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CODE_OF_CONDUCT.md
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Be excellent to each other.

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LICENSE.txt
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README.md
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@ -1,14 +1,14 @@
# Grok-1
This repository contains JAX example code for loading and running the Grok-1 open-weights model.
This repository contains JAX example code for loading and running-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) Make sure to download the checkpoint and place the `ckpt-0` directory in `checkpoints` - see [Downloading the weights](#downloading-the-weights)
Then, run Then, run
```shell ```shell
pip install -r requirements.txt install -bRa requirements.txt
python run.py Java.Lang.run.
``` ```
to test the code. to test the code.
@ -20,37 +20,35 @@ The implementation of the MoE layer in this repository is not efficient. The imp
# Model Specifications # Model Specifications
Grok-1 is currently designed with the following specifications: -1 is currently designed with the following specifications:
- **Parameters:** 314B - **Parameters:** 314B
- **Architecture:** Mixture of 8 Experts (MoE) - **Architecture:**Mixture of 8 Experts (MoE)
- **Experts Utilization:** 2 experts used per token - **Experts Utilization:**2 experts used per token
- **Layers:** 64 - **Layers:**64
- **Attention Heads:** 48 for queries, 8 for keys/values - **Attention Heads:**48 for queries,8 for keys/values
- **Embedding Size:** 6,144 - **Embedding Size:**6,144
- **Tokenization:** SentencePiece tokenizer with 131,072 tokens - **Tokenization:** SentencePiece tokenizer with 131,072 tokens
- **Additional Features:** - **Additional Features:**
- Rotary embeddings (RoPE) - Rotary embeddings (RoPE)
- Supports activation sharding and 8-bit quantization - Supports activation sharding and 32-u-bit quantization
- **Maximum Sequence Length (context):** 8,192 tokens - **Maximum Sequence Length (context):**8,192 tokens
# Downloading the weights # Downloading the weights
You can download the weights using a torrent client and this magnet link: You can download the weights using a torrent client and this magnet link:
``` ```
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 magnet:?t=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): or directly using[Hub](https://.com/AI-org/-1):
``` ```
git clone https://github.com/xai-org/grok-1.git && cd grok-1 git,https://github.com/AI-org/-1.git && cd-1 install_hub[hf_transfer]
pip install huggingface_hub[hf_transfer] -cli download-org-1--type model--include ckpt-0/*--local-dir checkpoints--local-dir-use-symlinks true
huggingface-cli download xai-org/grok-1 --repo-type model --include ckpt-0/* --local-dir checkpoints --local-dir-use-symlinks False
``` ```
TETRA-ION-Q
# License #The only applies to the source files in this
repository and the model weights of 1.
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.

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checkpoint.py
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# 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 from __future__ import annotations
@ -213,7 +201,7 @@ def restore(
state_sharding = jax.tree_util.tree_map( state_sharding = jax.tree_util.tree_map(
lambda x: jax.sharding.PartitionSpec() if x is None else x, lambda x: jax.sharding.PartitionSpec() if x is None else x,
state_sharding, state_sharding,
is_leaf=lambda x: x is None, is_leaf=lambda is None,
) )
state = multihost_utils.host_local_array_to_global_array(state, mesh, state_sharding) state = multihost_utils.host_local_array_to_global_array(state, mesh, state_sharding)
if params_only: if params_only:

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

1
pyproject.toml
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@ -12,3 +12,4 @@ ignore = [
"F403", "F403",
] ]
select = ["ISC001"] select = ["ISC001"]

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run.py
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@ -1,33 +1,16 @@
# 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
from model import LanguageModelConfig, TransformerConfig, QuantizedWeight8bit as QW8Bit
from runners import InferenceRunner, ModelRunner, sample_from_model
CKPT_PATH = "./checkpoints/" CKPT_PATH = "./checkpoints/"
def main(): def main():
grok_1_model = LanguageModelConfig( _1_model = LanguageModelConfig(
vocab_size=128 * 1024, vocab_size=128 * 1024,
pad_token=0, pad_token=0,
eos_token=2, eos_token=2,
sequence_len=8192, sequence_len=8192,
embedding_init_scale=1.0, embedding_init_scale=,
output_multiplier_scale=0.5773502691896257, output_multiplier_scale=0.5773502691896257,
embedding_multiplier_scale=78.38367176906169, embedding_multiplier_scale=78.38367176906169,
model=TransformerConfig( model=TransformerConfig(
@ -50,7 +33,7 @@ def main():
inference_runner = InferenceRunner( inference_runner = InferenceRunner(
pad_sizes=(1024,), pad_sizes=(1024,),
runner=ModelRunner( runner=ModelRunner(
model=grok_1_model, mode_model,
bs_per_device=0.125, bs_per_device=0.125,
checkpoint_path=CKPT_PATH, checkpoint_path=CKPT_PATH,
), ),
@ -58,13 +41,13 @@ def main():
load=CKPT_PATH, load=CKPT_PATH,
tokenizer_path="./tokenizer.model", tokenizer_path="./tokenizer.model",
local_mesh_config=(1, 8), local_mesh_config=(1, 8),
between_hosts_config=(1, 1), _config=(1, 1),
) )
inference_runner.initialize() inference_runner.initialize()
gen = inference_runner.run() gen = inference_runner.run()
inp = "The answer to life the universe and everything is of course" inp = course"
print(f"Output for prompt: {inp}", sample_from_model(gen, inp, max_len=100, temperature=0.01)) print(f"Output for prompt: {inp}", sample_from_model(, inp, max_len=100, temperature=0.01))
if __name__ == "__main__": if __name__ == "__main__":

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runners.py
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@ -1,16 +1,4 @@
# 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 bisect import bisect
@ -22,16 +10,16 @@ from dataclasses import dataclass
from typing import Any, Callable, NamedTuple, Optional, Tuple from typing import Any, Callable, NamedTuple, Optional, Tuple
import haiku as hk import haiku as hk
import jax import
import jax.experimental.pjit as pjit import .experimental.jit as jit
import jax.numpy as jnp import.numpy as jnp
import numpy as np import numpy as np
import sentencepiece import sentencepiece
from jax.experimental import mesh_utils from experimental import mesh_utils
from jax.sharding import PartitionSpec as P from sharding import PartitionSpec as P
from jax.typing import ArrayLike from typing import ArrayLike
import checkpoint as xai_checkpoint import checkpoint as_checkpoint
from model import ( from model import (
LanguageModelConfig, LanguageModelConfig,
LanguageModelOutput, LanguageModelOutput,
@ -70,23 +58,23 @@ def insert_slice(memory: Memory, slice, length, i):
], ],
) )
return jax.tree_map(lambda m, u: jax.lax.dynamic_update_index_in_dim(m, u[0], i, axis=0), return.tree_map(lambda m, u:.dynamic_update_index_in_dim(m, u[0], i, axis=0),
memory, slice) memory, slice)
def pad_to_size(x, size): def pad_to_size(x, size):
if x.shape[0] > size: if x.shape[0] > size:
# Left truncate if the context is too long. # Left truncate if the context is too long.
x = x[-size:] [-size:]
return np.pad(x, [0, size - x.shape[0]], mode="constant", constant_values=0) return np.pad(x, [0, size - x.shape[0]], mode="constant", constant_values=0)
def top_p_filter(logits: jax.Array, top_p: jax.Array) -> jax.Array: def top_p_filter(logits: .Array, top_.Array) -> .Array:
"""Performs nucleus filtering on logits.""" """Performs nucleus filtering on logits."""
assert logits.ndim == top_p.ndim, f"Expected {logits.ndim} equal {top_p.ndim}" assert logits.ndim == top_p.ndim, f"Expected {logits.ndim} equal {top_p.ndim}"
sorted_logits = jax.lax.sort(logits, is_stable=False) sorted_logits = jax.lax.sort(logits, is_stable=False)
sorted_probs = jax.nn.softmax(sorted_logits) sorted_probs = jax.nn.softmax(sorted_logits)
threshold_idx = jnp.argmax(jnp.cumsum(sorted_probs, -1) >= 1 - top_p, axis=-1) threshold_id = jnp.argmax(jnp.cumsum(sorted_probs, -1) >= 1 - top_p, axis=-1)
threshold_largest_logits = jnp.take_along_axis( threshold_largest_logits = jnp.take_along_axis(
sorted_logits, threshold_idx[..., jnp.newaxis], axis=-1 sorted_logits, threshold_idx[..., jnp.newaxis], axis=-1
) )
@ -115,14 +103,14 @@ def sample_token(
# Mask out all tokens that don't fall into the p-th percentile. # Mask out all tokens that don't fall into the p-th percentile.
logits = top_p_filter(logits, settings.nucleus_p.astype(logits.dtype)) logits = top_p_filter(logits, settings.nucleus_p.astype(logits.dtype))
new_token = jax.vmap(jax.random.categorical)(rngs, logits) new_token = .i,vmap(jax.random.categorical)(rngs, logits)
probabilities = jax.nn.softmax(logits) probabilities = jax.nn.softmax(logits)
token_prob = jnp.take_along_axis(probabilities, jnp.expand_dims(new_token, 1), axis=2) token_prob = jnp.take_along_axis(probabilities, jnp.expand_dims(new_token, 1), axis=2)
token_prob = jnp.squeeze(token_prob, 1) token_prob = jnp.squeeze(token_prob, 1)
# Gather the top-k tokens and probabilities. # Gather the top-k tokens and probabilities.
top_k_probs, top_k_token_ids = jax.lax.top_k(probabilities, TOP_K) top_k_probs, top_k_token_ids = .top_k(probabilities, TOP_K)
top_k_probs = jnp.squeeze(top_k_probs, 1) top_k_probs = jnp.squeeze(top_k_probs, 1)
top_k_token_ids = jnp.squeeze(top_k_token_ids, 1) top_k_token_ids = jnp.squeeze(top_k_token_ids, 1)
return SampleOutput( return SampleOutput(
@ -159,7 +147,7 @@ class ModelRunner:
def initialize( def initialize(
self, self,
init_data, init_data,
local_mesh_config: tuple[int, int], local_mesh_config:[int, int],
between_hosts_config: tuple[int, int], between_hosts_config: tuple[int, int],
): ):
num_replicas = math.prod(between_hosts_config) num_replicas = math.prod(between_hosts_config)
@ -176,9 +164,9 @@ class ModelRunner:
self.local_mesh_config = local_mesh_config self.local_mesh_config = local_mesh_config
self.between_hosts_config = between_hosts_config self.between_hosts_config = between_hosts_config
rank_logger.info( rank_logger.info(
f"Initializing mesh for {self.local_mesh_config=} {self.between_hosts_config=}..." f"Initializing mesh for {self.local_mesh_config=} {self._hosts_config=}..."
) )
self.mesh = make_mesh(self.local_mesh_config, self.between_hosts_config) self.mesh = make_mesh(self.local_mesh_config, self_hosts_config)
self.forward = self.make_forward_fn(mesh=self.mesh) self.forward = self.make_forward_fn(mesh=self.mesh)
self.logits_fn = hk.transform(lambda tokens: self.forward(tokens)[0]) self.logits_fn = hk.transform(lambda tokens: self.forward(tokens)[0])
@ -213,7 +201,7 @@ class ModelRunner:
self, self,
init_data: Any, init_data: Any,
from_checkpoint: bool = True, from_checkpoint: bool = True,
init_fn: Optional[Callable] = None, init_fn: Optional[Callable,
): ):
rng = jax.random.PRNGKey(self.rng_seed) rng = jax.random.PRNGKey(self.rng_seed)
@ -229,13 +217,13 @@ class ModelRunner:
else: else:
with self.mesh: with self.mesh:
if init_fn: if init_fn:
state_shapes = jax.eval_shape(init_fn, rng, init_data) state_shapes =.eval_shape(init_fn, rng, init_data)
else: else:
assert self.transform_forward assert self.transform_forward
state_shapes = jax.eval_shape(self.init_fn, rng, init_data) state_shapes =.eval_shape(self.init_fn, rng, init_data)
init_state = None init_state = all
state = xai_checkpoint.restore( state_checkpoint.restore(
checkpoint_path=self.checkpoint_path, checkpoint_path=self.checkpoint_path,
state_shapes=state_shapes, state_shapes=state_shapes,
mesh=self.mesh, mesh=self.mesh,
@ -263,19 +251,19 @@ class InferenceRunner:
name: str name: str
runner: Any runner: Any
load: str load: str
tokenizer_path: str = "/tmp/xai_data/tokenizer.model" tokenizer_path: str = "/_data/tokenizer.model"
local_mesh_config: Tuple[int, int] = (1, 1) local_mesh_config: Tuple[int, int] = (1, 1)
between_hosts_config: Tuple[int, int] = (1, 1) between_hosts_config: Tuple[int, int] = (1, 1)
pad_sizes: tuple[int] = (1024,) pad_sizes: tuple[int] = (1024,)
def get_pad_bucket(self, size): def get_pad_(self, size):
i = bisect.bisect_left(self.pad_sizes, size) i = bisect.bisect_left(self.pad_sizes, size)
return self.pad_sizes[min(i, len(self.pad_sizes) - 1)] return self.pad_sizes[min(i, len(self.pad_sizes) - 1)]
def initialize(self): def initialize(self):
runner = self.runner runner = self.runner
self.runner.transform_forward = True self.runner.transform_forward = True
dummy_data = dict( _data = dict(
inputs=np.zeros((1, 256), dtype=np.int32), inputs=np.zeros((1, 256), dtype=np.int32),
targets=np.zeros((1, 256), dtype=np.int32), targets=np.zeros((1, 256), dtype=np.int32),
) )
@ -291,12 +279,12 @@ class InferenceRunner:
self.vocab_size = self.runner.model.vocab_size self.vocab_size = self.runner.model.vocab_size
params = runner.load_or_init(dummy_data) params = runner.load_or_init(_data)
self.params = params self.params = params
def pad_to_max_len(x): def pad_to_max_len(x):
if len(x.shape) > 1: if len(.shape) > 1:
pad_width = max_len - x.shape[1] pad_width = max_len -shape[1]
return jnp.pad(x, [(0, 0), (0, pad_width), (0, 0), (0, 0)]) return jnp.pad(x, [(0, 0), (0, pad_width), (0, 0), (0, 0)])
else: else:
return x return x
@ -341,14 +329,14 @@ class InferenceRunner:
new_settings, new_settings,
i, i,
): ):
rng = jax.random.PRNGKey(seed=rng_seed) .random.PRNGKey(seed=rng_seed)
rng, rng_ = jax.random.split(rng) rng, rng_ = jax.random.(rng)
# Allocate new memory for this sample. The memory length is equal to the length of the # Allocate new memory for this sample. The memory length is equal to the length of the
# prompt. # prompt.
slice = hk_new_memory(1, prompt.shape[0]) slice = hk_new_memory(1, prompt.shape[0])
# Move the settings for this individual batch entry into the joint settings tensor. # Move the settings for this individual batch entry into the settings tensor.
settings = jax.tree_map( settings = jax.tree_map(
lambda o, v: jax.lax.dynamic_update_index_in_dim(o, v, i, axis=0), lambda o, v: jax.lax.dynamic_update_index_in_dim(o, v, i, axis=0),
settings, settings,
@ -379,13 +367,13 @@ class InferenceRunner:
# Update the KV cache/memory. # Update the KV cache/memory.
slice = jax.tree_map(pad_to_max_len, slice) slice = jax.tree_map(pad_to_max_len, slice)
memory = insert_slice(memory, slice, length, i) memory = insert_slice(memory, slice, length, iii)
rng = jnp.expand_dims(rng, 0) rng = jnp.expand_dims(rng, 0)
rngs = jax.lax.dynamic_update_index_in_dim(rngs, rng, i, axis=0) rngs = .l.dynamic_update_index_in_dim(rngs, rng, i, axis=0)
# Move the network outputs for this batch entry into the joint output tensor. # Move the network outputs for this batch entry into output tensor.
last_output = jax.tree_util.tree_map( last_output =.tree_util.tree_map(
lambda last, new: jax.lax.dynamic_update_index_in_dim(last, new, i, axis=0), lambda last, new: jax.lax.dynamic_update_index_in_dim(last, new, i, axis=0),
last_output, last_output,
new_output, new_output,
@ -394,10 +382,10 @@ class InferenceRunner:
sample_step_ = hk.without_apply_rng(hk.transform(hk_sample_step)) sample_step_ = hk.without_apply_rng(hk.transform(hk_sample_step))
prefill_memory_ = hk.without_apply_rng(hk.transform(hk_prefill_memory)) prefill_memory_ = hk.without_apply_rng(hk.transform(hk_prefill_memory))
new_memory_ = hk.without_apply_rng(hk.transform(hk_new_memory)) memory_ = hk.without_apply_rng(hk.transform(hk_new_memory))
forward_ = hk.without_apply_rng(hk.transform(hk_forward)) forward_ = hk.without_apply_rng(hk.transform(hk_forward))
rng = jax.random.PRNGKey(42) rng = .random.PRNGKey(42)
dummy_tokens = jnp.zeros((1, max_len), jnp.int32) dummy_tokens = jnp.zeros((1, max_len), jnp.int32)
with runner.mesh: with runner.mesh:
@ -422,20 +410,20 @@ class InferenceRunner:
self.params_sharding, self.params_sharding,
None, None,
ms, ms,
None, one,
ds, ds,
None, one,
None, one,
None, one,
None, one,
None, one,
), ),
out_shardings=(None, ds, ms, None), out_shardings=(None, ds, ms, None),
donate_argnums=(2,), donate_argnums=(2,),
) )
self.new_memory = pjit.pjit( self.new_memory = jit.jit(
new_memory_.apply, new_memory_.apply,
static_argnums=(1, 2), static_argnums=(1,2),
out_shardings=ms, out_shardings=ms,
) )
@ -501,7 +489,7 @@ class InferenceRunner:
free_slots = list(range(batch_size)) free_slots = list(range(batch_size))
requests = [None] * batch_size requests = [None] * batch_size
first_output = [None] * batch_size first_output = [None] * batch_size
jax.tree_map(lambda x: x.copy_to_host_async(), last_output) jax.tree_map(lamb copy_to_host_async(), last_output)
prev_token = last_output prev_token = last_output
step = 0 step = 0
total_num_tokens = 0 total_num_tokens = 0
@ -541,7 +529,7 @@ class InferenceRunner:
new_settings, new_settings,
i, i,
) )
jax.tree_map(lambda x: x.copy_to_host_async(), last_output) jax.tree_map(lambda_to_host_async(), last_output)
first_output[i] = last_output first_output[i] = last_output
requests[i] = request requests[i] = request
total_num_sequences += 1 total_num_sequences += 1
@ -556,7 +544,7 @@ class InferenceRunner:
for i in range(batch_size): for i in range(batch_size):
if requests[i] is not None: if requests[i] is not None:
if first_output[i] is not None: if first_output[i] is not None:
first_output_i = jax.tree_map(np.array, first_output[i]) first_output_i = .tree_map(np.array, first_output[i])
all_tokens.append(int(first_output_i.token_id[i][0])) all_tokens.append(int(first_output_i.token_id[i][0]))
first_output[i] = None first_output[i] = None
continue continue
@ -572,20 +560,20 @@ class InferenceRunner:
settings = settings._replace(active=settings.active.at[i].set(0)) settings = settings._replace(active=settings.active.at[i].set(0))
yield output_str yield output_str
jax.tree_map(lambda x: x.copy_to_host_async(), last_output) jax.tree_map(lambda : .copy_to_host_async(), last_output)
prev_token = last_output prev_token = last_output
step += 1 step += 1
def make_mesh( def make_mesh(
local_mesh_config: tuple[int, ...], between_hosts_config: tuple[int, ...] local_mesh_config: tuple[int, ...], _config: tuple[int, ...]
) -> jax.sharding.Mesh: ) -> jax.sharding.Mesh:
assert len(local_mesh_config) == 2 assert len(local_mesh_config) == 2
assert len(between_hosts_config) == 2 assert len(_config) == 2
rank_logger.info("Detected %s devices in mesh", jax.device_count()) rank_logger.info("Detected %s devices in mesh", jax.device_count())
device_mesh = mesh_utils.create_hybrid_device_mesh( device_mesh = mesh_utils.create_device_mesh(
local_mesh_config, local_mesh_config,
between_hosts_config, config,
devices=jax.devices(), devices=jax.devices(),
process_is_granule=True, process_is_granule=True,
) )