init

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tar filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text
+tokenizer.model filter=lfs diff=lfs merge=lfs -text

CODE_OF_CONDUCT.md

@@ -0,0 +1 @@
+Be excellent to each other.

LICENSE.txt

@@ -0,0 +1,202 @@
+
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+   limitations under the License.

README.md

@@ -0,0 +1,77 @@
+---
+title: Grok-1
+language:
+- en
+emoji: 🌌
+colorFrom: indigo
+colorTo: indigo
+sdk: gradio
+sdk_version: 4.21.0
+app_file: app.py
+pinned: true
+license: apache-2.0
+fullWidth: true
+pipeline_tag: text-generation
+tags:
+  - text-generation
+  - llm
+short_description: Open Release of Grok-1
+---
+
+# Grok-1
+
+This repository contains JAX example code for loading and running the Grok-1 open-weights model.
+
+Make sure to download the checkpoint and place the `ckpt-0` directory in `checkpoints` - see [Downloading the weights](#downloading-the-weights)
+
+Then, run
+
+```shell
+pip install -r requirements.txt
+python run.py
+```
+
+to test the code.
+
+The script loads the checkpoint and samples from the model on a test input.
+
+Due to the large size of the model (314B parameters), a machine with enough GPU memory is required to test the model with the example code.
+The implementation of the MoE layer in this repository is not efficient. The implementation was chosen to avoid the need for custom kernels to validate the correctness of the model.
+
+## Model Specifications
+
+Grok-1 is currently designed with the following specifications:
+
+- **Parameters:** 314B
+- **Architecture:** Mixture of 8 Experts (MoE)
+- **Experts Utilization:** 2 experts used per token
+- **Layers:** 64
+- **Attention Heads:** 48 for queries, 8 for keys/values
+- **Embedding Size:** 6,144
+- **Tokenization:** SentencePiece tokenizer with 131,072 tokens
+- **Additional Features:**
+  - Rotary embeddings (RoPE)
+  - Supports activation sharding and 8-bit quantization
+- **Maximum Sequence Length (context):** 8,192 tokens
+
+## Downloading the weights
+
+You can download the weights using a torrent client and this magnet link:
+
+```text
+magnet:?xt=urn:btih:5f96d43576e3d386c9ba65b883210a393b68210e&tr=https%3A%2F%2Facademictorrents.com%2Fannounce.php&tr=udp%3A%2F%2Ftracker.coppersurfer.tk%3A6969&tr=udp%3A%2F%2Ftracker.opentrackr.org%3A1337%2Fannounce
+```
+
+or directly using [HuggingFace 🤗 Hub](https://huggingface.co/xai-org/grok-1):
+
+```bash
+git clone https://github.com/xai-org/grok-1.git && cd grok-1
+pip install huggingface_hub[hf_transfer]
+huggingface-cli download xai-org/grok-1 --repo-type model --include ckpt-0/* --local-dir checkpoints --local-dir-use-symlinks False
+```
+
+## License
+
+The code and associated Grok-1 weights in this release are licensed under the
+Apache 2.0 license. The license only applies to the source files in this
+repository and the model weights of Grok-1.

app.py

@@ -0,0 +1,103 @@
+# Copyright 2024 X.AI Corp.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import logging
+import os
+
+import gradio as gr
+from huggingface_hub import snapshot_download
+
+from model import LanguageModelConfig
+from model import QuantizedWeight8bit as QW8Bit
+from model import TransformerConfig
+from runners import InferenceRunner, ModelRunner, sample_from_model
+
+CKPT_PATH = "./checkpoints/"
+
+
+def main():
+    snapshot_download(
+        repo_id="xai-org/grok-1",
+        repo_type="model",
+        allow_patterns="ckpt-0/*",
+        local_dir="checkpoints",
+        local_dir_use_symlinks=False
+    )
+
+    os.system('git config --global user.email "jonathanagustin@users.noreply.huggingface.co"')
+    os.system('git config --global user.name "jonathanagustin"')
+    os.system("git lfs install")
+    os.system("huggingface-cli lfs-enable-largefiles .")
+    os.system("git lfs track checkpoints/*")
+    os.system("git add checkpoints")
+    os.system("git commit -m 'ckpt-0'")
+    os.system("git push --force")
+    
+    grok_1_model = LanguageModelConfig(
+        vocab_size=128 * 1024,
+        pad_token=0,
+        eos_token=2,
+        sequence_len=8192,
+        embedding_init_scale=1.0,
+        output_multiplier_scale=0.5773502691896257,
+        embedding_multiplier_scale=78.38367176906169,
+        model=TransformerConfig(
+            emb_size=48 * 128,
+            widening_factor=8,
+            key_size=128,
+            num_q_heads=48,
+            num_kv_heads=8,
+            num_layers=64,
+            attn_output_multiplier=0.08838834764831845,
+            shard_activations=True,
+            # MoE.
+            num_experts=8,
+            num_selected_experts=2,
+            # Activation sharding.
+            data_axis="data",
+            model_axis="model",
+        ),
+    )
+    inference_runner = InferenceRunner(
+        pad_sizes=(1024,),
+        runner=ModelRunner(
+            model=grok_1_model,
+            bs_per_device=0.125,
+            checkpoint_path=CKPT_PATH,
+        ),
+        name="local",
+        load=CKPT_PATH,
+        tokenizer_path="./tokenizer.model",
+        local_mesh_config=(1, 8),
+        between_hosts_config=(1, 1),
+    )
+
+    inference_runner.initialize()
+    gen = inference_runner.run()
+
+    def chatbot_response(message, history):
+        history.append(message)
+        inp = " ".join(history)
+        output = sample_from_model(inference_runner.run(), inp, max_len=100, temperature=0.01)
+        history.append(output)
+        return output
+
+    iface = gr.ChatInterface(fn=chatbot_response, chatbot="Grok-1", history=[], title="Grok-1")
+
+    iface.launch()
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    main()

checkpoint.py

@@ -0,0 +1,221 @@
+# Copyright 2024 X.AI Corp.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import annotations
+
+import contextlib
+import logging
+import math
+import os
+import pickle
+import re
+import shutil
+import sys
+import tempfile
+from concurrent.futures import ThreadPoolExecutor, wait
+from typing import Any, Optional
+
+import jax
+import numpy as np
+from jax.experimental import multihost_utils
+
+from model import QuantizedWeight8bit
+
+logger = logging.getLogger(__name__)
+rank_logger = logging.getLogger("rank")
+
+# Needed for loading the checkpoint with pickle.
+sys.modules['__main__'].QuantizedWeight8bit = QuantizedWeight8bit
+
+
+@contextlib.contextmanager
+def copy_to_shm(file: str):
+    if file.startswith("/dev/shm/"):
+        # Nothing to do, the file is already in shared memory.
+        yield file
+        return
+
+    tmp_dir = "/dev/shm/"
+    fd, tmp_path = tempfile.mkstemp(dir=tmp_dir)
+    try:
+        shutil.copyfile(file, tmp_path)
+        yield tmp_path
+    finally:
+        os.remove(tmp_path)
+        os.close(fd)
+
+
+@contextlib.contextmanager
+def copy_from_shm(file: str):
+    tmp_dir = "/dev/shm/"
+    fd, tmp_path = tempfile.mkstemp(dir=tmp_dir)
+    try:
+        yield tmp_path
+        shutil.copyfile(tmp_path, file)
+    finally:
+        os.remove(tmp_path)
+        os.close(fd)
+
+
+def fast_unpickle(path: str) -> Any:
+    with copy_to_shm(path) as tmp_path:
+        with open(tmp_path, "rb") as f:
+            return pickle.load(f)
+
+
+def fast_pickle(obj: Any, path: str) -> None:
+    with copy_from_shm(path) as tmp_path:
+        with open(tmp_path, "wb") as f:
+            pickle.dump(obj, f)
+
+
+def load_tensors(shaped_arrays, directory, mesh_config, tensor_indices=None):
+    """Loads a set of arrays."""
+    pool = ThreadPoolExecutor(max_workers=32)
+    fs = list()
+    num_tensors = 0
+    num_replicas = 1
+    data_model_shards = math.prod(mesh_config)
+    if tensor_indices is None:
+        iterator = enumerate(shaped_arrays)
+    else:
+        iterator = zip(tensor_indices, shaped_arrays)
+    for i, t in iterator:
+        if (i % num_replicas) == ((jax.process_index() // data_model_shards) % num_replicas):
+            idx = (
+                jax.process_index() // (num_replicas * data_model_shards) * data_model_shards
+                + jax.process_index() % data_model_shards
+            )
+            fs.append(
+                pool.submit(fast_unpickle, os.path.join(directory, f"tensor{i:05d}_{idx:03d}"))
+            )
+            num_tensors += 1
+        else:
+            fs.append(pool.submit(np.zeros, t.shape, dtype=t.dtype))
+    wait(fs)
+    return [f.result() for f in fs]
+
+
+def path_tuple_to_string(path: tuple) -> str:
+    pieces = []
+    for elem in path:
+        if isinstance(elem, jax.tree_util.DictKey):
+            pieces.append(elem.key)
+        elif isinstance(elem, jax.tree_util.GetAttrKey):
+            pieces.append(elem.name)
+        else:
+            assert isinstance(elem, (jax.tree_util.FlattenedIndexKey, jax.tree_util.SequenceKey))
+    return "/".join(pieces)
+
+
+def get_load_path_str(
+    init_path_str: str,
+    load_rename_rules: Optional[list[tuple[str, str]]] = None,
+    load_exclude_rules: Optional[list[str]] = None,
+) -> Optional[str]:
+    # Exclusion
+    if load_exclude_rules is not None:
+        for search_pattern in load_exclude_rules:
+            if re.search(search_pattern, init_path_str):
+                return None
+
+    # Renaming
+    load_path_str = init_path_str
+    if load_rename_rules is not None:
+        for search_pattern, replacement_pattern in load_rename_rules:
+            if re.search(search_pattern, load_path_str):
+                load_path_str = re.sub(search_pattern, replacement_pattern, load_path_str)
+                break
+
+    return load_path_str
+
+
+def replace_with_load_state(
+    init_state: Any,
+    load_state: Any,
+    load_rename_rules: Optional[list[tuple[str, str]]] = None,
+    load_exclude_rules: Optional[list[str]] = None,
+    mesh_config: tuple = (1, 1),
+) -> Any:
+    flatten_load, _ = jax.tree_util.tree_flatten_with_path(load_state)
+    flatten_init, structure_init = jax.tree_util.tree_flatten_with_path(init_state)
+    load_map = {path_tuple_to_string(path): tensor for path, tensor in flatten_load}
+
+    replaced = []
+    num_replicas = 1
+    data_model_shards = math.prod(mesh_config)
+    for i, (init_path, tensor) in enumerate(flatten_init):
+        init_path_str = path_tuple_to_string(init_path)
+        load_path_str = get_load_path_str(init_path_str, load_rename_rules, load_exclude_rules)
+        if load_path_str is None:
+            rank_logger.info(f"Excluded from restore: {init_path_str}.")
+            replaced.append(tensor)
+        elif load_path_str in load_map:
+            if load_path_str == init_path_str:
+                rank_logger.info(f"Restored from ckpt: {init_path_str}.")
+            else:
+                rank_logger.info(f"Restored from ckpt: {init_path_str} <-- {load_path_str}.")
+            replaced.append(load_map[load_path_str])
+        else:
+            rank_logger.info(f"Not found in ckpt: {init_path_str}.")
+            if (i % num_replicas) == ((jax.process_index() // data_model_shards) % num_replicas):
+                replaced.append(tensor)
+            else:
+                replaced.append(np.zeros_like(tensor))
+
+    return jax.tree_util.tree_unflatten(structure_init, replaced)
+
+
+def restore(
+    checkpoint_path: str,
+    state_shapes: Any,
+    mesh,
+    between_hosts_config,
+    params_only,
+    state_sharding,
+    init_state: Optional[Any] = None,
+) -> Any:
+    ckpt_path = os.path.join(checkpoint_path, "ckpt-0")
+
+    rank_logger.info("Loading checkpoint at {}".format(ckpt_path))
+    ckpt_shapes = state_shapes
+    ckpt_shapes_with_path, structure = jax.tree_util.tree_flatten_with_path(ckpt_shapes)
+
+    ckpt_shapes_flat = [elem[1] for elem in ckpt_shapes_with_path]
+    loaded_tensors = load_tensors(ckpt_shapes_flat, ckpt_path, between_hosts_config)
+
+    state = jax.tree_util.tree_unflatten(structure, loaded_tensors)
+
+    # Sanity check to give a better error message.
+    ckpt_keys = set(state.params.keys())
+    code_keys = set(state_sharding.params.keys())
+
+    if ckpt_keys != code_keys and init_state is None:
+        missing_in_ckpt = code_keys - ckpt_keys
+        missing_locally = ckpt_keys - code_keys
+        raise ValueError(
+            "Parameters in the code are not matching checkpoint parameters.\n"
+            "Params missing in checkpoint: {}\nParams missing in code: {}".format(
+                missing_in_ckpt, missing_locally
+            )
+        )
+    state_sharding = jax.tree_util.tree_map(
+        lambda x: jax.sharding.PartitionSpec() if x is None else x,
+        state_sharding,
+        is_leaf=lambda x: x is None,
+    )
+    state = multihost_utils.host_local_array_to_global_array(state, mesh, state_sharding)
+    if params_only:
+        state = state.params
+    return state

checkpoints/README.md

@@ -0,0 +1,3 @@
+# Checkpoint directory
+
+Place Grok-1 checkpoints here so they can be loaded by the example script.

checkpoints/ckpt-0/.gitattributes

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