,10 @@
+{
+  "bos_token_id": 1,
+  "do_sample": true,
+  "eos_token_id": 2,
+  "max_length": 4096,
+  "pad_token_id": 0,
+  "temperature": 0.6,
+  "top_p": 0.9,
+  "transformers_version": "4.32.0.dev0"
+}

tokenizer_Llama-2-7b-chat-hf/lit_config.json

@@ -0,0 +1 @@
+{"name": "Llama-2-7b-chat-hf", "hf_config": {"org": "meta-llama", "name": "Llama-2-7b-chat-hf"}, "block_size": 4096, "vocab_size": 32000, "padding_multiple": 64, "padded_vocab_size": 32000, "n_layer": 32, "n_head": 32, "n_embd": 4096, "rotary_percentage": 1.0, "parallel_residual": false, "bias": false, "lm_head_bias": false, "n_query_groups": 32, "shared_attention_norm": false, "_norm_class": "RMSNorm", "norm_eps": 1e-05, "_mlp_class": "LLaMAMLP", "gelu_approximate": "none", "intermediate_size": 11008, "rope_condense_ratio": 1, "rope_base": 10000}

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+    "model.layers.9.mlp.gate_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.mlp.up_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.post_attention_layernorm.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.k_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.o_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.q_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.rotary_emb.inv_freq": "pytorch_model-00001-of-00002.bin",
+    "model.layers.9.self_attn.v_proj.weight": "pytorch_model-00001-of-00002.bin",
+    "model.norm.weight": "pytorch_model-00002-of-00002.bin"
+  }
+}

tokenizer_Llama-2-7b-chat-hf/tokenizer.model

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:9e556afd44213b6bd1be2b850ebbbd98f5481437a8021afaf58ee7fb1818d347
+size 499723

tokenizer_Llama-2-7b-chat-hf/tokenizer_config.json

@@ -0,0 +1,36 @@
+{
+  "add_bos_token": true,
+  "add_eos_token": false,
+  "bos_token": {
+    "__type": "AddedToken",
+    "content": "<s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "chat_template": "{% if messages[0]['role'] == 'system' %}{% set loop_messages = messages[1:] %}{% set system_message = messages[0]['content'] %}{% else %}{% set loop_messages = messages %}{% set system_message = false %}{% endif %}{% for message in loop_messages %}{% if (message['role'] == 'user') != (loop.index0 % 2 == 0) %}{{ raise_exception('Conversation roles must alternate user/assistant/user/assistant/...') }}{% endif %}{% if loop.index0 == 0 and system_message != false %}{% set content = '<<SYS>>\\n' + system_message + '\\n<</SYS>>\\n\\n' + message['content'] %}{% else %}{% set content = message['content'] %}{% endif %}{% if message['role'] == 'user' %}{{ bos_token + '[INST] ' + content.strip() + ' [/INST]' }}{% elif message['role'] == 'assistant' %}{{ ' '  + content.strip() + ' ' + eos_token }}{% endif %}{% endfor %}",
+  "clean_up_tokenization_spaces": false,
+  "eos_token": {
+    "__type": "AddedToken",
+    "content": "</s>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  },
+  "legacy": false,
+  "model_max_length": 1000000000000000019884624838656,
+  "pad_token": null,
+  "padding_side": "right",
+  "sp_model_kwargs": {},
+  "tokenizer_class": "LlamaTokenizer",
+  "unk_token": {
+    "__type": "AddedToken",
+    "content": "<unk>",
+    "lstrip": false,
+    "normalized": false,
+    "rstrip": false,
+    "single_word": false
+  }
+}

tsai_gpt/__init__.py

@@ -0,0 +1,15 @@
+from lightning_utilities.core.imports import RequirementCache
+
+from tsai_gpt.config import Config
+from tsai_gpt.model import GPT
+from tsai_gpt.tokenizer import Tokenizer
+
+_LIGHTNING_AVAILABLE = RequirementCache("lightning>=2.1.0.dev0")
+if not bool(_LIGHTNING_AVAILABLE):
+    raise ImportError(
+        "Lit-GPT requires lightning==2.1. Please run:\n"
+        f" pip uninstall -y lightning; pip install -r requirements.txt\n{str(_LIGHTNING_AVAILABLE)}"
+    )
+
+
+__all__ = ["GPT", "Config", "Tokenizer"]

tsai_gpt/config.py

@@ -0,0 +1,1192 @@
+import json
+from copy import deepcopy
+from dataclasses import dataclass, field
+from pathlib import Path
+from typing import Any, Literal, Optional, Type, Union
+
+import torch
+from typing_extensions import Self
+
+from tsai_gpt.utils import find_multiple
+
+
+@dataclass
+class Config:
+    name: str = ""
+    hf_config: dict = field(default_factory=dict)
+    block_size: int = 4096
+    vocab_size: int = 50254
+    padding_multiple: int = 512
+    padded_vocab_size: Optional[int] = None
+    n_layer: int = 16
+    n_head: int = 32
+    n_embd: int = 4096
+    rotary_percentage: float = 0.25
+    parallel_residual: bool = True
+    bias: bool = True
+    lm_head_bias: bool = False
+    # to use multi-head attention (MHA), set this to `n_head` (default)
+    # to use multi-query attention (MQA), set this to 1
+    # to use grouped-query attention (GQA), set this to a value in between
+    # Example with `n_head=4`
+    # ┌───┐┌───┐┌───┐┌───┐     ┌───┐    ┌───┐             ┌───┐
+    # │ v ││ v ││ v ││ v │     │ v │    │ v │             │ v │
+    # └───┘└───┘└───┘└───┘     └───┘    └───┘             └───┘
+    #   │    │    │    │         │        │                 │
+    # ┌───┐┌───┐┌───┐┌───┐     ┌───┐    ┌───┐             ┌───┐
+    # │ k ││ k ││ k ││ k │     │ k │    │ k │             │ k │
+    # └───┘└───┘└───┘└───┘     └───┘    └───┘             └───┘
+    #   │    │    │    │      ┌──┴──┐  ┌──┴──┐      ┌────┬──┴─┬────┐
+    # ┌───┐┌───┐┌───┐┌───┐  ┌───┐┌───┐┌───┐┌───┐  ┌───┐┌───┐┌───┐┌───┐
+    # │ q ││ q ││ q ││ q │  │ q ││ q ││ q ││ q │  │ q ││ q ││ q ││ q │
+    # └───┘└───┘└───┘└───┘  └───┘└───┘└───┘└───┘  └───┘└───┘└───┘└───┘
+    # ◀──────────────────▶  ◀──────────────────▶  ◀──────────────────▶
+    #         MHA                    GQA                   MQA
+    #   n_query_groups=4       n_query_groups=2      n_query_groups=1
+    #
+    # credit https://arxiv.org/pdf/2305.13245.pdf
+    n_query_groups: Optional[int] = None
+    shared_attention_norm: bool = False
+    _norm_class: Literal["LayerNorm", "RMSNorm"] = "LayerNorm"
+    norm_eps: float = 1e-5
+    _mlp_class: Literal["GptNeoxMLP", "LLaMAMLP"] = "GptNeoxMLP"
+    gelu_approximate: str = "none"
+    intermediate_size: Optional[int] = None
+    rope_condense_ratio: int = 1
+    rope_base: int = 10000
+
+    def __post_init__(self):
+        if not self.name:
+            self.name = self.hf_config.get("name", self.name)
+
+        assert self.n_embd % self.n_head == 0
+        self.head_size = self.n_embd // self.n_head
+
+        # vocab size should be a power of 2 to be optimal on hardware. compute the closest value
+        if self.padded_vocab_size is None:
+            self.padded_vocab_size = find_multiple(self.vocab_size, self.padding_multiple)
+        else:
+            # vocab size shouldn't be larger than padded vocab size
+            self.vocab_size = min(self.vocab_size, self.padded_vocab_size)
+
+        # compute the number of query groups
+        if self.n_query_groups is not None:
+            assert self.n_head % self.n_query_groups == 0
+        else:
+            self.n_query_groups = self.n_head
+
+        # compute the intermediate size for MLP if not set
+        if self.intermediate_size is None:
+            if self._mlp_class == "LLaMAMLP":
+                raise ValueError("The config needs to set the `intermediate_size`")
+            self.intermediate_size = 4 * self.n_embd
+
+        self.rope_n_elem = int(self.rotary_percentage * self.head_size)
+
+    @classmethod
+    def from_name(cls, name: str, **kwargs: Any) -> Self:
+        if name not in name_to_config:
+            # search through all `config['hf_config']['name']`
+            conf_dict = next(config for config in configs if name == config["hf_config"]["name"])
+        else:
+            conf_dict = name_to_config[name]
+
+        conf_dict = conf_dict.copy()
+        if "condense_ratio" in kwargs:  # legacy name
+            kwargs["rope_condense_ratio"] = kwargs.pop("condense_ratio")
+        conf_dict.update(kwargs)
+        return cls(**conf_dict)
+
+    @classmethod
+    def from_json(cls, path: Union[str, Path], **kwargs: Any) -> Self:
+        with open(path, encoding="utf-8") as fp:
+            json_kwargs = json.load(fp)
+        if "condense_ratio" in json_kwargs:  # legacy name
+            json_kwargs["rope_condense_ratio"] = json_kwargs.pop("condense_ratio")
+        if "condense_ratio" in kwargs:  # legacy name
+            kwargs["rope_condense_ratio"] = kwargs.pop("condense_ratio")
+        if "org" in json_kwargs:  # legacy name
+            json_kwargs["hf_config"] = {"name": json_kwargs["name"], "org": json_kwargs.pop("org")}
+        if "org" in kwargs:  # legacy name
+            kwargs["hf_config"] = {
+                "name": kwargs.get("name", json_kwargs["name"]),
+                "org": kwargs.pop("org"),
+            }
+        json_kwargs.update(kwargs)
+        return cls(**json_kwargs)
+
+    @property
+    def mlp_class(self) -> Type:
+        # `self._mlp_class` cannot be the type to keep the config json serializable
+        import tsai_gpt.model
+
+        return getattr(tsai_gpt.model, self._mlp_class)
+
+    @property
+    def norm_class(self) -> Type:
+        # `self._norm_class` cannot be the type to keep the config json serializable
+        if self._norm_class == "RMSNorm":
+            from tsai_gpt.rmsnorm import RMSNorm
+
+            return RMSNorm
+        return getattr(torch.nn, self._norm_class)
+
+
+########################
+# Stability AI StableLM
+########################
+configs = [
+    # https://huggingface.co/stabilityai/stablelm-base-alpha-3b/blob/main/config.json
+    dict(
+        name="stablelm-base-alpha-3b",
+        hf_config=dict(org="stabilityai", name="stablelm-base-alpha-3b"),
+    ),
+    # https://huggingface.co/stabilityai/stablelm-base-alpha-7b/blob/main/config.json
+    dict(
+        name="stablelm-base-alpha-7b",
+        hf_config=dict(org="stabilityai", name="stablelm-base-alpha-7b"),
+        n_head=48,
+        n_embd=6144,
+        padding_multiple=256,
+    ),
+    # https://huggingface.co/stabilityai/stablelm-tuned-alpha-3b/blob/main/config.json
+    dict(
+        name="stablelm-tuned-alpha-3b",
+        hf_config=dict(org="stabilityai", name="stablelm-tuned-alpha-3b"),
+        n_head=32,
+    ),
+    # https://huggingface.co/stabilityai/stablelm-tuned-alpha-7b/blob/main/config.json
+    dict(
+        name="stablelm-tuned-alpha-7b",
+        hf_config=dict(org="stabilityai", name="stablelm-tuned-alpha-7b"),
+        n_head=48,
+        n_embd=6144,
+        padding_multiple=256,
+    ),
+]
+
+####################
+# EleutherAI Pythia
+####################
+pythia = [
+    # https://huggingface.co/EleutherAI/pythia-70m/blob/main/config.json
+    dict(
+        name="pythia-70m",
+        hf_config=dict(org="EleutherAI", name="pythia-70m"),
+        block_size=2048,
+        n_layer=6,
+        n_embd=512,
+        n_head=8,
+        padding_multiple=128,
+    ),
+    # https://huggingface.co/EleutherAI/pythia-160m/blob/main/config.json
+    dict(
+        name="pythia-160m",
+        hf_config=dict(org="EleutherAI", name="pythia-160m"),
+        block_size=2048,
+        n_layer=12,
+        n_embd=768,
+        n_head=12,
+        padding_multiple=128,
+    ),
+    # https://huggingface.co/EleutherAI/pythia-410m/blob/main/config.json
+    dict(
+        name="pythia-410m",
+        hf_config=dict(org="EleutherAI", name="pythia-410m"),
+        block_size=2048,
+        n_layer=24,
+        n_embd=1024,
+        n_head=16,
+        padding_multiple=128,
+    ),
+    # https://huggingface.co/EleutherAI/pythia-1b/blob/main/config.json
+    dict(
+        name="pythia-1b",
+        hf_config=dict(org="EleutherAI", name="pythia-1b"),
+        block_size=2048,
+        n_embd=2048,
+        n_head=8,
+        padding_multiple=128,
+    ),
+    # https://huggingface.co/EleutherAI/pythia-1.4b/blob/main/config.json
+    dict(
+        name="pythia-1.4b",
+        hf_config=dict(org="EleutherAI", name="pythia-1.4b"),
+        block_size=2048,
+        n_layer=24,
+        n_embd=2048,
+        n_head=16,
+        padding_multiple=128,
+    ),
+    # https://huggingface.co/EleutherAI/pythia-2.8b/blob/main/config.json
+    dict(
+        name="pythia-2.8b",
+        hf_config=dict(org="EleutherAI", name="pythia-2.8b"),
+        block_size=2048,
+        n_layer=32,
+        n_embd=2560,
+        padding_multiple=128,
+    ),
+    # https://huggingface.co/EleutherAI/pythia-6.9b/blob/main/config.json
+    dict(
+        name="pythia-6.9b",
+        hf_config=dict(org="EleutherAI", name="pythia-6.9b"),
+        block_size=2048,
+        n_layer=32,
+        padding_multiple=256,
+    ),
+    # https://huggingface.co/EleutherAI/pythia-12b/blob/main/config.json
+    dict(
+        name="pythia-12b",
+        hf_config=dict(org="EleutherAI", name="pythia-12b"),
+        block_size=2048,
+        n_layer=36,
+        n_embd=5120,
+        n_head=40,
+    ),
+]
+configs.extend(pythia)
+for c in pythia:
+    copy = c.copy()
+    copy["name"] = f"{c['name']}-deduped"
+    copy["hf_config"]["name"] = f"{c['hf_config']['name']}-deduped"
+    configs.append(copy)
+
+
+####################################
+# togethercomputer RedPajama INCITE
+####################################
+redpajama_incite = [
+    # https://huggingface.co/togethercomputer/RedPajama-INCITE-Base-3B-v1/blob/main/config.json
+    dict(
+        name="RedPajama-INCITE-{}-3B-v1",
+        hf_config=dict(org="togethercomputer", name="RedPajama-INCITE-{}-3B-v1"),
+        block_size=2048,
+        n_layer=32,
+        n_embd=2560,
+        padding_multiple=256,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+    ),
+    # https://huggingface.co/togethercomputer/RedPajama-INCITE-7B-Base/blob/main/config.json
+    dict(
+        name="RedPajama-INCITE-7B-{}",
+        hf_config=dict(org="togethercomputer", name="RedPajama-INCITE-7B-{}"),
+        block_size=2048,
+        n_layer=32,
+        padding_multiple=256,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+    ),
+    # this redirects to the checkpoint above. kept for those who had the old weights already downloaded
+    dict(
+        name="RedPajama-INCITE-{}-7B-v0.1",
+        hf_config=dict(org="togethercomputer", name="RedPajama-INCITE-{}-7B-v0.1"),
+        block_size=2048,
+        n_layer=32,
+        padding_multiple=256,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+    ),
+]
+for c in redpajama_incite:
+    for kind in ("Base", "Chat", "Instruct"):
+        copy = c.copy()
+        copy["name"] = c["name"].format(kind)
+        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
+        configs.append(copy)
+
+
+#################
+# TII UAE Falcon
+#################
+falcon = [
+    # https://huggingface.co/tiiuae/falcon-7b/blob/main/config.json
+    dict(
+        name="falcon-7b{}",
+        hf_config=dict(org="tiiuae", name="falcon-7b{}"),
+        block_size=2048,
+        vocab_size=65024,
+        padded_vocab_size=65024,
+        n_layer=32,
+        n_head=71,
+        n_embd=4544,
+        rotary_percentage=1.0,
+        n_query_groups=1,
+        bias=False,
+        # this is not in the config, but in the original model implementation, only for this config
+        shared_attention_norm=True,
+    ),
+    # https://huggingface.co/tiiuae/falcon-40b/blob/main/config.json
+    dict(
+        name="falcon-40b{}",
+        hf_config=dict(org="tiiuae", name="falcon-40b{}"),
+        block_size=2048,
+        vocab_size=65024,
+        padded_vocab_size=65024,
+        n_layer=60,
+        n_head=128,
+        n_embd=8192,
+        rotary_percentage=1.0,
+        n_query_groups=8,
+        bias=False,
+    ),
+]
+for c in falcon:
+    for kind in ("", "-instruct"):
+        copy = c.copy()
+        copy["name"] = c["name"].format(kind)
+        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
+        configs.append(copy)
+
+# https://huggingface.co/tiiuae/falcon-180b/blob/main/config.json
+falcon180b = dict(
+    name="falcon-180B{}",
+    hf_config=dict(org="tiiuae", name="falcon-180B{}"),
+    block_size=2048,
+    vocab_size=65024,
+    padded_vocab_size=65024,
+    n_layer=80,
+    n_head=232,
+    n_embd=14848,
+    rotary_percentage=1.0,
+    n_query_groups=8,
+    bias=False,
+)
+
+for kind in ("", "-chat"):
+    copy = falcon180b.copy()
+    copy["name"] = falcon180b["name"].format(kind)
+    copy["hf_config"]["name"] = falcon180b["hf_config"]["name"].format(kind)
+    configs.append(copy)
+
+
+#############################
+# OpenLM Research Open LLaMA
+#############################
+open_LLaMA = [
+    # https://huggingface.co/openlm-research/open_llama_3b/blob/main/config.json
+    dict(
+        name="open_llama_3b",
+        hf_config=dict(org="openlm-research", name="open_llama_3b"),
+        block_size=2048,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=26,
+        n_embd=3200,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=8640,
+    ),
+    # https://huggingface.co/openlm-research/open_llama_7b/blob/main/config.json
+    dict(
+        name="open_llama_7b",
+        hf_config=dict(org="openlm-research", name="open_llama_7b"),
+        block_size=2048,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+    ),
+    # https://huggingface.co/openlm-research/open_llama_13b/blob/main/config.json
+    dict(
+        name="open_llama_13b",
+        hf_config=dict(org="openlm-research", name="open_llama_13b"),
+        block_size=2048,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+]
+configs.extend(open_LLaMA)
+
+
+###############
+# LMSYS Vicuna
+###############
+vicuna = [
+    # https://huggingface.co/lmsys/vicuna-7b-v1.3/blob/main/config.json
+    dict(
+        name="vicuna-7b-v1.3",
+        hf_config=dict(org="lmsys", name="vicuna-7b-v1.3"),
+        block_size=2048,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+    ),
+    # https://huggingface.co/lmsys/vicuna-13b-v1.3/blob/main/config.json
+    dict(
+        name="vicuna-13b-v1.3",
+        hf_config=dict(org="lmsys", name="vicuna-13b-v1.3"),
+        block_size=2048,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+    # https://huggingface.co/lmsys/vicuna-33b-v1.3/blob/main/config.json
+    dict(
+        name="vicuna-33b-v1.3",
+        hf_config=dict(org="lmsys", name="vicuna-33b-v1.3"),
+        block_size=2048,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=60,
+        n_head=52,
+        n_embd=6656,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=17920,
+    ),
+    # https://huggingface.co/lmsys/vicuna-7b-v1.5/blob/main/config.json
+    dict(
+        name="vicuna-7b-v1.5",
+        hf_config=dict(org="lmsys", name="vicuna-7b-v1.5"),
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+    ),
+    # https://huggingface.co/lmsys/vicuna-7b-v1.5-16k/blob/main/config.json
+    dict(
+        name="vicuna-7b-v1.5-16k",
+        hf_config=dict(org="lmsys", name="vicuna-7b-v1.5-16k"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+        rope_condense_ratio=4,
+    ),
+    # https://huggingface.co/lmsys/vicuna-13b-v1.5/blob/main/config.json
+    dict(
+        name="vicuna-13b-v1.5",
+        hf_config=dict(org="lmsys", name="vicuna-13b-v1.5"),
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+    # https://huggingface.co/lmsys/vicuna-13b-v1.5-16k/blob/main/config.json
+    dict(
+        name="vicuna-13b-v1.5-16k",
+        hf_config=dict(org="lmsys", name="vicuna-13b-v1.5-16k"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+        rope_condense_ratio=4,
+    ),
+]
+configs.extend(vicuna)
+
+
+#################
+# LMSYS LongChat
+#################
+long_chat = [
+    # https://huggingface.co/lmsys/longchat-7b-16k/blob/main/config.json
+    dict(
+        name="longchat-7b-16k",
+        hf_config=dict(org="lmsys", name="longchat-7b-16k"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+        rope_condense_ratio=8,
+    ),
+    # https://huggingface.co/lmsys/longchat-13b-16k/blob/main/config.json
+    dict(
+        name="longchat-13b-16k",
+        hf_config=dict(org="lmsys", name="longchat-13b-16k"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+        rope_condense_ratio=8,
+    ),
+]
+configs.extend(long_chat)
+
+
+######################
+# NousResearch Hermes
+######################
+nous_research = [
+    # https://huggingface.co/NousResearch/Nous-Hermes-llama-2-7b/blob/main/config.json
+    dict(
+        name="Nous-Hermes-llama-2-7b",
+        hf_config=dict(org="NousResearch", name="Nous-Hermes-llama-2-7b"),
+        padded_vocab_size=32000,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+    ),
+    # https://huggingface.co/NousResearch/Nous-Hermes-13B/blob/main/config.json
+    dict(
+        name="Nous-Hermes-13b",
+        hf_config=dict(org="NousResearch", name="Nous-Hermes-13b"),
+        block_size=2048,
+        vocab_size=32000,
+        padded_vocab_size=32001,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-6,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+    # https://huggingface.co/NousResearch/Nous-Hermes-Llama2-13b
+    dict(
+        name="Nous-Hermes-Llama2-13b",
+        hf_config=dict(org="NousResearch", name="Nous-Hermes-Llama2-13b"),
+        vocab_size=32000,
+        padded_vocab_size=32032,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+]
+configs.extend(nous_research)
+
+
+###############
+# Meta LLaMA 2
+###############
+llama_2 = [
+    # https://huggingface.co/meta-llama/Llama-2-7b-hf/blob/main/config.json
+    dict(
+        name="Llama-2-7b{}-hf",
+        hf_config=dict(org="meta-llama", name="Llama-2-7b{}-hf"),
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+    ),
+    # https://huggingface.co/meta-llama/Llama-2-13b-hf/blob/main/config.json
+    dict(
+        name="Llama-2-13b{}-hf",
+        hf_config=dict(org="meta-llama", name="Llama-2-13b{}-hf"),
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+    # https://huggingface.co/meta-llama/Llama-2-70b-hf/blob/main/config.json
+    dict(
+        name="Llama-2-70b{}-hf",
+        hf_config=dict(org="meta-llama", name="Llama-2-70b{}-hf"),
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=80,
+        n_head=64,
+        n_embd=8192,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=28672,
+    ),
+]
+for c in llama_2:
+    for kind in ("", "-chat"):
+        copy = c.copy()
+        copy["name"] = c["name"].format(kind)
+        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
+        configs.append(copy)
+
+
+##########################
+# Stability AI FreeWilly2
+##########################
+freewilly_2 = [
+    # https://huggingface.co/stabilityai/FreeWilly2/blob/main/config.json
+    dict(
+        name="FreeWilly2",
+        hf_config=dict(org="stabilityai", name="FreeWilly2"),
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=80,
+        n_head=64,
+        n_embd=8192,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=28672,
+    )
+]
+configs.extend(freewilly_2)
+
+
+##################
+# Meta Code Llama
+##################
+code_llama = [
+    # https://huggingface.co/codellama/CodeLlama-7b-hf/blob/main/config.json
+    dict(
+        name="CodeLlama-7b-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-7b-hf"),
+        block_size=16384,
+        vocab_size=32016,
+        padding_multiple=16,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+        rope_base=1000000,
+    ),
+    # https://huggingface.co/codellama/CodeLlama-13b-hf/blob/main/config.json
+    dict(
+        name="CodeLlama-13b-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-13b-hf"),
+        block_size=16384,
+        vocab_size=32016,
+        padding_multiple=16,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+        rope_base=1000000,
+    ),
+    # https://huggingface.co/codellama/CodeLlama-34b-hf/blob/main/config.json
+    dict(
+        name="CodeLlama-34b-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-34b-hf"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=48,
+        n_head=64,
+        n_embd=8192,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=22016,
+        rope_base=1000000,
+    ),
+    # https://huggingface.co/codellama/CodeLlama-7b-Python-hf/blob/main/config.json
+    dict(
+        name="CodeLlama-7b-Python-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-7b-Python-hf"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+        rope_base=1000000,
+    ),
+    # https://huggingface.co/codellama/CodeLlama-13b-Python-hf/blob/main/config.json
+    dict(
+        name="CodeLlama-13b-Python-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-13b-Python-hf"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+        rope_base=1000000,
+    ),
+    # https://huggingface.co/codellama/CodeLlama-34b-Python-hf/blob/main/config.json
+    dict(
+        name="CodeLlama-34b-Python-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-34b-Python-hf"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=48,
+        n_head=64,
+        n_embd=8192,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=22016,
+        rope_base=1000000,
+    ),
+    # https://huggingface.co/codellama/CodeLlama-7b-Instruct-hf/tree/main/config.json
+    dict(
+        name="CodeLlama-7b-Instruct-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-7b-Instruct-hf"),
+        block_size=16384,
+        vocab_size=32016,
+        padding_multiple=16,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+        rope_base=1000000,
+    ),
+    # https://huggingface.co/codellama/CodeLlama-13b-Instruct-hf/blob/main/config.json
+    dict(
+        name="CodeLlama-13b-Instruct-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-13b-Instruct-hf"),
+        block_size=2048,
+        vocab_size=32016,
+        padding_multiple=16,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+        rope_base=1000000,
+    ),
+    # https://huggingface.co/codellama/CodeLlama-34b-Instruct-hf/blob/main/config.json
+    dict(
+        name="CodeLlama-34b-Instruct-hf",
+        hf_config=dict(org="codellama", name="CodeLlama-34b-Instruct-hf"),
+        block_size=16384,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=48,
+        n_head=64,
+        n_embd=8192,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=22016,
+        rope_base=1000000,
+    ),
+]
+configs.extend(code_llama)
+
+
+########################
+# garage-bAInd Platypus
+########################
+platypus = [
+    # https://huggingface.co/garage-bAInd/Platypus-30B/blob/main/config.json
+    dict(
+        name="Platypus-30B",
+        hf_config=dict(org="garage-bAInd", name="Platypus-30B"),
+        block_size=2048,
+        padded_vocab_size=32000,
+        n_layer=60,
+        n_head=52,
+        n_embd=6656,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-06,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=17920,
+    ),
+    # https://huggingface.co/garage-bAInd/Platypus2-7B/blob/main/config.json
+    dict(
+        name="Platypus2-7B",
+        hf_config=dict(org="garage-bAInd", name="Platypus2-7B"),
+        padded_vocab_size=32000,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+    ),
+    # https://huggingface.co/garage-bAInd/Platypus2-13B/blob/main/config.json
+    dict(
+        name="Platypus2-13B",
+        hf_config=dict(org="garage-bAInd", name="Platypus2-13B"),
+        padded_vocab_size=32000,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+    # https://huggingface.co/garage-bAInd/Platypus2-70B/blob/main/config.json
+    dict(
+        name="Platypus2-70B",
+        hf_config=dict(org="garage-bAInd", name="Platypus2-70B"),
+        padded_vocab_size=32000,
+        n_layer=80,
+        n_head=64,
+        n_embd=8192,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=28672,
+    ),
+    # https://huggingface.co/garage-bAInd/Camel-Platypus2-13B/blob/main/config.json
+    dict(
+        name="Camel-Platypus2-13B",
+        hf_config=dict(org="garage-bAInd", name="Camel-Platypus2-13B"),
+        padded_vocab_size=32000,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+    # https://huggingface.co/garage-bAInd/Camel-Platypus2-70B/blob/main/config.json
+    dict(
+        name="Camel-Platypus2-70B",
+        hf_config=dict(org="garage-bAInd", name="Camel-Platypus2-70B"),
+        padded_vocab_size=32000,
+        n_layer=80,
+        n_head=64,
+        n_embd=8192,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=28672,
+    ),
+    # https://huggingface.co/garage-bAInd/Stable-Platypus2-13B/blob/main/config.json
+    dict(
+        name="Stable-Platypus2-13B",
+        hf_config=dict(org="garage-bAInd", name="Stable-Platypus2-13B"),
+        padded_vocab_size=32000,
+        n_layer=40,
+        n_head=40,
+        n_embd=5120,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=13824,
+    ),
+    # https://huggingface.co/garage-bAInd/Platypus2-70B-instruct/blob/main/config.json
+    dict(
+        name="Platypus2-70B-instruct",
+        hf_config=dict(org="garage-bAInd", name="Platypus2-70B-instruct"),
+        padded_vocab_size=32000,
+        n_layer=80,
+        n_head=64,
+        n_embd=8192,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=28672,
+    ),
+]
+configs.extend(platypus)
+
+
+##########################
+# Stability AI StableCode
+##########################
+stablecode = [
+    # https://huggingface.co/stabilityai/stablecode-completion-alpha-3b/blob/main/config.json
+    dict(
+        name="stablecode-completion-alpha-3b",
+        hf_config=dict(org="stabilityai", name="stablecode-completion-alpha-3b"),
+        block_size=16384,
+        vocab_size=49152,
+        n_layer=32,
+        n_embd=2560,
+    ),
+    # https://huggingface.co/stabilityai/stablecode-completion-alpha-3b-4k/blob/main/config.json
+    dict(
+        name="stablecode-completion-alpha-3b-4k",
+        hf_config=dict(org="stabilityai", name="stablecode-completion-alpha-3b-4k"),
+        vocab_size=49152,
+        n_layer=32,
+        n_embd=2560,
+    ),
+    # https://huggingface.co/stabilityai/stablecode-instruct-alpha-3b/blob/main/config.json
+    dict(
+        name="stablecode-instruct-alpha-3b",
+        hf_config=dict(org="stabilityai", name="stablecode-instruct-alpha-3b"),
+        vocab_size=49152,
+        n_layer=32,
+        n_embd=2560,
+    ),
+]
+configs.extend(stablecode)
+
+
+##################################
+# togethercomputer LLaMA-2-7B-32K
+##################################
+together_llama2_32k = [
+    # https://huggingface.co/togethercomputer/LLaMA-2-7B-32K/blob/main/config.json
+    dict(
+        name="LLaMA-2-7B-32K",
+        hf_config=dict(org="togethercomputer", name="LLaMA-2-7B-32K"),
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=32,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        _mlp_class="LLaMAMLP",
+        intermediate_size=11008,
+        rope_condense_ratio=8,
+    )
+]
+configs.extend(together_llama2_32k)
+
+
+################
+# Microsoft Phi
+################
+phi = [
+    # https://huggingface.co/microsoft/phi-1_5/blob/main/config.json
+    dict(
+        name="phi-1_5",
+        hf_config=dict(org="microsoft", name="phi-1_5"),
+        vocab_size=50257,
+        padded_vocab_size=51200,
+        block_size=2048,
+        n_embd=2048,
+        n_layer=24,
+        rotary_percentage=0.5,  # 32 / (n_embd / n_head) = 32 / 64
+        shared_attention_norm=True,
+        lm_head_bias=True,
+        gelu_approximate="tanh",
+    )
+]
+configs.extend(phi)
+
+
+#############
+# Mistral AI
+#############
+mistral = [
+    # https://huggingface.co/mistralai/Mistral-7B-v0.1/blob/main/config.json
+    dict(
+        name="Mistral-7B-{}v0.1",
+        hf_config=dict(org="mistralai", name="Mistral-7B-{}v0.1"),
+        padded_vocab_size=32000,
+        block_size=4096,  # should be 32768 but sliding window attention is not implemented
+        n_layer=32,
+        n_query_groups=8,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",
+        norm_eps=1e-05,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=14336,
+    )
+]
+for c in mistral:
+    for kind in ("", "Instruct-"):
+        copy = c.copy()
+        copy["name"] = c["name"].format(kind)
+        copy["hf_config"]["name"] = c["hf_config"]["name"].format(kind)
+        configs.append(copy)
+
+
+############
+# TinyLlama
+############
+tiny_llama = [
+    dict(
+        name="tiny-llama-1.1b",
+        hf_config=dict(org="PY007", name="TinyLlama-1.1B-intermediate-step-480k-1T"),
+        block_size=2048,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=22,
+        n_head=32,
+        n_embd=2048,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",  # original TinyLlama uses FusedRMSNorm
+        norm_eps=1e-5,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=5632,
+        n_query_groups=4,
+    ),
+    dict(
+        name="tiny-llama-new",
+        hf_config=dict(org="PY007", name="TinyLlama-1.1B-intermediate-step-480k-1T"),
+        block_size=768,
+        vocab_size=32000,
+        padding_multiple=64,
+        n_layer=18,
+        n_head=32,
+        n_embd=1024,
+        rotary_percentage=1.0,
+        parallel_residual=False,
+        bias=False,
+        _norm_class="RMSNorm",  # original TinyLlama uses FusedRMSNorm
+        norm_eps=1e-5,
+        _mlp_class="LLaMAMLP",
+        intermediate_size=5632,
+        n_query_groups=4,
+    ),
+]
+configs.extend(tiny_llama)
+
+
+name_to_config = {config["name"]: config for config in configs}

tsai_gpt/model.py

@@ -0,0 +1,367 @@
+"""Full definition of a GPT NeoX Language Model, all of it in this single file.
+
+Based on the nanoGPT implementation: https://github.com/karpathy/nanoGPT and
+https://github.com/EleutherAI/gpt-neox/tree/main/megatron/model.
+"""
+import math
+from typing import Any, Optional, Tuple
+
+import torch
+import torch.nn as nn
+from typing_extensions import Self
+
+from tsai_gpt.config import Config
+
+
+class GPT(nn.Module):
+    def __init__(self, config: Config) -> None:
+        super().__init__()
+        assert config.padded_vocab_size is not None
+        self.config = config
+
+        self.lm_head = nn.Linear(config.n_embd, config.padded_vocab_size, bias=config.lm_head_bias)
+        self.transformer = nn.ModuleDict(
+            dict(
+                wte=nn.Embedding(config.padded_vocab_size, config.n_embd),
+                h=nn.ModuleList(Block(config) for _ in range(config.n_layer)),
+                ln_f=config.norm_class(config.n_embd, eps=config.norm_eps),
+            )
+        )
+        self.max_seq_length = self.config.block_size
+        self.mask_cache: Optional[torch.Tensor] = None
+
+    @property
+    def max_seq_length(self) -> int:
+        return self._max_seq_length
+
+    @max_seq_length.setter
+    def max_seq_length(self, value: int) -> None:
+        """
+        When doing inference, the sequences used might be shorter than the model's context length.
+        This allows setting a smaller number to avoid allocating unused memory
+        """
+        if value > self.config.block_size:
+            raise ValueError(
+                f"Cannot attend to {value}, block size is only {self.config.block_size}"
+            )
+        self._max_seq_length = value
+        if not hasattr(self, "cos"):
+            # first call
+            cos, sin = self.rope_cache()
+            self.register_buffer("cos", cos, persistent=False)
+            self.register_buffer("sin", sin, persistent=False)
+        elif value != self.cos.size(0):
+            # override
+            self.cos, self.sin = self.rope_cache(device=self.cos.device)
+        # the mask and kv cache size will get updated on `set_kv_cache`. we cannot update it here because we don't know
+        # if the kv cache is expected
+
+    def reset_parameters(self) -> None:
+        # Trigger resetting the rope-cache
+        self.max_seq_length = self.config.block_size
+
+    def _init_weights(self, module: nn.Module) -> None:
+        """Meant to be used with `gpt.apply(gpt._init_weights)`."""
+        if isinstance(module, nn.Linear):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
+
+    def forward(self, idx: torch.Tensor, input_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
+        T = idx.size(1)
+        if self.max_seq_length < T:
+            raise ValueError(
+                f"Cannot forward sequence of length {T}, max seq length is only {self.max_seq_length}."
+            )
+
+        if input_pos is not None:  # use the kv cache
+            cos = self.cos.index_select(0, input_pos)
+            sin = self.sin.index_select(0, input_pos)
+            if self.mask_cache is None:
+                raise TypeError("You need to call `gpt.set_kv_cache()`")
+            mask = self.mask_cache.index_select(2, input_pos)
+        else:
+            cos = self.cos[:T]
+            sin = self.sin[:T]
+            mask = None
+
+        x = self.transformer.wte(idx)  # token embeddings of shape (b, t, n_embd)
+        for block in self.transformer.h:
+            x = block(x, cos, sin, mask, input_pos)
+        x = self.transformer.ln_f(x)
+        return self.lm_head(x)  # (b, t, vocab_size)
+
+    @classmethod
+    def from_name(cls, name: str, **kwargs: Any) -> Self:
+        return cls(Config.from_name(name, **kwargs))
+
+    def rope_cache(
+        self, device: Optional[torch.device] = None
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        return build_rope_cache(
+            seq_len=self.max_seq_length,
+            n_elem=self.config.rope_n_elem,
+            device=device,
+            condense_ratio=self.config.rope_condense_ratio,
+            base=self.config.rope_base,
+        )
+
+    def set_kv_cache(
+        self,
+        batch_size: int,
+        rope_cache_length: Optional[int] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> None:
+        if rope_cache_length is None:
+            rope_cache_length = self.cos.size(-1)
+        max_seq_length = self.max_seq_length
+
+        # initialize the kv cache for all blocks
+        for block in self.transformer.h:
+            block.attn.kv_cache = block.attn.build_kv_cache(
+                batch_size, max_seq_length, rope_cache_length, device, dtype
+            )
+
+        if self.mask_cache is None or self.mask_cache.size(3) != max_seq_length:
+            # passing `attn_mask` to SDPA downgrades it to use the inefficient implementation. since we only need the mask
+            # for the kv-cache support (only during inference), we only create it in that situation
+            # this will be resolved by https://github.com/pytorch/pytorch/issues/96099
+            ones = torch.ones((max_seq_length, max_seq_length), device=device, dtype=torch.bool)
+            self.mask_cache = torch.tril(ones).unsqueeze(0).unsqueeze(0)
+
+    def clear_kv_cache(self) -> None:
+        self.mask_cache = None
+        for block in self.transformer.h:
+            block.attn.kv_cache = None
+
+
+class Block(nn.Module):
+    def __init__(self, config: Config) -> None:
+        super().__init__()
+        self.norm_1 = config.norm_class(config.n_embd, eps=config.norm_eps)
+        self.attn = CausalSelfAttention(config)
+        self.norm_2 = (
+            None
+            if config.shared_attention_norm
+            else config.norm_class(config.n_embd, eps=config.norm_eps)
+        )
+        self.mlp = config.mlp_class(config)
+
+        self.config = config
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+        input_pos: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        n_1 = self.norm_1(x)
+        h = self.attn(n_1, cos, sin, mask, input_pos)
+        if self.config.parallel_residual:
+            n_2 = n_1 if self.config.shared_attention_norm else self.norm_2(x)
+            x = self.mlp(n_2) + h + x
+        else:
+            if self.config.shared_attention_norm:
+                raise NotImplementedError(
+                    "No checkpoint amongst the ones we support uses this configuration"
+                    " (non-parallel residual and shared attention norm)."
+                )
+            x = h + x
+            x = self.mlp(self.norm_2(x)) + x
+        return x
+
+
+class CausalSelfAttention(nn.Module):
+    def __init__(self, config: Config) -> None:
+        super().__init__()
+        shape = (config.n_head + 2 * config.n_query_groups) * config.head_size
+        # key, query, value projections for all heads, but in a batch
+        self.attn = nn.Linear(config.n_embd, shape, bias=config.bias)
+        # output projection
+        self.proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)
+        # disabled by default
+        self.kv_cache: Optional[KVCache] = None
+
+        self.config = config
+
+    def forward(
+        self,
+        x: torch.Tensor,
+        cos: torch.Tensor,
+        sin: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+        input_pos: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        B, T, C = x.size()  # batch size, sequence length, embedding dimensionality (n_embd)
+
+        qkv = self.attn(x)
+
+        # assemble into a number of query groups to support MHA, MQA and GQA together (see `config.n_query_groups`)
+        q_per_kv = self.config.n_head // self.config.n_query_groups
+        total_qkv = q_per_kv + 2  # each group has 1+ queries, 1 key, and 1 value
+        qkv = qkv.view(B, T, self.config.n_query_groups, total_qkv, self.config.head_size)
+        qkv = qkv.permute(0, 2, 3, 1, 4)  # (B, n_query_groups, total_qkv, T, hs)
+
+        # split batched computation into three
+        q, k, v = qkv.split((q_per_kv, 1, 1), dim=2)
+
+        # maybe repeat k and v if for the non multi-head attention cases
+        # training: flash attention requires it
+        # inference: multi-query would require a full kv cache so avoid it to limit its memory usage
+        if self.config.n_query_groups != self.config.n_head and (
+            input_pos is None or self.config.n_query_groups != 1
+        ):
+            k = k.expand(B, self.config.n_query_groups, q_per_kv, T, self.config.head_size)
+            v = v.expand(B, self.config.n_query_groups, q_per_kv, T, self.config.head_size)
+
+        q = q.reshape(B, -1, T, self.config.head_size)  # (B, nh_q, T, hs)
+        k = k.reshape(B, -1, T, self.config.head_size)  # (B, nh_k, T, hs)
+        v = v.reshape(B, -1, T, self.config.head_size)  # (B, nh_v, T, hs)
+
+        q_roped = apply_rope(q[..., : self.config.rope_n_elem], cos, sin)
+        k_roped = apply_rope(k[..., : self.config.rope_n_elem], cos, sin)
+        q = torch.cat((q_roped, q[..., self.config.rope_n_elem :]), dim=-1)
+        k = torch.cat((k_roped, k[..., self.config.rope_n_elem :]), dim=-1)
+
+        if input_pos is not None:
+            if not isinstance(self.kv_cache, KVCache):
+                raise TypeError("You need to call `gpt.set_kv_cache()`")
+            k, v = self.kv_cache(input_pos, k, v)
+
+        y = self.scaled_dot_product_attention(q, k, v, mask)
+
+        y = y.reshape(B, T, C)  # re-assemble all head outputs side by side
+
+        # output projection
+        return self.proj(y)
+
+    def scaled_dot_product_attention(
+        self,
+        q: torch.Tensor,
+        k: torch.Tensor,
+        v: torch.Tensor,
+        mask: Optional[torch.Tensor] = None,
+    ) -> torch.Tensor:
+        scale = 1.0 / math.sqrt(self.config.head_size)
+        y = torch.nn.functional.scaled_dot_product_attention(
+            q, k, v, attn_mask=mask, dropout_p=0.0, scale=scale, is_causal=mask is None
+        )
+        return y.transpose(1, 2)
+
+    def build_kv_cache(
+        self,
+        batch_size: int,
+        max_seq_length: int,
+        rope_cache_length: Optional[int] = None,
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> "KVCache":
+        heads = 1 if self.config.n_query_groups == 1 else self.config.n_head
+        v_shape = (batch_size, heads, max_seq_length, self.config.head_size)
+        if rope_cache_length is None:
+            if self.config.rotary_percentage != 1.0:
+                raise TypeError("Please pass the `rope_cache_length=gpt.cos.size(-1)` value")
+            k_shape = v_shape
+        else:
+            k_shape = (
+                batch_size,
+                heads,
+                max_seq_length,
+                rope_cache_length + self.config.head_size - self.config.rope_n_elem,
+            )
+        return KVCache(k_shape, v_shape, device=device, dtype=dtype)
+
+
+class GptNeoxMLP(nn.Module):
+    def __init__(self, config: Config) -> None:
+        super().__init__()
+        self.fc = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)
+        self.proj = nn.Linear(config.intermediate_size, config.n_embd, bias=config.bias)
+
+        self.config = config
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x = self.fc(x)
+        x = torch.nn.functional.gelu(x, approximate=self.config.gelu_approximate)
+        return self.proj(x)
+
+
+class LLaMAMLP(nn.Module):
+    def __init__(self, config: Config) -> None:
+        super().__init__()
+        self.fc_1 = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)
+        self.fc_2 = nn.Linear(config.n_embd, config.intermediate_size, bias=config.bias)
+        self.proj = nn.Linear(config.intermediate_size, config.n_embd, bias=config.bias)
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        x_fc_1 = self.fc_1(x)
+        x_fc_2 = self.fc_2(x)
+        x = torch.nn.functional.silu(x_fc_1) * x_fc_2
+        return self.proj(x)
+
+
+def build_rope_cache(
+    seq_len: int,
+    n_elem: int,
+    device: Optional[torch.device] = None,
+    base: int = 10000,
+    condense_ratio: int = 1,
+) -> Tuple[torch.Tensor, torch.Tensor]:
+    """Enhanced Transformer with Rotary Position Embedding.
+
+    Derived from: https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/labml_nn/
+    transformers/rope/__init__.py. MIT License:
+    https://github.com/labmlai/annotated_deep_learning_paper_implementations/blob/master/license.
+    """
+    # $\Theta = {\theta_i = 10000^{\frac{2(i-1)}{d}}, i \in [1, 2, ..., \frac{d}{2}]}$
+    theta = 1.0 / (base ** (torch.arange(0, n_elem, 2, device=device).float() / n_elem))
+
+    # Create position indexes `[0, 1, ..., seq_len - 1]`
+    seq_idx = torch.arange(seq_len, device=device) / condense_ratio
+
+    # Calculate the product of position index and $\theta_i$
+    idx_theta = torch.outer(seq_idx, theta).repeat(1, 2)
+
+    return torch.cos(idx_theta), torch.sin(idx_theta)
+
+
+def apply_rope(x: torch.Tensor, cos: torch.Tensor, sin: torch.Tensor) -> torch.Tensor:
+    head_size = x.size(-1)
+    x1 = x[..., : head_size // 2]  # (B, nh, T, hs/2)
+    x2 = x[..., head_size // 2 :]  # (B, nh, T, hs/2)
+    rotated = torch.cat((-x2, x1), dim=-1)  # (B, nh, T, hs)
+    roped = (x * cos) + (rotated * sin)
+    return roped.type_as(x)
+
+
+class KVCache(nn.Module):
+    def __init__(
+        self,
+        k_shape: Tuple[int, int, int, int],
+        v_shape: Tuple[int, int, int, int],
+        device: Optional[torch.device] = None,
+        dtype: Optional[torch.dtype] = None,
+    ) -> None:
+        super().__init__()
+        self.register_buffer(
+            "k", torch.zeros(k_shape, device=device, dtype=dtype), persistent=False
+        )
+        self.register_buffer(
+            "v", torch.zeros(v_shape, device=device, dtype=dtype), persistent=False
+        )
+
+    def forward(
+        self, input_pos: torch.Tensor, k: torch.Tensor, v: torch.Tensor
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        # move the buffer to the activation dtype for when AMP is used
+        self.k = self.k.to(k.dtype)
+        self.v = self.v.to(v.dtype)
+        # update the cache
+        k = self.k.index_copy_(2, input_pos, k)
+        v = self.v.index_copy_(2, input_pos, v)
+        return k, v

tsai_gpt/packed_dataset.py

@@ -0,0 +1,254 @@
+# Very loosely inspired by indexed_dataset in Fairseq, Megatron
+# https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/data/indexed_dataset.py
+
+
+import os
+import random
+import struct
+
+import numpy as np
+import torch
+from torch.utils.data import IterableDataset, get_worker_info
+
+dtypes = {
+    1: np.uint8,
+    2: np.int8,
+    3: np.int16,
+    4: np.int32,
+    5: np.int64,
+    6: np.float32,
+    7: np.float64,
+    8: np.uint16,
+}
+
+
+def code(dtype):
+    for k in dtypes:
+        if dtypes[k] == dtype:
+            return k
+    raise ValueError(dtype)
+
+
+HDR_MAGIC = b"LITPKDS"
+HDR_SIZE = 24  # bytes
+
+
+class PackedDataset(IterableDataset):
+    def __init__(
+        self,
+        filenames,
+        n_chunks,
+        block_size,
+        seed=12345,
+        shuffle=True,
+        wrap=False,
+        num_processes=1,
+        process_rank=0,
+    ):
+        self._filenames = filenames
+        self._n_chunks = n_chunks
+        self._block_size = block_size
+        self._seed = seed
+        self._shuffle = shuffle
+        self._wrap = wrap
+        self._num_processes = num_processes
+        self._process_rank = process_rank
+
+    def __iter__(self):
+        worker_info = get_worker_info()
+        num_workers = worker_info.num_workers if worker_info is not None else 1
+        worker_id = worker_info.id if worker_info is not None else 0
+        num_shards = num_workers * self._num_processes
+        shard_id = self._process_rank * num_workers + worker_id
+
+        max_num_files = len(self._filenames) // num_shards * num_shards
+        filenames = self._filenames[shard_id:max_num_files:num_shards]
+
+        return PackedDatasetIterator(
+            filenames=filenames,
+            n_chunks=self._n_chunks,
+            block_size=self._block_size,
+            seed=self._seed,
+            shuffle=self._shuffle,
+            wrap=self._wrap,
+        )
+
+
+class PackedDatasetBuilder(object):
+    def __init__(self, outdir, prefix, chunk_size, sep_token, dtype="auto", vocab_size=None):
+        if dtype == "auto":
+            if vocab_size is None:
+                raise ValueError("vocab_size cannot be None when dtype='auto'")
+            if vocab_size is not None and vocab_size < 65500:
+                self._dtype = np.uint16
+            else:
+                self._dtype = np.int32
+        else:
+            self._dtype = dtype
+        self._counter = 0
+        self._chunk_size = chunk_size
+        self._outdir = outdir
+        self._prefix = prefix
+        self._sep_token = sep_token
+        self._arr = np.zeros(self._chunk_size, dtype=self._dtype)
+        self._arr.fill(self._sep_token)
+        self._idx = 0
+        self._version = 1
+        self._filenames = []
+
+    def _write_chunk(self):
+        filename = f"{self._prefix}_{self._counter:010d}.bin"
+        filename = os.path.join(self._outdir, filename)
+
+        with open(filename, "wb") as f:
+            f.write(HDR_MAGIC)
+            f.write(struct.pack("<Q", self._version))
+            f.write(struct.pack("<B", code(self._dtype)))
+            f.write(struct.pack("<Q", self._chunk_size))
+            f.write(self._arr.tobytes(order="C"))
+
+        self._filenames.append(filename)
+        self._counter += 1
+        self._arr.fill(self._sep_token)
+        self._idx = 0
+
+    @property
+    def dtype(self):
+        return self._dtype
+
+    @property
+    def filenames(self):
+        return self._filenames.copy()
+
+    def add_array(self, arr):
+        while self._idx + arr.shape[0] > self._chunk_size:
+            part_len = self._chunk_size - self._idx
+            self._arr[self._idx : self._idx + part_len] = arr[:part_len]
+            self._write_chunk()
+            arr = arr[part_len:]
+
+        arr_len = arr.shape[0]
+        self._arr[self._idx : self._idx + arr_len] = arr
+        self._idx += arr_len
+
+    def write_reminder(self):
+        self._write_chunk()
+
+
+class PackedDatasetIterator:
+    def __init__(self, filenames, n_chunks, block_size, seed, shuffle, wrap):
+        self._seed = seed
+        self._shuffle = shuffle
+        self._rng = np.random.default_rng(seed) if shuffle else None
+        self._block_idxs = None
+
+        self._wrap = wrap
+
+        # TODO: instead of filenames, we could have a single text stream
+        #       (or text file) with the sequence of all files to be
+        #       fetched/loaded.
+        self._filenames = filenames
+        self._file_idx = 0
+
+        self._n_chunks = n_chunks
+
+        self._dtype = None
+        self._block_size = block_size
+        self._n_blocks = None
+
+        self._mmaps = []
+        self._buffers = []
+
+        self._block_idxs = []
+        self._curr_idx = 0
+
+        self._load_n_chunks()
+
+    def _read_header(self, path):
+        with open(path, "rb") as f:
+            magic = f.read(len(HDR_MAGIC))
+            assert magic == HDR_MAGIC, "File doesn't match expected format."
+            version = struct.unpack("<Q", f.read(8))
+            assert version == (1,)
+            (dtype_code,) = struct.unpack("<B", f.read(1))
+            dtype = dtypes[dtype_code]
+            (chunk_size,) = struct.unpack("<Q", f.read(8))
+        return dtype, chunk_size
+
+    def _close_mmaps(self):
+        for mmap in self._mmaps:
+            mmap._mmap.close()
+
+    def _load_n_chunks(self):
+        self._close_mmaps()
+        self._mmaps = []
+        self._buffers = []
+
+        if self._n_chunks > len(self._filenames[self._file_idx :]):
+            if not self._wrap:
+                raise StopIteration
+            self._file_idx = 0
+
+        for i in range(self._n_chunks):
+            filename = self._filenames[self._file_idx + i]
+            if self._dtype is None:
+                self._dtype, self._chunk_size = self._read_header(filename)
+                self._n_blocks = self._chunk_size // self._block_size
+            # TODO: check header matches with previous files
+            mmap = np.memmap(filename, mode="r", order="C", offset=HDR_SIZE)
+            self._mmaps.append(mmap)
+            self._buffers.append(memoryview(mmap))
+
+        self._file_idx += self._n_chunks
+        n_all_blocks = self._n_chunks * self._n_blocks
+
+        self._block_idxs = (
+            self._rng.permutation(n_all_blocks) if self._shuffle else range(n_all_blocks)
+        )
+
+        self._curr_idx = 0
+
+    def __del__(self):
+        self._close_mmaps()
+        del self._mmaps
+        del self._buffers
+
+    def __iter__(self):
+        return self
+
+    def __next__(self):
+        if self._curr_idx >= len(self._block_idxs):
+            self._load_n_chunks()
+            # TODO: trigger fetching next next n_chunks if remote
+        block_idx = self._block_idxs[self._curr_idx]
+        chunk_id = block_idx // self._n_blocks
+        buffer = self._buffers[chunk_id]
+        elem_id = (block_idx % self._n_blocks) * self._block_size
+        offset = np.dtype(self._dtype).itemsize * elem_id
+        arr = np.frombuffer(buffer, dtype=self._dtype, count=self._block_size, offset=offset)
+        self._curr_idx += 1
+        return torch.from_numpy(arr.astype(np.int64))
+
+
+class CombinedDataset(IterableDataset):
+    def __init__(self, datasets, seed, weights=None):
+        self._seed = seed
+        self._datasets = datasets
+        self._weights = weights
+        n_datasets = len(datasets)
+        if weights is None:
+            self._weights = [1 / n_datasets] * n_datasets
+
+    def __iter__(self):
+        return CombinedDatasetIterator(self._datasets, self._seed, self._weights)
+
+
+class CombinedDatasetIterator:
+    def __init__(self, datasets, seed, weights):
+        self._datasets = [iter(el) for el in datasets]
+        self._weights = weights
+        self._rng = random.Random(seed)
+
+    def __next__(self):
+        (dataset,) = self._rng.choices(self._datasets, weights=self._weights, k=1)
+        return next(dataset)

tsai_gpt/rmsnorm.py

@@ -0,0 +1,26 @@
+import torch
+
+
+class RMSNorm(torch.nn.Module):
+    """Root Mean Square Layer Normalization.
+
+    Derived from https://github.com/bzhangGo/rmsnorm/blob/master/rmsnorm_torch.py. BSD 3-Clause License:
+    https://github.com/bzhangGo/rmsnorm/blob/master/LICENSE.
+    """
+
+    def __init__(self, size: int, dim: int = -1, eps: float = 1e-5) -> None:
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.ones(size))
+        self.eps = eps
+        self.dim = dim
+
+    def forward(self, x: torch.Tensor) -> torch.Tensor:
+        dtype = x.dtype
+        x = x.float()
+        # NOTE: the original RMSNorm paper implementation is not equivalent
+        norm_x = torch.mean(x * x, dim=self.dim, keepdim=True)
+        x_normed = x * torch.rsqrt(norm_x + self.eps)
+        return (self.weight * x_normed).to(dtype=dtype)
+
+    def reset_parameters(self) -> None:
+        torch.nn.init.ones_(self.weight)

tsai_gpt/speed_monitor.py

@@ -0,0 +1,438 @@
+import time
+from collections import deque
+from contextlib import nullcontext
+from typing import Any, Callable, Deque, Dict, Optional
+
+import torch
+from lightning import Callback, Fabric, LightningModule, Trainer
+from lightning.fabric.accelerators.xla import _XLA_GREATER_EQUAL_2_1
+from lightning.fabric.plugins import (BitsandbytesPrecision, DoublePrecision,
+                                      FSDPPrecision, HalfPrecision,
+                                      MixedPrecision, Precision,
+                                      TransformerEnginePrecision, XLAPrecision)
+from lightning.fabric.utilities.rank_zero import \
+    rank_zero_only as fabric_rank_zero_only
+from lightning.pytorch.plugins import (DoublePrecisionPlugin,
+                                       FSDPPrecisionPlugin,
+                                       HalfPrecisionPlugin,
+                                       MixedPrecisionPlugin,
+                                       XLAPrecisionPlugin)
+from lightning.pytorch.utilities.rank_zero import \
+    rank_zero_only as trainer_rank_zero_only
+from torch.utils.flop_counter import FlopCounterMode
+
+from tsai_gpt import GPT
+from tsai_gpt.utils import num_parameters
+
+GPU_AVAILABLE_FLOPS = {
+    # source: https://resources.nvidia.com/en-us-tensor-core/nvidia-tensor-core-gpu-datasheet
+    # nvidia publishes spec sheet with a 2x sparsity factor
+    "h100-sxm": {
+        torch.float64: 67e12,
+        torch.float32: 67e12,
+        torch.bfloat16: 1.979e15 / 2,
+        torch.float16: 1.979e15 / 2,
+        torch.int8: 3.958e15 / 2,
+    },
+    "h100-pcie": {
+        torch.float64: 51e12,
+        torch.float32: 51e12,
+        torch.bfloat16: 1.513e15 / 2,
+        torch.float16: 1.513e15 / 2,
+        torch.int8: 3.026e15 / 2,
+    },
+    # source: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/nvidia-a100-datasheet-us-nvidia-1758950-r4-web.pdf
+    # sxm and pcie have same flop counts
+    "a100": {
+        torch.float64: 19.5e12,
+        torch.float32: 19.5e12,
+        torch.bfloat16: 312e12,
+        torch.float16: 312e12,
+    },
+    # source: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a10/pdf/a10-datasheet.pdf
+    "a10g": {torch.float32: 31.2e12, torch.bfloat16: 125e12, torch.float16: 125e12},
+    # source: https://images.nvidia.com/content/technologies/volta/pdf/volta-v100-datasheet-update-us-1165301-r5.pdf
+    "v100-sxm": {torch.float64: 7.8e12, torch.float32: 15.7e12, torch.float16: 125e12},
+    "v100-pcie": {torch.float64: 7e12, torch.float32: 14e12, torch.float16: 112e12},
+    "v100s-pcie": {torch.float64: 8.2e12, torch.float32: 16.4e12, torch.float16: 130e12},
+    # source: https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/tesla-t4/t4-tensor-core-datasheet-951643.pdf
+    # sxm and pcie have same flop counts
+    "t4": {torch.float32: 8.1e12, torch.float16: 65e12, torch.int8: 130e12},
+    # https://www.nvidia.com/content/dam/en-zz/Solutions/design-visualization/quadro-product-literature/quadro-rtx-5000-data-sheet-us-nvidia-704120-r4-web.pdf
+    "quadro rtx 5000": {torch.float32: 11.2e12, torch.float16: 89.2e12},
+}
+
+TPU_AVAILABLE_FLOPS = {
+    # flop count for each TPU generation is the same for all precisions
+    # since bfloat16 precision is always used for performing matrix operations
+    # for more info: https://cloud.google.com/tpu/docs/bfloat16#choosing_bfloat16
+    # source: https://arxiv.org/pdf/1907.10701.pdf
+    "v2": 45e12,
+    # source: https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#tpu_v3
+    "v3": 123e12,
+    # source: https://cloud.google.com/tpu/docs/system-architecture-tpu-vm#tpu_v4
+    "v4": 275e12,
+    # source: https://cloud.google.com/tpu/docs/v5e-training
+    "v5litepod": 197e12,
+}
+
+
+def get_flops_available(device: torch.device, dtype: torch.dtype) -> Optional[float]:
+    if device.type == "cuda":
+        device_name = torch.cuda.get_device_name(device).lower()
+        if "h100" in device_name and "hbm3" in device_name:
+            device_name = "h100-sxm"
+        elif "h100" in device_name and ("pcie" in device_name or "hbm2e" in device_name):
+            device_name = "h100-pcie"
+        elif "a100" in device_name:
+            device_name = "a100"
+        elif "a10g" in device_name:
+            device_name = "a10g"
+        elif "v100-sxm" in device_name:
+            device_name = "v100-sxm"
+        elif "v100-pcie" in device_name:
+            device_name = "v100-pcie"
+        elif "t4" in device_name:
+            device_name = "t4"
+        elif "quadro rtx 5000" in device_name:
+            device_name = "quadro rtx 5000"
+        else:
+            device_name = None
+
+        if device_name is not None:
+            try:
+                return int(GPU_AVAILABLE_FLOPS[device_name][dtype])
+            except KeyError:
+                raise KeyError(
+                    f"flop count not found for {device_name} with dtype: {dtype}; "
+                    "MFU cannot be calculated and reported."
+                )
+    elif device.type == "xla":
+        if _XLA_GREATER_EQUAL_2_1:
+            from torch_xla._internal import tpu
+        else:
+            from torch_xla.experimental import tpu
+
+        device_name = tpu.get_tpu_env()["TYPE"].lower()
+        try:
+            return int(TPU_AVAILABLE_FLOPS[device_name])
+        except KeyError:
+            raise KeyError(
+                f"flop count not found for {device_name} with dtype: {dtype}; MFU cannot be calculated and reported."
+            )
+
+    return None
+
+
+# Adapted from https://github.com/mosaicml/composer/blob/f2a2dc820cb75023b9eb7c46fdfd25273712abd0/composer/callbacks/speed_monitor.py
+
+
+class SpeedMonitorBase:
+    """Logs the training throughput and utilization.
+
+    +-------------------------------------+-----------------------------------------------------------+
+    | Key                                 | Logged data                                               |
+    +=====================================+===========================================================+
+    |                                     | Rolling average (over `window_size` most recent           |
+    | `throughput/batches_per_sec`        | batches) of the number of batches processed per second    |
+    |                                     |                                                           |
+    +-------------------------------------+-----------------------------------------------------------+
+    |                                     | Rolling average (over `window_size` most recent           |
+    | `throughput/samples_per_sec`        | batches) of the number of samples processed per second    |
+    |                                     |                                                           |
+    +-------------------------------------+-----------------------------------------------------------+
+    |                                     | Rolling average (over `window_size` most recent           |
+    | `throughput/tokens_per_sec`         | batches) of the number of tokens processed per second.    |
+    |                                     | This may include padding depending on dataset             |
+    +-------------------------------------+-----------------------------------------------------------+
+    |                                     | Estimates flops by `flops_per_batch * batches_per_sec`    |
+    | `throughput/flops_per_sec`          |                                                           |
+    |                                     |                                                           |
+    +-------------------------------------+-----------------------------------------------------------+
+    | `throughput/device/batches_per_sec` | `throughput/batches_per_sec` divided by world size        |
+    +-------------------------------------+-----------------------------------------------------------+
+    | `throughput/device/samples_per_sec` | `throughput/samples_per_sec` divided by world size        |
+    +-------------------------------------+-----------------------------------------------------------+
+    |                                     | `throughput/tokens_per_sec` divided by world size. This   |
+    | `throughput/device/tokens_per_sec`  | may include pad tokens depending on dataset               |
+    |                                     |                                                           |
+    +-------------------------------------+-----------------------------------------------------------+
+    |                                     | `throughput/flops_per_sec` divided by world size. Only    |
+    | `throughput/device/flops_per_sec`   | logged when model has attribute `flops_per_batch`         |
+    |                                     |                                                           |
+    +-------------------------------------+-----------------------------------------------------------+
+    |                                     | `throughput/device/flops_per_sec` divided by world size.  |
+    | `throughput/device/mfu`             |                                                           |
+    |                                     |                                                           |
+    +-------------------------------------+-----------------------------------------------------------+
+    | `time/train`                        | Total elapsed training time                               |
+    +-------------------------------------+-----------------------------------------------------------+
+    | `time/val`                          | Total elapsed validation time                             |
+    +-------------------------------------+-----------------------------------------------------------+
+    | `time/total`                        | Total elapsed time (time/train + time/val)                |
+    +-------------------------------------+-----------------------------------------------------------+
+
+    Notes:
+        - The implementation assumes that devices are homogeneous as it normalizes by the world size.
+        - Tokens/sec, flops/sec and MFU do not account for padding tokens if present. We suggest using samples/sec or
+          batches/sec to measure throughput under this circumstance.
+        - Be careful when comparing MFU numbers across projects, as this will highly depend on the ``flops_per_batch``.
+          There is no widespread, realistic, and reliable implementation to compute them.
+          We suggest using our ``measure_flops`` function, but many other works will use ``estimated_flops`` which
+          will almost always be an overestimate when compared to the true value.
+
+    Args:
+        window_size (int, optional): Number of batches to use for a rolling average of throughput.
+            Defaults to 100.
+        time_unit (str, optional): Time unit to use for `time` logging. Can be one of
+            'seconds', 'minutes', 'hours', or 'days'. Defaults to 'hours'.
+    """
+
+    def __init__(
+        self,
+        flops_available: float,
+        log_dict: Callable[[Dict, int], None],
+        window_size: int = 100,
+        time_unit: str = "hours",
+    ):
+        self.flops_available = flops_available
+        self.log_dict = log_dict
+
+        # Track the batch num samples and wct to compute throughput over a window of batches
+        self.history_samples: Deque[int] = deque(maxlen=window_size + 1)
+        self.history_wct: Deque[float] = deque(maxlen=window_size + 1)
+        self.history_lengths: Deque[int] = deque(maxlen=window_size + 1)
+        self.history_flops: Deque[int] = deque(maxlen=window_size + 1)
+
+        self.divider = 1
+        if time_unit == "seconds":
+            self.divider = 1
+        elif time_unit == "minutes":
+            self.divider = 60
+        elif time_unit == "hours":
+            self.divider = 60 * 60
+        elif time_unit == "days":
+            self.divider = 60 * 60 * 24
+        else:
+            raise ValueError(
+                f'Invalid time_unit: {time_unit}. Must be one of "seconds", "minutes", "hours", or "days".'
+            )
+
+        # Keep track of time spent evaluating
+        self.total_eval_wct = 0.0
+        self.step = -1
+
+    def on_train_batch_end(
+        self,
+        samples: int,  # total samples seen (per device)
+        train_elapsed: float,  # total training time (seconds)
+        world_size: int,
+        flops_per_batch: Optional[int] = None,  # (per device)
+        lengths: Optional[int] = None,  # total length of the samples seen (per device)
+    ) -> None:
+        self.step += 1
+        step = self.step
+        metrics = {}
+
+        self.history_samples.append(samples)
+        if lengths is not None:
+            self.history_lengths.append(lengths)
+            # if lengths are passed, there should be as many values as samples
+            assert len(self.history_samples) == len(self.history_lengths)
+        self.history_wct.append(train_elapsed)
+        if len(self.history_wct) == self.history_wct.maxlen:
+            elapsed_batches = len(self.history_samples) - 1
+            elapsed_samples = self.history_samples[-1] - self.history_samples[0]
+            elapsed_wct = self.history_wct[-1] - self.history_wct[0]
+            samples_per_sec = elapsed_samples * world_size / elapsed_wct
+            dev_samples_per_sec = elapsed_samples / elapsed_wct
+            metrics.update(
+                {
+                    "throughput/batches_per_sec": elapsed_batches * world_size / elapsed_wct,
+                    "throughput/samples_per_sec": samples_per_sec,
+                    "throughput/device/batches_per_sec": elapsed_batches / elapsed_wct,
+                    "throughput/device/samples_per_sec": dev_samples_per_sec,
+                }
+            )
+            if lengths is not None:
+                elapsed_lengths = int(self.history_lengths[-1]) - int(self.history_lengths[0])
+                avg_length = elapsed_lengths / elapsed_batches
+                metrics.update(
+                    {
+                        "throughput/tokens_per_sec": samples_per_sec * avg_length,
+                        "throughput/device/tokens_per_sec": dev_samples_per_sec * avg_length,
+                    }
+                )
+
+        if flops_per_batch is not None:
+            # sum of flops per batch across ranks
+            self.history_flops.append(flops_per_batch * world_size)
+        if len(self.history_flops) == self.history_flops.maxlen:
+            elapsed_flops = sum(self.history_flops) - self.history_flops[0]
+            elapsed_wct = self.history_wct[-1] - self.history_wct[0]
+            flops_per_sec = elapsed_flops / elapsed_wct
+            device_flops_per_sec = flops_per_sec / world_size
+            metrics.update(
+                {
+                    "throughput/flops_per_sec": flops_per_sec,
+                    "throughput/device/flops_per_sec": device_flops_per_sec,
+                }
+            )
+            if self.flops_available:
+                metrics["throughput/device/mfu"] = device_flops_per_sec / self.flops_available
+
+        metrics.update(
+            {
+                "time/train": train_elapsed / self.divider,
+                "time/val": self.total_eval_wct / self.divider,
+                "time/total": (train_elapsed + self.total_eval_wct) / self.divider,
+                "samples": samples,
+            }
+        )
+
+        self.log_dict(metrics, step)
+
+    def eval_end(self, eval_elapsed: float) -> None:
+        self.total_eval_wct += eval_elapsed  # seconds
+
+
+def plugin_to_compute_dtype(plugin: Precision) -> torch.dtype:
+    if isinstance(plugin, BitsandbytesPrecision):
+        return plugin.dtype
+    if isinstance(plugin, (HalfPrecision, MixedPrecision, HalfPrecisionPlugin)):
+        return plugin._desired_input_dtype
+    if isinstance(plugin, MixedPrecisionPlugin):
+        return torch.bfloat16 if plugin.precision == "bf16-mixed" else torch.half
+    if isinstance(plugin, (DoublePrecision, DoublePrecisionPlugin)):
+        return torch.double
+    if isinstance(plugin, (XLAPrecision, XLAPrecisionPlugin)):
+        return plugin._desired_dtype
+    if isinstance(plugin, TransformerEnginePrecision):
+        return torch.int8
+    if isinstance(plugin, (FSDPPrecision, FSDPPrecisionPlugin)):
+        return plugin.mixed_precision_config.reduce_dtype
+    if isinstance(plugin, Precision):
+        return torch.float32
+    raise NotImplementedError(plugin)
+
+
+class SpeedMonitorFabric(SpeedMonitorBase):
+    def __init__(self, fabric: Fabric, *args: Any, **kwargs: Any) -> None:
+        dtype = plugin_to_compute_dtype(fabric.strategy.precision)
+        flops_available = get_flops_available(fabric.device, dtype)
+        super().__init__(flops_available, fabric.log_dict, *args, **kwargs)
+
+    @fabric_rank_zero_only
+    def on_train_batch_end(self, *args: Any, **kwargs: Any) -> None:
+        super().on_train_batch_end(*args, **kwargs)
+
+
+class SpeedMonitorCallback(Callback):
+    def __init__(self, length_fn: Callable[[Any], int], batch_size: int, **kwargs: Any) -> None:
+        super().__init__()
+        self.speed_monitor: Optional[SpeedMonitorBase] = None
+        self.speed_monitor_kwargs = kwargs
+        self.length_fn = length_fn
+        self.batch_size = batch_size
+        self.eval_t0: int = 0
+        self.train_t0: int = 0
+        self.total_lengths: int = 0
+
+    def setup(self, trainer: Trainer, pl_module: LightningModule, stage: str) -> None:
+        if self.speed_monitor is not None:
+            return  # already setup
+        dtype = plugin_to_compute_dtype(trainer.precision_plugin)
+        flops_available = get_flops_available(trainer.strategy.root_device, dtype)
+        self.speed_monitor = SpeedMonitorBase(
+            flops_available, trainer.logger.log_metrics, **self.speed_monitor_kwargs
+        )
+
+    @trainer_rank_zero_only
+    def on_train_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
+        if trainer.fit_loop._should_accumulate():
+            return
+
+        self.train_t0 = time.perf_counter()
+
+    @trainer_rank_zero_only
+    def on_train_batch_end(
+        self,
+        trainer: Trainer,
+        pl_module: LightningModule,
+        outputs: Any,
+        batch: Any,
+        batch_idx: int,
+    ) -> None:
+        self.total_lengths += self.length_fn(batch)
+        if trainer.fit_loop._should_accumulate():
+            return
+        train_elapsed = time.perf_counter() - self.train_t0
+        assert self.speed_monitor is not None
+        iter_num = trainer.fit_loop.total_batch_idx
+        assert (measured_flops := pl_module.measured_flops) is not None
+        self.speed_monitor.on_train_batch_end(
+            (iter_num + 1) * self.batch_size,
+            train_elapsed,
+            # this assumes that device FLOPs are the same and that all devices have the same batch size
+            trainer.world_size,
+            flops_per_batch=measured_flops,
+            lengths=self.total_lengths,
+        )
+
+    @trainer_rank_zero_only
+    def on_validation_start(self, trainer: Trainer, pl_module: LightningModule) -> None:
+        self.eval_t0 = time.perf_counter()
+
+    @trainer_rank_zero_only
+    def on_validation_end(self, trainer: Trainer, pl_module: LightningModule) -> None:
+        eval_elapsed = time.perf_counter() - self.eval_t0
+        assert self.speed_monitor is not None
+        self.speed_monitor.eval_end(eval_elapsed)
+
+
+def flops_per_param(max_seq_length: int, n_layer: int, n_embd: int, n_params: int) -> int:
+    flops_per_token = (
+        2 * n_params
+    )  # each parameter is used for a MAC (2 FLOPS) per network operation
+    # this assumes that all samples have a fixed length equal to the block size
+    # which is most likely false during finetuning
+    flops_per_seq = flops_per_token * max_seq_length
+    attn_flops_per_seq = n_layer * 2 * 2 * (n_embd * (max_seq_length**2))
+    return flops_per_seq + attn_flops_per_seq
+
+
+def estimate_flops(model: GPT) -> int:
+    """Measures estimated FLOPs for MFU.
+
+    Refs:
+        * https://ar5iv.labs.arxiv.org/html/2205.05198#A1
+        * https://ar5iv.labs.arxiv.org/html/2204.02311#A2
+    """
+    # using all parameters for this is a naive over estimation because not all model parameters actually contribute to
+    # this FLOP computation (e.g. embedding, norm). For this reason, the result will be higher by a fixed percentage
+    # (~10%) compared to the measured FLOPs, making those lower but more realistic.
+    # For a proper estimate, this needs a more fine-grained calculation as in Appendix A of the paper.
+    n_trainable_params = num_parameters(model, requires_grad=True)
+    trainable_flops = flops_per_param(
+        model.max_seq_length, model.config.n_layer, model.config.n_embd, n_trainable_params
+    )
+    # forward + backward + gradients (assumes no gradient accumulation)
+    ops_per_step = 3 if model.training else 1
+    n_frozen_params = num_parameters(model, requires_grad=False)
+    frozen_flops = flops_per_param(
+        model.max_seq_length, model.config.n_layer, model.config.n_embd, n_frozen_params
+    )
+    # forward + backward
+    frozen_ops_per_step = 2 if model.training else 1
+    return ops_per_step * trainable_flops + frozen_ops_per_step * frozen_flops
+
+
+def measure_flops(model: GPT, x: torch.Tensor) -> int:
+    """Measures real FLOPs for HFU"""
+    flop_counter = FlopCounterMode(model, display=False)
+    ctx = nullcontext() if model.training else torch.no_grad()
+    with ctx, flop_counter:
+        y = model(x)
+        if model.training:
+            y.sum().backward()
+    return flop_counter.get_total_flops()

tsai_gpt/tokenizer.py

@@ -0,0 +1,106 @@
+import json
+from pathlib import Path
+from typing import Optional
+
+import torch
+
+
+class Tokenizer:
+    def __init__(self, checkpoint_dir: Path) -> None:
+        self.use_bos = self.check_if_bos_token_used(checkpoint_dir)
+        self.bos_id = None
+        self.eos_id = None
+
+        # some checkpoints have both files, `.model` takes precedence
+        if (vocabulary_path := checkpoint_dir / "tokenizer.model").is_file():
+            from sentencepiece import SentencePieceProcessor
+
+            self.processor = SentencePieceProcessor(model_file=str(vocabulary_path))
+            self.backend = "sentencepiece"
+            self.bos_id = self.processor.bos_id()
+            self.eos_id = self.processor.eos_id()
+
+        elif (vocabulary_path := checkpoint_dir / "tokenizer.json").is_file():
+            from tokenizers import Tokenizer as HFTokenizer
+
+            self.processor = HFTokenizer.from_file(str(vocabulary_path))
+            self.backend = "huggingface"
+
+            if (special_tokens_path := checkpoint_dir / "tokenizer_config.json").is_file():
+                with open(special_tokens_path) as fp:
+                    config = json.load(fp)
+                bos_token = config.get("bos_token")
+                self.bos_id = self.token_to_id(bos_token) if bos_token is not None else None
+                eos_token = config.get("eos_token")
+                self.eos_id = self.token_to_id(eos_token) if eos_token is not None else None
+            if (special_tokens_path := checkpoint_dir / "generation_config.json").is_file():
+                with open(special_tokens_path) as fp:
+                    config = json.load(fp)
+                if self.bos_id is None:
+                    self.bos_id = config.get("bos_token_id")
+                if self.eos_id is None:
+                    self.eos_id = config.get("eos_token_id")
+        else:
+            raise NotImplementedError
+
+    @property
+    def vocab_size(self) -> int:
+        if self.backend == "huggingface":
+            return self.processor.get_vocab_size(with_added_tokens=False)
+        if self.backend == "sentencepiece":
+            return self.processor.vocab_size()
+        raise RuntimeError
+
+    def token_to_id(self, token: str) -> int:
+        if self.backend == "huggingface":
+            id_ = self.processor.token_to_id(token)
+        elif self.backend == "sentencepiece":
+            id_ = self.processor.piece_to_id(token)
+        else:
+            raise RuntimeError
+        if id_ is None:
+            raise ValueError(f"token {token!r} not found in the collection.")
+        return id_
+
+    def check_if_bos_token_used(self, checkpoint_dir: Path) -> bool:
+        if not (tokenizer_config_path := checkpoint_dir / "tokenizer_config.json").is_file():
+            return False
+        with open(tokenizer_config_path) as fp:
+            config = json.load(fp)
+        if any(config.get(check, False) for check in ("add_bos_token", "add_prefix_space")):
+            return True
+        # for examples that also use the Llama tokenizer, but do not have or set add_bos_token to True.
+        # ex: https://huggingface.co/stabilityai/StableBeluga2/blob/main/tokenizer_config.json#L2
+        return (
+            config.get("add_bos_token") is None
+            and config.get("tokenizer_class") == "LlamaTokenizer"
+        )
+
+    def encode(
+        self,
+        string: str,
+        device: Optional[torch.device] = None,
+        bos: Optional[bool] = None,
+        eos: bool = False,
+        max_length: int = -1,
+    ) -> torch.Tensor:
+        if self.backend == "huggingface":
+            tokens = self.processor.encode(string).ids
+        elif self.backend == "sentencepiece":
+            tokens = self.processor.encode(string)
+        else:
+            raise RuntimeError
+        if bos or (bos is None and self.use_bos):
+            bos_id = self.bos_id
+            if bos_id is None:
+                raise NotImplementedError("This tokenizer does not have a defined a bos token")
+            tokens = [bos_id] + tokens
+        if eos:
+            tokens = tokens + [self.eos_id]
+        if max_length > 0:
+            tokens = tokens[:max_length]
+        return torch.tensor(tokens, dtype=torch.int, device=device)
+
+    def decode(self, tensor: torch.Tensor) -> str:
+        tokens = [tensor.item()] if tensor.ndim == 0 else tensor.tolist()
+        return self.processor.decode(tokens)

tsai_gpt/utils.py

@@ -0,0 +1,367 @@
+"""Utility functions for training and inference."""
+import math
+import pickle
+import sys
+from contextlib import nullcontext
+from io import BytesIO
+from pathlib import Path
+from typing import (TYPE_CHECKING, ContextManager, Dict, List, Mapping,
+                    Optional, TypeVar, Union)
+
+import lightning as L
+import torch
+import torch.nn as nn
+import torch.utils._device
+from lightning.fabric.strategies import FSDPStrategy
+from lightning.fabric.utilities.load import _lazy_load as lazy_load
+from torch.serialization import normalize_storage_type
+
+if TYPE_CHECKING:
+    from model import GPT
+
+
+def find_multiple(n: int, k: int) -> int:
+    assert k > 0
+    if n % k == 0:
+        return n
+    return n + k - (n % k)
+
+
+def num_parameters(module: nn.Module, requires_grad: Optional[bool] = None) -> int:
+    total = 0
+    for p in module.parameters():
+        if requires_grad is None or p.requires_grad == requires_grad:
+            if hasattr(p, "quant_state"):
+                # bitsandbytes 4bit layer support
+                total += math.prod(p.quant_state[1])
+            else:
+                total += p.numel()
+    return total
+
+
+def gptq_quantization(enabled: bool = False) -> ContextManager:
+    if not enabled:
+        return nullcontext()
+
+    from lightning.fabric.plugins.precision.utils import \
+        _ClassReplacementContextManager
+    from quantize.gptq import ColBlockQuantizedLinear
+
+    class QuantizedLinear(ColBlockQuantizedLinear):
+        def __init__(self, *args, **kwargs):
+            super().__init__(*args, bits=4, tile_cols=-1, **kwargs)
+
+    return _ClassReplacementContextManager({"torch.nn.Linear": QuantizedLinear})
+
+
+def check_valid_checkpoint_dir(checkpoint_dir: Path) -> None:
+    files = {
+        "lit_model.pth": (checkpoint_dir / "lit_model.pth").is_file(),
+        "lit_config.json": (checkpoint_dir / "lit_config.json").is_file(),
+        "tokenizer.json OR tokenizer.model": (checkpoint_dir / "tokenizer.json").is_file()
+        or (checkpoint_dir / "tokenizer.model").is_file(),
+        "tokenizer_config.json": (checkpoint_dir / "tokenizer_config.json").is_file(),
+    }
+    if checkpoint_dir.is_dir():
+        if all(files.values()):
+            # we're good
+            return
+        problem = f" is missing the files: {[f for f, exists in files.items() if not exists]!r}"
+    else:
+        problem = " is not a checkpoint directory"
+
+    # list locally available checkpoints
+    available = list(Path("checkpoints").glob("*/*"))
+    if available:
+        options = "\n --checkpoint_dir ".join([""] + [repr(str(p.resolve())) for p in available])
+        extra = f"\nYou have downloaded locally:{options}\n"
+    else:
+        extra = ""
+
+    error_message = (
+        f"--checkpoint_dir {str(checkpoint_dir.absolute())!r}{problem}."
+        "\nFind download instructions at https://github.com/Lightning-AI/lit-gpt/blob/main/tutorials\n"
+        f"{extra}\nSee all download options by running:\n python scripts/download.py"
+    )
+    print(error_message, file=sys.stderr)
+    raise SystemExit(1)
+
+
+class SavingProxyForStorage:
+    def __init__(self, obj, saver, protocol_version=5):
+        self.protocol_version = protocol_version
+        self.saver = saver
+        if not (isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj)):
+            raise TypeError(f"expected storage, not {type(obj)}")
+
+        # this logic is taken from PyTorch 2.0+ torch/serialization.py
+        if isinstance(obj, torch.storage.TypedStorage):
+            # PT upstream wants to deprecate this eventually...
+            storage = obj._untyped_storage
+            storage_type_str = obj._pickle_storage_type()
+            storage_type = getattr(torch, storage_type_str)
+            storage_numel = obj._size()
+        else:
+            storage = obj
+            storage_type = normalize_storage_type(type(obj))
+            storage_numel = storage.nbytes()
+
+        storage_key = saver._write_storage_and_return_key(storage)
+        location = torch.serialization.location_tag(storage)
+
+        self.storage_info = ("storage", storage_type, storage_key, location, storage_numel)
+
+    def __reduce_ex__(self, protocol_version):
+        assert False, "this should be handled with out of band"
+
+
+class SavingProxyForTensor:
+    def __init__(self, tensor, saver, protocol_version=5):
+        self.protocol_version = protocol_version
+        self.reduce_ret_fn, reduce_args = tensor.__reduce_ex__(protocol_version)
+        if reduce_args[0] == torch._utils._rebuild_tensor_v2:
+            # for Tensors with Python attributes
+            (a0, a1, (storage, *a2_other), *other_reduce_args) = reduce_args
+            assert isinstance(storage, torch.storage.TypedStorage), "Please check for updates"
+            storage_proxy = SavingProxyForStorage(
+                storage, saver, protocol_version=protocol_version
+            )
+            self.reduce_args = (a0, a1, (storage_proxy, *a2_other), *other_reduce_args)
+        else:
+            (storage, *other_reduce_args) = reduce_args
+            assert isinstance(storage, torch.storage.TypedStorage), "Please check for updates"
+            storage_proxy = SavingProxyForStorage(
+                storage, saver, protocol_version=protocol_version
+            )
+            self.reduce_args = (storage_proxy, *other_reduce_args)
+
+    def __reduce_ex__(self, protocol_version):
+        if protocol_version != self.protocol_version:
+            raise RuntimeError(
+                f"Unexpected protocol version: expected {self.protocol_version}, got {protocol_version}"
+            )
+        return self.reduce_ret_fn, self.reduce_args
+
+
+class IncrementalPyTorchPickler(pickle.Pickler):
+    def __init__(self, saver, *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.storage_dtypes = {}
+        self.saver = saver
+        self.id_map = {}
+
+    # this logic is taken from PyTorch 2.0+ torch/serialization.py
+    def persistent_id(self, obj):
+        # FIXME: the docs say that persistent_id should only return a string
+        # but torch store returns tuples. This works only in the binary protocol
+        # see
+        # https://docs.python.org/2/library/pickle.html#pickling-and-unpickling-external-objects
+        # https://github.com/python/cpython/blob/master/Lib/pickle.py#L527-L537
+        if isinstance(obj, SavingProxyForStorage):
+            return obj.storage_info
+
+        if isinstance(obj, torch.storage.TypedStorage) or torch.is_storage(obj):
+            if isinstance(obj, torch.storage.TypedStorage):
+                # TODO: Once we decide to break serialization FC, this case
+                # can be deleted
+                storage = obj._untyped_storage
+                storage_dtype = obj.dtype
+                storage_type_str = obj._pickle_storage_type()
+                storage_type = getattr(torch, storage_type_str)
+                storage_numel = obj._size()
+
+            else:
+                storage = obj
+                storage_dtype = torch.uint8
+                storage_type = normalize_storage_type(type(obj))
+                storage_numel = storage.nbytes()
+
+            # If storage is allocated, ensure that any other saved storages
+            # pointing to the same data all have the same dtype. If storage is
+            # not allocated, don't perform this check
+            if storage.data_ptr() != 0:
+                if storage.data_ptr() in self.storage_dtypes:
+                    if storage_dtype != self.storage_dtypes[storage.data_ptr()]:
+                        raise RuntimeError(
+                            "Cannot save multiple tensors or storages that view the same data as different types"
+                        )
+                else:
+                    self.storage_dtypes[storage.data_ptr()] = storage_dtype
+
+            storage_key = self.id_map.get(storage._cdata)
+            if storage_key is None:
+                storage_key = self.saver._write_storage_and_return_key(storage)
+                self.id_map[storage._cdata] = storage_key
+            location = torch.serialization.location_tag(storage)
+
+            return ("storage", storage_type, storage_key, location, storage_numel)
+
+        return None
+
+
+class incremental_save:
+    def __init__(self, name):
+        self.name = name
+        self.zipfile = torch._C.PyTorchFileWriter(str(name))
+        self.has_saved = False
+        self.next_key = 0
+
+    def __enter__(self):
+        return self
+
+    def store_early(self, tensor):
+        if isinstance(tensor, torch.Tensor):
+            return SavingProxyForTensor(tensor, self)
+        raise TypeError(f"can only store tensors early, not {type(tensor)}")
+
+    def save(self, obj):
+        if self.has_saved:
+            raise RuntimeError("have already saved")
+        # Write the pickle data for `obj`
+        data_buf = BytesIO()
+        pickler = IncrementalPyTorchPickler(self, data_buf, protocol=5)
+        pickler.dump(obj)
+        data_value = data_buf.getvalue()
+        self.zipfile.write_record("data.pkl", data_value, len(data_value))
+        self.has_saved = True
+
+    def _write_storage_and_return_key(self, storage):
+        if self.has_saved:
+            raise RuntimeError("have already saved")
+        key = self.next_key
+        self.next_key += 1
+        name = f"data/{key}"
+        if storage.device.type != "cpu":
+            storage = storage.cpu()
+        num_bytes = storage.nbytes()
+        self.zipfile.write_record(name, storage.data_ptr(), num_bytes)
+        return key
+
+    def __exit__(self, type, value, traceback):
+        self.zipfile.write_end_of_file()
+
+
+T = TypeVar("T")
+
+
+def chunked_cross_entropy(
+    logits: Union[torch.Tensor, List[torch.Tensor]], targets: torch.Tensor, chunk_size: int = 128
+) -> torch.Tensor:
+    # with large max_sequence_lengths, the beginning of `backward` allocates a large memory chunk which can dominate
+    # the memory usage in fine-tuning settings with low number of parameters.
+    # as a workaround hack, the cross entropy computation is chunked to force it to deallocate on the go, reducing
+    # the memory spike's magnitude
+
+    # lm_head was chunked (we are fine-tuning)
+    if isinstance(logits, list):
+        # don't want to chunk cross entropy
+        if chunk_size == 0:
+            logits = torch.cat(logits, dim=1)
+            logits = logits.reshape(-1, logits.size(-1))
+            targets = targets.reshape(-1)
+            return torch.nn.functional.cross_entropy(logits, targets, ignore_index=-1)
+
+        # chunk cross entropy
+        logit_chunks = [logit_chunk.reshape(-1, logit_chunk.size(-1)) for logit_chunk in logits]
+        target_chunks = [
+            target_chunk.reshape(-1) for target_chunk in targets.split(logits[0].size(1), dim=1)
+        ]
+        loss_chunks = [
+            torch.nn.functional.cross_entropy(
+                logit_chunk, target_chunk, ignore_index=-1, reduction="none"
+            )
+            for logit_chunk, target_chunk in zip(logit_chunks, target_chunks)
+        ]
+        return torch.cat(loss_chunks).mean()
+
+    # no chunking at all
+    logits = logits.reshape(-1, logits.size(-1))
+    targets = targets.reshape(-1)
+    if chunk_size == 0:
+        return torch.nn.functional.cross_entropy(logits, targets, ignore_index=-1)
+
+    # lm_head wasn't chunked, chunk cross entropy
+    logit_chunks = logits.split(chunk_size)
+    target_chunks = targets.split(chunk_size)
+    loss_chunks = [
+        torch.nn.functional.cross_entropy(
+            logit_chunk, target_chunk, ignore_index=-1, reduction="none"
+        )
+        for logit_chunk, target_chunk in zip(logit_chunks, target_chunks)
+    ]
+    return torch.cat(loss_chunks).mean()
+
+
+def map_old_state_dict_weights(state_dict: Dict, mapping: Mapping, prefix: str) -> Dict:
+    for checkpoint_name, attribute_name in mapping.items():
+        full_checkpoint_name = prefix + checkpoint_name
+        if full_checkpoint_name in state_dict:
+            full_attribute_name = prefix + attribute_name
+            state_dict[full_attribute_name] = state_dict.pop(full_checkpoint_name)
+    return state_dict
+
+
+def get_default_supported_precision(training: bool) -> str:
+    """Return default precision that is supported by the hardware: either `bf16` or `16`.
+
+    Args:
+        training: `-mixed` or `-true` version of the precision to use
+
+    Returns:
+        default precision that is suitable for the task and is supported by the hardware
+    """
+    from lightning.fabric.accelerators import MPSAccelerator
+
+    if MPSAccelerator.is_available() or (
+        torch.cuda.is_available() and not torch.cuda.is_bf16_supported()
+    ):
+        return "16-mixed" if training else "16-true"
+    return "bf16-mixed" if training else "bf16-true"
+
+
+def load_checkpoint(
+    fabric: L.Fabric, model: nn.Module, checkpoint_path: Path, strict: bool = True
+) -> None:
+    if isinstance(fabric.strategy, FSDPStrategy):
+        fabric.load_raw(checkpoint_path, model, strict=strict)
+    else:
+        state_dict = lazy_load(checkpoint_path)
+        state_dict = state_dict.get("model", state_dict)
+        model.load_state_dict(state_dict, strict=strict)
+
+
+def flops_per_param(max_seq_length: int, n_layer: int, n_embd: int, n_params: int) -> int:
+    flops_per_token = (
+        2 * n_params
+    )  # each parameter is used for a MAC (2 FLOPS) per network operation
+    # this assumes that all samples have a fixed length equal to the block size
+    # which is most likely false during finetuning
+    flops_per_seq = flops_per_token * max_seq_length
+    attn_flops_per_seq = n_layer * 2 * 2 * (n_embd * (max_seq_length**2))
+    return flops_per_seq + attn_flops_per_seq
+
+
+def estimate_flops(model: "GPT", training: bool) -> int:
+    """Measures estimated FLOPs for MFU.
+
+    Refs:
+        * https://ar5iv.labs.arxiv.org/html/2205.05198#A1
+        * https://ar5iv.labs.arxiv.org/html/2204.02311#A2
+    """
+    # using all parameters for this is a naive over estimation because not all model parameters actually contribute to
+    # this FLOP computation (e.g. embedding, norm). For this reason, the result will be higher by a fixed percentage
+    # (~10%) compared to the measured FLOPs, making those lower but more realistic.
+    # For a proper estimate, this needs a more fine-grained calculation as in Appendix A of the paper.
+    n_trainable_params = num_parameters(model, requires_grad=True)
+    trainable_flops = flops_per_param(
+        model.max_seq_length, model.config.n_layer, model.config.n_embd, n_trainable_params
+    )
+    # forward + backward + gradients (assumes no gradient accumulation)
+    ops_per_step = 3 if training else 1
+    n_frozen_params = num_parameters(model, requires_grad=False)
+    frozen_flops = flops_per_param(
+        model.max_seq_length, model.config.n_layer, model.config.n_embd, n_frozen_params
+    )
+    # forward + backward
+    frozen_ops_per_step = 2 if training else 1
+    return ops_per_step * trainable_flops + frozen_ops_per_step * frozen_flops