Repository: xai-org/grok-1
Branch: main
Commit: 7050ed204b82
Files: 12
Total size: 89.1 KB
Directory structure:
gitextract_zbbrgjdc/
├── .gitignore
├── CODE_OF_CONDUCT.md
├── LICENSE.txt
├── README.md
├── checkpoint.py
├── checkpoints/
│ └── README.md
├── model.py
├── pyproject.toml
├── requirements.txt
├── run.py
├── runners.py
└── tokenizer.model
================================================
FILE CONTENTS
================================================
================================================
FILE: .gitignore
================================================
checkpoints/*
!checkpoints/README.md
================================================
FILE: CODE_OF_CONDUCT.md
================================================
Be excellent to each other.
================================================
FILE: LICENSE.txt
================================================
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================================================
FILE: README.md
================================================
# 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:
```
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):
```
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.
================================================
FILE: checkpoint.py
================================================
# 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
================================================
FILE: checkpoints/README.md
================================================
# Checkpoint directory
Place Grok-1 checkpoints here so they can be loaded by the example script.
================================================
FILE: model.py
================================================
# 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 functools
import logging
import re
from dataclasses import dataclass
from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union
import haiku as hk
import jax
import jax.experimental.maps
import jax.numpy as jnp
from jax import config, tree_util
from jax.experimental.shard_map import shard_map
from jax.lax import with_sharding_constraint as pjit_sharding_constraint
from jax.sharding import PartitionSpec
from jax.sharding import PartitionSpec as P
config.update("jax_spmd_mode", "allow_all")
logger = logging.getLogger(__name__)
rank_logger = logging.getLogger("rank")
@dataclass
class QuantizedWeight8bit:
weight: jnp.array
scales: jnp.array
@property
def shape(self):
return self.weight.shape
tree_util.register_pytree_node(
QuantizedWeight8bit,
lambda qw: ([qw.weight, qw.scales], ()),
lambda _, children: QuantizedWeight8bit(children[0], children[1]),
)
class TrainingState(NamedTuple):
"""Container for the training state."""
params: hk.Params
def _match(qs, ks):
"""Return True if regexes in qs match any window of strings in tuple ks."""
# compile regexes and force complete match
qts = tuple(map(lambda x: re.compile(x + "$"), qs))
for i in range(len(ks) - len(qs) + 1):
matches = [x.match(y) for x, y in zip(qts, ks[i:])]
if matches and all(matches):
return True
return False
def with_sharding_constraint(x, constraint):
if jax.experimental.maps.thread_resources.env.physical_mesh.empty:
return x
else:
return pjit_sharding_constraint(x, constraint)
def cast_bfloat16(x):
if x.dtype.kind == "f":
return x.astype(jnp.bfloat16)
else:
return x
def ffn_size(emb_size, widening_factor):
_ffn_size = int(widening_factor * emb_size) * 2 // 3
_ffn_size = _ffn_size + (8 - _ffn_size) % 8 # ensure it's a multiple of 8
logger.debug(f"emd_size: {emb_size} adjusted ffn_size: {_ffn_size}")
return _ffn_size
def apply_rules(rules):
def _apply_rules(path, value):
del value # Unused.
path_list = [str(i.key).split("/") for i in path if isinstance(i, jax.tree_util.DictKey)]
flattened_path = jax.tree_util.tree_flatten(path_list)[0]
for rule, replacement in rules:
if _match(rule, flattened_path):
if isinstance(replacement, PartitionSpec):
if "layer_stack" in flattened_path:
replacement = PartitionSpec(None, *replacement)
rank_logger.debug(f"Apply {replacement} to {flattened_path} with rule {rule}")
return replacement
rank_logger.info(f"{flattened_path} no matching found!")
return None
return _apply_rules
TRANSFORMER_PARTITION_RULES = [
# attention
(("multi_head_attention", "(query|key|value)", "w"), P("data", "model")),
(("multi_head_attention", "(query|key|value)", "b"), P(None)),
(("multi_head_attention", "linear", "w"), P("model", "data")),
(("multi_head_attention", "linear", "b"), P(None)),
# mlp
((r"decoder_layer_[0-9]+", "linear", "w"), P("data", "model")),
((r"decoder_layer_[0-9]+", "linear", "b"), P(None)),
((r"decoder_layer_[0-9]+", "linear_v", "w"), P("data", "model")),
((r"decoder_layer_[0-9]+", "linear_v", "b"), P(None)),
(
(r"decoder_layer_[0-9]+", "linear_1", "w"),
P(
"model",
"data",
),
),
((r"decoder_layer_[0-9]+", "linear_1", "b"), P(None)),
# layer norms
((r"decoder_layer_[0-9]+", "layer_norm", "offset"), P(None)),
((r"decoder_layer_[0-9]+", "layer_norm", "scale"), P(None)),
((r"decoder_layer_[0-9]+", "layer_norm_1", "offset"), P(None)),
((r"decoder_layer_[0-9]+", "layer_norm_1", "scale"), P(None)),
# rms norms
((r"decoder_layer_[0-9]+", "rms_norm", "scale"), P(None)),
((r"decoder_layer_[0-9]+", "rms_norm_1", "scale"), P(None)),
((r"decoder_layer_[0-9]+", "rms_norm_2", "scale"), P(None)),
((r"decoder_layer_[0-9]+", "rms_norm_3", "scale"), P(None)),
# router
(("router", "w"), P("data")),
# moe mlp
(("moe", "linear", "w"), P(None, "data", "model")),
(("moe", "linear", "b"), P(None)),
(("moe", "linear_v", "w"), P(None, "data", "model")),
(("moe", "linear_v", "b"), P(None)),
(("moe", "linear_1", "w"), P(None, "model", "data")),
(("moe", "linear_1", "b"), P(None)),
# layer norms
(("moe", "layer_norm", "offset"), P(None)),
(("moe", "layer_norm", "scale"), P(None)),
(("moe", "layer_norm_1", "offset"), P(None)),
(("moe", "layer_norm_1", "scale"), P(None)),
# rms norms
(("moe", "rms_norm", "scale"), P(None)),
(("moe", "rms_norm_1", "scale"), P(None)),
(("moe", "rms_norm_2", "scale"), P(None)),
(("moe", "rms_norm_3", "scale"), P(None)),
]
LM_PARTITION_RULES = [
# Embedding layer.
(
("language_model", "positional_embeddings"),
P(None, ("data", "model")),
),
(
("language_model", "in_out_embed", "embeddings"),
P(None, ("data", "model")),
),
# Final RMSNorm.
(("language_model", "rms_norm"), P(None)),
]
TOP_K = 8
class KVMemory(NamedTuple):
k: Optional[jax.Array]
v: Optional[jax.Array]
step: Optional[jax.Array]
def init_layer_memories(
batch_size: int,
sequence_len: int,
num_kv_heads: int,
key_size: int,
num_layers: int,
step: Optional[jax.Array] = None,
dtype=jnp.bfloat16,
):
return [
KVMemory(
k=jnp.zeros((batch_size, sequence_len, num_kv_heads, key_size), dtype=dtype),
v=jnp.zeros((batch_size, sequence_len, num_kv_heads, key_size), dtype=dtype),
step=step,
)
for _ in range(num_layers)
]
class Memory(NamedTuple):
# Self-attention key/value cache.
layers: List[KVMemory]
class Router(hk.Module):
def __init__(
self,
num_selected_experts: int,
data_axis: Union[str, Tuple[str, ...]] = "data",
model_axis: Union[str, Tuple[str, ...]] = "model",
shard_activations: bool = False,
mesh: Any = None,
name: str = "router",
):
super().__init__(name)
self.shard_activations = shard_activations
self.data_axis = data_axis
self.model_axis = model_axis
self.mesh = mesh
self.num_selected_experts = num_selected_experts
def compute_routing_prob(
self, inputs: jax.Array, padding_mask: Optional[jax.Array], num_experts: int
):
return self._compute_routing_prob(inputs, padding_mask, num_experts)
@hk.transparent
def _compute_routing_prob(
self,
inputs: jax.Array,
padding_mask: Optional[jax.Array],
num_experts: int,
):
# Using fp32 for the routing prob computation.
inputs = jax.lax.convert_element_type(inputs, jnp.float32)
# [batch_size, seq_len, num_experts]
routing_logits = self._router_weights(inputs, num_experts, sharding=P("data"))
assert routing_logits.dtype == jnp.float32
routing_probs = jax.nn.softmax(routing_logits)
if padding_mask is not None:
routing_probs *= padding_mask
return routing_probs, routing_logits, 0
@hk.transparent
def _router_weights(
self,
x: jax.Array,
num_experts: int,
sharding: Optional[P] = None,
):
fprop_dtype = x.dtype
if not x.shape:
raise ValueError("Input must not be scalar.")
input_size = self.input_size = x.shape[-1]
w = hk.get_parameter(
"w", [input_size, num_experts], jnp.float32, init=hk.initializers.Constant(0)
)
if sharding:
w = with_sharding_constraint(w, sharding)
out = jnp.dot(x, w.astype(fprop_dtype))
return out
class MoELayer(hk.Module):
def __init__(
self,
num_experts: int,
layer_fn: Callable,
router: Router,
mesh: Any = None,
shard_activations: bool = False,
data_axis: Union[str, Tuple[str, ...]] = "data",
model_axis: Union[str, Tuple[str, ...]] = "model",
name: Optional[str] = "moe",
):
super().__init__(name)
self.num_experts = num_experts
self.layer_fn = layer_fn
self.router = router
self.mesh = mesh
self.shard_activations = shard_activations
self.data_axis = data_axis
self.model_axis = model_axis
@hk.transparent
def _inference_call(self, inputs: jax.Array, padding_mask: Optional[jax.Array] = None):
routing_probs, _, _ = self.router.compute_routing_prob(
inputs, padding_mask, self.num_experts
)
expert_gate, expert_index = jax.lax.top_k(routing_probs, k=self.router.num_selected_experts)
tmp = jnp.reshape(inputs, (inputs.shape[0] * inputs.shape[1], inputs.shape[2]))
broad_inputs = jnp.tile(tmp[:, jnp.newaxis, :], (1, self.router.num_selected_experts, 1))
broad_inputs = jnp.reshape(
broad_inputs, (broad_inputs.shape[0] * broad_inputs.shape[1], broad_inputs.shape[2])
)
init_fn, _ = hk.transform(self.layer_fn)
vmapped_init_fn = jax.vmap(init_fn, in_axes=0, out_axes=0)
lifted_init_fn = hk.experimental.transparent_lift(vmapped_init_fn)
# Fetch the vmapped params of the DenseBlock.
params = lifted_init_fn(
jax.random.split(jax.random.PRNGKey(1), self.num_experts),
jnp.zeros((self.num_experts, 1, 1, inputs.shape[-1])),
)
# Index and prob are in the shape [m, 2] indicating which token assigned to which experts.
# b: num_expert
# m: token or sequence dim
# k: input embed dim
# n: output embed dim
# e: the number of experts chosen for each token
@functools.partial(
shard_map,
mesh=self.mesh,
in_specs=(
P(self.data_axis, None),
P(None, None, self.model_axis),
P(None, None, self.model_axis),
P(None),
P(None),
),
out_specs=P(self.data_axis, self.model_axis),
check_rep=False,
)
def moe_slow_matmul1(input, weight, scales, index, prob):
weight = weight * scales
one_hot_indices = jax.nn.one_hot(index.reshape(-1), 8, axis=0)
all_expert_output = jnp.einsum("mk,bkn->bmn", input, weight)
output = jnp.einsum("bm,bmn->mn", one_hot_indices, all_expert_output)
return output
@functools.partial(
shard_map,
mesh=self.mesh,
in_specs=(
P(self.data_axis, self.model_axis),
P(None, self.model_axis, None),
P(None, self.model_axis, None),
P(None),
P(None),
),
out_specs=P(self.data_axis, None),
check_rep=False,
)
def moe_slow_matmul2(input, weight, scales, index, prob):
weight = weight * scales
one_hot_indices = jax.nn.one_hot(index.reshape(-1), 8, axis=0)
all_expert_output = jnp.einsum("mk,bkn->bmn", input, weight)
output = jnp.einsum("bm,bmn->mn", one_hot_indices, all_expert_output)
return jax.lax.psum(output, axis_name="model")
if hasattr(params["linear"]["w"], "scales"):
x = moe_slow_matmul1(
broad_inputs,
params["linear_v"]["w"].weight,
params["linear_v"]["w"].scales,
expert_index,
expert_gate,
)
y = moe_slow_matmul1(
broad_inputs,
params["linear"]["w"].weight,
params["linear"]["w"].scales,
expert_index,
expert_gate,
)
y = jax.nn.gelu(y)
out = moe_slow_matmul2(
x * y,
params["linear_1"]["w"].weight,
params["linear_1"]["w"].scales,
expert_index,
expert_gate,
)
out = jnp.reshape(
out,
[
inputs.shape[0],
inputs.shape[1],
self.router.num_selected_experts,
out.shape[-1],
],
)
out = expert_gate[:, :, :, None].astype(jnp.bfloat16) * out
out = jnp.sum(out, axis=2)
out = out.astype(jnp.bfloat16)
else:
# This is only here so that we can construct a valid init_fn with this code.
return inputs
return out
def __call__(self, inputs: jax.Array, padding_mask: jax.Array):
return self._inference_call(inputs)
class MHAOutput(NamedTuple):
"""Outputs of the multi-head attention operation."""
embeddings: jax.Array
memory: Any
class DecoderOutput(NamedTuple):
embeddings: jax.Array
memory: Any
class TransformerOutput(NamedTuple):
embeddings: jax.Array
memory: Any
@dataclass
class TransformerConfig:
emb_size: int
key_size: int
num_q_heads: int
num_kv_heads: int
num_layers: int
vocab_size: int = 128 * 1024
widening_factor: float = 4.0
attn_output_multiplier: float = 1.0
name: Optional[str] = None
num_experts: int = -1
capacity_factor: float = 1.0
num_selected_experts: int = 1
init_scale: float = 1.0
shard_activations: bool = False
# Used for activation sharding.
data_axis: Union[str, Tuple[str, ...]] = "data"
model_axis: Union[str, Tuple[str, ...]] = "model"
def __post_init__(self):
if isinstance(self.data_axis, list):
self.data_axis = tuple(self.data_axis)
if isinstance(self.model_axis, list):
self.model_axis = tuple(self.model_axis)
def partition_rules(self):
return TRANSFORMER_PARTITION_RULES
def make(self, mesh=None) -> "Transformer":
data_axis = tuple(self.data_axis) if isinstance(self.data_axis, list) else self.data_axis
model_axis = (
tuple(self.model_axis) if isinstance(self.model_axis, list) else self.model_axis
)
return Transformer(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
widening_factor=self.widening_factor,
key_size=self.key_size,
init_scale=self.init_scale,
mesh=mesh,
attn_output_multiplier=self.attn_output_multiplier,
shard_activations=self.shard_activations,
num_layers=self.num_layers,
num_experts=self.num_experts,
num_selected_experts=self.num_selected_experts,
data_axis=data_axis,
model_axis=model_axis,
)
def get_memory_sharding(self):
return Memory(
layers=[
KVMemory(
k=P(self.data_axis, self.model_axis),
v=P(self.data_axis, self.model_axis),
step=P(self.data_axis),
)
for _ in range(self.num_layers)
],
)
def hk_rms_norm(
x: jax.Array,
fixed_scale=False,
sharding=P(None),
) -> jax.Array:
"""Applies a unique LayerNorm to x with default settings."""
ln = RMSNorm(axis=-1, create_scale=not fixed_scale, sharding=sharding)
return ln(x)
def make_attention_mask(
query_input: jax.Array,
key_input: jax.Array,
pairwise_fn: Callable[..., Any] = jnp.multiply,
dtype: Any = jnp.bfloat16,
):
"""Mask-making helper for attention weights.
In case of 1d inputs (i.e., `[batch..., len_q]`, `[batch..., len_kv]`, the
attention weights will be `[batch..., heads, len_q, len_kv]` and this
function will produce `[batch..., 1, len_q, len_kv]`.
Args:
query_input: a batched, flat input of query_length size
key_input: a batched, flat input of key_length size
pairwise_fn: broadcasting elementwise comparison function
dtype: mask return dtype
Returns:
A `[batch..., 1, len_q, len_kv]` shaped mask for 1d attention.
"""
mask = pairwise_fn(jnp.expand_dims(query_input, axis=-1), jnp.expand_dims(key_input, axis=-2))
mask = jnp.expand_dims(mask, axis=-3)
return mask.astype(dtype)
class Linear(hk.Linear):
def __init__(
self,
output_size: int,
with_bias: bool = True,
sharding: Optional[P] = None,
mesh: Any = None,
name: Optional[str] = None,
shard_axis: int = 0,
):
super().__init__(
output_size=output_size,
with_bias=with_bias,
name=name,
)
self.sharding = sharding
self.mesh = mesh
self.shard_axis = shard_axis
def __call__(
self,
inputs: jax.Array,
) -> jax.Array:
"""Computes a linear transform of the input."""
fprop_dtype = inputs.dtype
if not inputs.shape:
raise ValueError("Input must not be scalar.")
input_size = self.input_size = inputs.shape[-1]
output_size = self.output_size
w = hk.get_parameter(
"w", [input_size, output_size], jnp.float32, init=hk.initializers.Constant(0)
)
if hasattr(w, "scales"):
shape = inputs.shape
inputs = jnp.reshape(inputs, (-1, shape[-1]))
@functools.partial(
shard_map,
mesh=self.mesh,
in_specs=(self.sharding, self.sharding),
out_specs=self.sharding,
check_rep=False,
)
def mul(w, s):
return w.astype(s.dtype) * s
w = mul(w.weight, w.scales)
out = jnp.dot(inputs, w.astype(fprop_dtype))
if self.with_bias:
b = hk.get_parameter(
"b", [self.output_size], jnp.float32, init=hk.initializers.Constant(0)
)
b = jnp.broadcast_to(b, out.shape)
out = out + b.astype(fprop_dtype)
return out
class RMSNorm(hk.RMSNorm):
def __init__(
self,
axis: Union[int, Sequence[int], slice],
eps: float = 1e-5,
name: Optional[str] = None,
create_scale: bool = True,
sharding: Optional[P] = None,
):
super().__init__(axis, eps, create_scale=create_scale, name=name)
self.sharding = sharding
def __call__(self, inputs: jax.Array):
fprop_dtype = inputs.dtype
param_shape = (inputs.shape[-1],)
if self.create_scale:
scale = hk.get_parameter(
"scale",
param_shape,
dtype=jnp.float32,
init=hk.initializers.Constant(0),
)
if self.sharding:
scale = with_sharding_constraint(scale, self.sharding)
scale = jnp.broadcast_to(scale.astype(jnp.float32), inputs.shape)
else:
scale = 1.0
inputs = inputs.astype(jnp.float32)
scale = scale.astype(jnp.float32)
mean_squared = jnp.mean(jnp.square(inputs), axis=[-1], keepdims=True)
mean_squared = jnp.broadcast_to(mean_squared, inputs.shape)
normed_inputs = inputs * jax.lax.rsqrt(mean_squared + self.eps)
outputs = scale * normed_inputs
return outputs.astype(fprop_dtype)
def rotate_half(
x: jax.Array,
) -> jax.Array:
"""Obtain the rotated counterpart of each feature"""
x1, x2 = jnp.split(x, 2, axis=-1)
return jnp.concatenate((-x2, x1), axis=-1)
class RotaryEmbedding(hk.Module):
"""Applies rotary embeddings (RoPE) to the input sequence tensor,
as described in https://arxiv.org/abs/2104.09864.
Attributes:
dim (int): Dimensionality of the feature vectors
base_exponent (int): Base exponent to compute embeddings from
"""
def __init__(
self,
dim: int,
name: Optional[str] = None,
base_exponent: int = 10000,
):
super().__init__(name)
self.dim = dim
self.base_exponent = base_exponent
assert self.dim % 2 == 0
def __call__(
self,
x: jax.Array,
seq_dim: int,
offset: jax.Array,
const_position: Optional[int] = None,
t: Optional[jax.Array] = None,
) -> jax.Array:
fprop_dtype = x.dtype
# Compute the per-dimension frequencies
exponents = jnp.arange(0, self.dim, 2, dtype=jnp.float32)
inv_freq = jnp.asarray(
1.0 / (self.base_exponent ** (exponents / self.dim)), dtype=jnp.float32
)
if jnp.shape(offset) == ():
# Offset can be a scalar or one offset per batch element.
offset = jnp.expand_dims(offset, 0)
# Compute the per element phase (to pass into sin and cos)
if const_position:
t = const_position * jnp.ones(
(
1,
x.shape[seq_dim],
),
dtype=jnp.float32,
)
elif t is None:
t = jnp.arange(x.shape[seq_dim], dtype=jnp.float32) + jnp.expand_dims(offset, -1)
phase = jnp.einsum("bi,j->bij", t, inv_freq)
phase = jnp.tile(phase, reps=(1, 2))[:, :, None, :]
x = x * jnp.cos(phase) + rotate_half(x) * jnp.sin(phase)
x = x.astype(fprop_dtype)
return x
class MultiHeadAttention(hk.Module):
def __init__(
self,
num_q_heads: int,
num_kv_heads: int,
key_size: int,
*,
with_bias: bool = True,
value_size: Optional[int] = None,
model_size: Optional[int] = None,
attn_output_multiplier: 1.0,
data_axis: Union[str, Tuple[str, ...]] = "data",
model_axis: Union[str, Tuple[str, ...]] = "model",
name: Optional[str] = None,
):
super().__init__(name=name)
self.num_q_heads = num_q_heads
self.num_kv_heads = num_kv_heads
self.key_size = key_size
self.value_size = value_size or key_size
self.model_size = model_size or key_size * num_q_heads
self.data_axis = data_axis
self.model_axis = model_axis
self.attn_output_multiplier = attn_output_multiplier
self.with_bias = with_bias
def __call__(
self,
query: jax.Array,
key: Optional[jax.Array],
value: Optional[jax.Array],
mask: Optional[jax.Array] = None,
kv_memory: Optional[KVMemory] = None,
mesh: Any = None,
) -> MHAOutput:
# In shape hints below, we suppress the leading dims [...] for brevity.
# Hence e.g. [A, B] should be read in every case as [..., A, B].
sequence_length = query.shape[1]
projection = self._linear_projection
use_memory = False
if kv_memory is not None:
if kv_memory.k is None:
assert kv_memory.v is None
assert key is not None
assert value is not None
else:
assert kv_memory.v is not None
use_memory = True
else:
assert key is not None
assert value is not None
# Check that the keys and values have consistent batch size and sequence length.
if not use_memory:
assert key.shape[:2] == value.shape[:2], f"key/value shape: {key.shape}/{value.shape}"
if mask is not None:
assert mask.ndim == 4
assert mask.shape[0] in {
1,
query.shape[0],
}, f"mask/query shape: {mask.shape}/{query.shape}"
if not use_memory:
assert key.shape[0] in {
1,
query.shape[0],
}, f"key/query shape: {key.shape}/{query.shape}"
assert mask.shape[1] == 1
assert mask.shape[2] in {
1,
query.shape[1],
}, f"mask/query shape: {mask.shape}/{query.shape}"
if not use_memory:
assert mask.shape[3] in {
1,
key.shape[1],
}, f"mask/query shape: {mask.shape}/{key.shape}"
# Compute key/query/values (overload K/Q/V to denote the respective sizes).
assert self.num_q_heads % self.num_kv_heads == 0
query_heads = projection(
query,
self.key_size,
self.num_q_heads,
name="query",
sharding=P("data", "model"),
mesh=mesh,
) # [B, T', H, Q=K]
new_memory = None
key_heads = projection(
key,
self.key_size,
self.num_kv_heads,
name="key",
sharding=P("data", "model"),
mesh=mesh,
) # [B, T, H, K]
value_heads = projection(
value,
self.value_size,
self.num_kv_heads,
name="value",
sharding=P("data", "model"),
mesh=mesh,
) # [B, T, H, V]
rotate = RotaryEmbedding(dim=self.key_size, base_exponent=int(1e4))
key_heads = rotate(key_heads, seq_dim=1, offset=(kv_memory.step if kv_memory else 0))
query_heads = rotate(query_heads, seq_dim=1, offset=(kv_memory.step if kv_memory else 0))
@functools.partial(jax.vmap)
def update_into(mem, start, update):
return jax.lax.dynamic_update_slice_in_dim(mem, update, start, axis=0)
if kv_memory:
if mesh is not None:
@functools.partial(
shard_map,
mesh=mesh,
in_specs=(
P("data", None, "model"),
P("data"),
P("data", None, "model"),
),
out_specs=P("data", None, "model"),
check_rep=False,
)
def update_into_shmap(mems, starts, updates):
return update_into(mems, starts, updates)
key_heads = update_into_shmap(kv_memory.k, kv_memory.step, key_heads)
value_heads = update_into_shmap(kv_memory.v, kv_memory.step, value_heads)
else:
key_heads = update_into(kv_memory.k, kv_memory.step, key_heads)
value_heads = update_into(kv_memory.v, kv_memory.step, value_heads)
new_step = kv_memory.step + sequence_length
memory_mask = jnp.arange(kv_memory.k.shape[1]) < new_step[:, None]
memory_mask = memory_mask[:, None, None, :] # [B, H, T, T]
if mask is not None:
mask = memory_mask * mask
else:
mask = memory_mask
new_memory = KVMemory(
k=key_heads,
v=value_heads,
step=new_step,
)
# Add separate dimension for grouped query heads.
query_heads = with_sharding_constraint(query_heads, P(self.data_axis, None, "model", None))
key_heads = with_sharding_constraint(key_heads, P(self.data_axis, None, "model", None))
value_heads = with_sharding_constraint(value_heads, P(self.data_axis, None, "model", None))
b, t, h, d = query_heads.shape
_, _, kv_h, _ = key_heads.shape
assert h % kv_h == 0, f"query_heads {h} must be a multiple of kv_heads {kv_h}"
query_heads = jnp.reshape(query_heads, (b, t, kv_h, h // kv_h, d))
query_heads = with_sharding_constraint(
query_heads, P(self.data_axis, None, "model", None, None)
)
# Compute attention weights.
# Attention softmax is always carried out in fp32.
attn_logits = jnp.einsum("...thHd,...Thd->...hHtT", query_heads, key_heads).astype(
jnp.float32
)
attn_logits *= self.attn_output_multiplier
max_attn_val = jnp.array(30.0, dtype=attn_logits.dtype)
attn_logits = max_attn_val * jnp.tanh(attn_logits / max_attn_val)
mask = mask[:, :, None, :, :]
if mask is not None:
if mask.ndim != attn_logits.ndim:
raise ValueError(
f"Mask dimensionality {mask.ndim} must match logits dimensionality "
f"{attn_logits.ndim} for {mask.shape}/{attn_logits.shape}."
)
attn_logits = jnp.where(mask, attn_logits, -1e30)
attn_weights = jax.nn.softmax(attn_logits).astype(query.dtype) # [H, T', T]
# Weight the values by the attention and flatten the head vectors.
attn = jnp.einsum("...hHtT,...Thd->...thHd", attn_weights, value_heads)
attn = with_sharding_constraint(attn, P(self.data_axis, None, "model", None, None))
leading_dims = attn.shape[:2]
attn = jnp.reshape(attn, (*leading_dims, -1)) # [T', H*V]
attn = with_sharding_constraint(attn, P(self.data_axis, None, "model"))
# Apply another projection to get the final embeddings.
final_projection = Linear(
self.model_size,
with_bias=False,
sharding=P("model", "data"),
mesh=mesh,
)
return MHAOutput(final_projection(attn), new_memory)
@hk.transparent
def _linear_projection(
self,
x: jax.Array,
head_size: int,
num_heads: int,
sharding: Optional[P] = None,
name: Optional[str] = None,
mesh: Any = None,
) -> jax.Array:
y = Linear(
num_heads * head_size,
with_bias=False,
name=name,
sharding=sharding,
mesh=mesh,
)(x)
*leading_dims, _ = x.shape
return y.reshape((*leading_dims, num_heads, head_size))
@dataclass
class MHABlock(hk.Module):
"""A MHA Block"""
num_q_heads: int
num_kv_heads: int
key_size: int
attn_output_multiplier: float = 1.0
mesh: Any = None
data_axis: Union[str, Tuple[str, ...]] = "data"
model_axis: Union[str, Tuple[str, ...]] = "model"
@hk.transparent
def __call__(
self,
inputs: jax.Array, # [B, T, D]
mask: jax.Array, # [B, 1, T, T] or [B, 1, 1, T] or B[1, 1, 1, 1]
layer_memory: Optional[KVMemory],
) -> MHAOutput:
_, _, model_size = inputs.shape
assert mask.ndim == 4, f"shape: {mask.shape}"
assert mask.shape[2] in {1, inputs.shape[1]}, str(mask.shape)
assert mask.shape[3] in {1, inputs.shape[1]}, str(mask.shape)
side_input = inputs
def attn_block(query, key, value, mask, memory) -> MHAOutput:
return MultiHeadAttention(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
key_size=self.key_size,
model_size=model_size,
data_axis=self.data_axis,
model_axis=self.model_axis,
attn_output_multiplier=self.attn_output_multiplier,
)(
query,
key,
value,
mask,
memory,
mesh=self.mesh,
)
attn_output = attn_block(inputs, side_input, side_input, mask, layer_memory)
h_attn = attn_output.embeddings
return attn_output._replace(embeddings=h_attn)
@dataclass
class DenseBlock(hk.Module):
num_q_heads: int
num_kv_heads: int
key_size: int
widening_factor: float = 4.0
sharding_constraint: bool = False
mesh: Any = None
@hk.transparent
def __call__(
self,
inputs: jax.Array, # [B, T, D]
) -> jax.Array: # [B, T, D]
_, _, model_size = inputs.shape
h_v = Linear(
ffn_size(
model_size,
self.widening_factor,
),
with_bias=False,
mesh=self.mesh,
sharding=P("data", "model"),
name="linear_v",
)(inputs)
h_w1 = jax.nn.gelu(
Linear(
ffn_size(
model_size,
self.widening_factor,
),
with_bias=False,
mesh=self.mesh,
sharding=P("data", "model"),
)(inputs)
)
h_dense = Linear(
model_size,
with_bias=False,
sharding=P("model", "data"),
mesh=self.mesh,
shard_axis=1,
)(h_w1 * h_v)
return h_dense
@dataclass
class DecoderLayer(hk.Module):
"""A transformer stack."""
num_q_heads: int
num_kv_heads: int
key_size: int
num_layers: int
# MoE.
num_experts: int
layer_index: Optional[int] = None
num_selected_experts: int = 1
widening_factor: float = 4.0
name: Optional[str] = None
data_axis: Union[str, Tuple[str, ...]] = "data"
model_axis: Union[str, Tuple[str, ...]] = "model"
shard_activations: bool = False
attn_output_multiplier: float = 1.0
mesh: Any = None
def __call__(
self,
inputs: jax.Array, # [B, T, D]
mask: jax.Array, # [B, 1, T, T] or [B, 1, 1, T]
padding_mask: Optional[jax.Array],
layer_memory: Optional[KVMemory],
) -> DecoderOutput:
"""Transforms input embedding sequences to output embedding sequences."""
def layer_norm(x):
return hk_rms_norm(x)
if self.shard_activations:
sharding = P(self.data_axis, None, self.model_axis)
else:
sharding = P(self.data_axis, None)
h = with_sharding_constraint(inputs, sharding)
attn_output = MHABlock(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
key_size=self.key_size,
attn_output_multiplier=self.attn_output_multiplier,
mesh=self.mesh,
data_axis=self.data_axis,
model_axis=self.model_axis,
)(layer_norm(h), mask, layer_memory)
h_attn = attn_output.embeddings
h_attn = layer_norm(h_attn)
h += h_attn
h = with_sharding_constraint(h, sharding)
def base_dense_block(h):
h = DenseBlock(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
key_size=self.key_size,
widening_factor=self.widening_factor,
sharding_constraint=False,
mesh=self.mesh,
)(h)
return h
if self.num_experts > 1:
rank_logger.debug("Using MoE!")
router = Router(
num_selected_experts=self.num_selected_experts,
shard_activations=self.shard_activations,
data_axis=self.data_axis,
model_axis=self.model_axis,
mesh=self.mesh,
)
h_dense = MoELayer(
num_experts=self.num_experts,
mesh=self.mesh,
layer_fn=base_dense_block,
router=router,
shard_activations=self.shard_activations,
data_axis=self.data_axis,
model_axis=self.model_axis,
)(layer_norm(h), padding_mask)
else:
h_dense = base_dense_block(layer_norm(h))
h_dense = layer_norm(h_dense)
h += h_dense
h = with_sharding_constraint(h, sharding)
return DecoderOutput(
embeddings=h,
memory=attn_output.memory,
)
class LanguageModelOutput(NamedTuple):
logits: jax.Array
model_state: Any
class InOutEmbed(hk.Embed):
"""Module for embedding tokens in a low-dimensional space."""
def __init__(
self,
vocab_size: Optional[int] = None,
embed_dim: Optional[int] = None,
sharding: Optional[P] = None,
name: Optional[str] = None,
):
super().__init__(
vocab_size=vocab_size,
embed_dim=embed_dim,
name=name,
)
self.sharding = sharding
@property
def embeddings(self):
embed_mat = hk.get_parameter(
"embeddings",
[self.vocab_size, self.embed_dim],
dtype=jnp.float32,
init=hk.initializers.Constant(0),
)
if self.sharding:
embed_mat = with_sharding_constraint(embed_mat, self.sharding)
return embed_mat
def decode(
self,
inputs: jax.Array,
) -> jax.Array:
return jnp.dot(inputs, self.embeddings.T.astype(inputs.dtype))
@dataclass
class LanguageModelConfig:
"""An autoregressive transformer-based language model."""
model: Optional[TransformerConfig]
vocab_size: int
pad_token: int
eos_token: int
sequence_len: int
model_size: int = 0
embedding_init_scale: float = 1.0
embedding_multiplier_scale: float = 1.0
output_multiplier_scale: float = 1.0
name: Optional[str] = None
fprop_dtype: Any = jnp.bfloat16
model_type: Optional[str] = None
init_scale_override: Optional[float] = None
shard_embeddings: bool = True
_initialized = False
def initialize(self):
# We cannot specify [] as a default value (it is mutable), hence None.
model_config = self.model
assert self.init_scale_override is None, (
"Overriding model initialize scale is supported only for predefined models."
)
if self.model_size == 0:
self.model_size = model_config.emb_size
assert self.model is not None, "Model could not be initialized."
self._initialized = True
return self
def make(self, *args, **kwargs):
if not self._initialized:
logger.warning(
f"LanguageModel {self.name} is not initialized. Initializing for one replica."
)
self.initialize()
return LanguageModel(
model=self.model.make(*args, **kwargs),
config=self,
fprop_dtype=self.fprop_dtype,
mesh=kwargs.get("mesh", None),
)
def partition_rules(self):
return LM_PARTITION_RULES + self.model.partition_rules()
def layer_norm(x, model):
return hk_rms_norm(x)
@dataclass
class LanguageModel(hk.Module):
"""An autoregressive transformer-based language model."""
model: "Transformer"
config: LanguageModelConfig
fprop_dtype: Any = jnp.bfloat16
name: Optional[str] = None
mesh: Any = None
def __call__(
self,
tokens: jax.Array,
memory: Optional[Memory] = None,
*,
batch: Dict[str, jax.Array] = {},
last_hid_only: bool = False,
length: Optional[jax.Array] = None,
) -> LanguageModelOutput:
"""Forward pass, producing a sequence of logits."""
del batch # Unused.
config = self.config
input_mask = jnp.greater(tokens, config.pad_token)
# Embed the input tokens and positions.
in_out_embed = InOutEmbed(
self.config.vocab_size,
embed_dim=self.config.model_size,
sharding=P(None, ("data", "model")),
)
input_embeddings = in_out_embed(tokens).astype(config.fprop_dtype)
input_embeddings = with_sharding_constraint(
input_embeddings, P("data", None, self.model.model_axis)
)
input_embeddings *= config.embedding_multiplier_scale
model_output = self.model(
input_embeddings,
input_mask,
memory=memory,
) # [B, T, D]
embeddings, model_state = model_output.embeddings, model_output.memory
if self.model.shard_activations:
embeddings = with_sharding_constraint(
embeddings, P("data", None, self.model.model_axis)
)
else:
embeddings = with_sharding_constraint(embeddings, P("data", None))
rank_logger.debug(f"Final embedding shape: {embeddings.shape}")
embeddings = layer_norm(embeddings, self.model)
assert embeddings.dtype == self.fprop_dtype
if last_hid_only:
last_step = jnp.maximum(jnp.sum(input_mask.astype(jnp.int32), axis=1) - 1, 0)
last_hid = jax.vmap(lambda x, i: x[i], in_axes=0, out_axes=0)(embeddings, last_step)
return last_hid
if length is not None:
last_step = jnp.maximum(length.astype(jnp.int32) - 1, 0)
embeddings = jax.vmap(lambda x, i: x[i], in_axes=0, out_axes=0)(embeddings, last_step)
embeddings = jnp.expand_dims(embeddings, axis=1)
# Decode the embeddings (here, we use tied weights).
rank_logger.info(embeddings.shape)
out = in_out_embed.decode(embeddings)
rank_logger.info(out.shape)
out *= config.output_multiplier_scale
if self.model.shard_activations:
out = with_sharding_constraint(out, P("data", None, self.model.model_axis))
else:
out = with_sharding_constraint(out, P("data", None))
return LanguageModelOutput(
logits=out,
model_state=model_state,
)
def init_memory(self, batch_size: int, seq_len: int, dtype=jnp.bfloat16):
return self.model.init_memory(batch_size=batch_size, sequence_len=seq_len, dtype=dtype)
def prefill_memory(self, prompts, memory):
# Pad to the left and right align?
# Basically assume prompt is already padded
model_output = self(prompts, memory=memory)
return model_output.logits, model_output.model_state
@dataclass
class Transformer(hk.Module):
"""A transformer stack."""
num_q_heads: int
num_kv_heads: int
key_size: int
widening_factor: float
init_scale: float
mesh: Any
attn_output_multiplier: float
shard_activations: bool
num_layers: int
# MoE
num_experts: int
num_selected_experts: int
name: Optional[str] = None
# Used for activation sharding
data_axis: Union[str, Tuple[str, ...]] = "data"
model_axis: Union[str, Tuple[str, ...]] = "model"
def init_memory(self, batch_size: int, sequence_len: int, dtype=jnp.bfloat16):
return Memory(
layers=init_layer_memories(
batch_size,
sequence_len,
self.num_kv_heads,
self.key_size,
self.num_layers,
step=jnp.zeros(batch_size, dtype=jnp.int32),
dtype=dtype,
),
)
def __call__(
self,
embeddings: jax.Array, # [B, T, D]
mask: jax.Array, # [B, T]
memory: Optional[Memory],
) -> TransformerOutput:
"""Transforms input embedding sequences to output embedding sequences."""
fprop_dtype = embeddings.dtype
_, seq_len, model_size = embeddings.shape
padding_mask = mask.copy()
mask = mask[:, None, None, :] # [B, H=1, T'=1, T]
# Compute causal mask for autoregressive sequence modelling.
causal_mask = jnp.tril(jnp.ones((1, 1, seq_len, seq_len))).astype(
fprop_dtype
) # [B=1, H=1, T, T]
mask = mask * causal_mask # [B, H=1, T, T]
h = embeddings
kv_memories = []
def block(
h,
mask,
padding_mask,
memory,
layer_index: Optional[int] = None,
widening_factor: Optional[int] = None,
name: Optional[str] = None,
) -> DecoderOutput:
return DecoderLayer(
num_q_heads=self.num_q_heads,
num_kv_heads=self.num_kv_heads,
key_size=self.key_size,
widening_factor=widening_factor or self.widening_factor,
num_layers=self.num_layers,
mesh=self.mesh,
data_axis=self.data_axis,
model_axis=self.model_axis,
attn_output_multiplier=self.attn_output_multiplier,
shard_activations=self.shard_activations,
# MoE.
num_experts=self.num_experts,
num_selected_experts=self.num_selected_experts,
name=name,
layer_index=layer_index,
)(
h,
mask,
padding_mask,
memory,
)
for i in range(self.num_layers):
decoder_output = block(
h,
mask,
padding_mask,
memory.layers[i] if memory else None,
layer_index=i,
name=f"decoder_layer_{i}",
)
h, new_kv_memory = (
decoder_output.embeddings,
decoder_output.memory,
)
kv_memories.append(new_kv_memory)
return TransformerOutput(
embeddings=h,
memory=Memory(layers=kv_memories),
)
================================================
FILE: pyproject.toml
================================================
[tool.ruff]
indent-width = 4
line-length = 100
[tool.ruff.lint]
ignore = [
"E722",
"E731",
"E741",
"F405",
"E402",
"F403",
]
select = ["ISC001"]
================================================
FILE: requirements.txt
================================================
dm_haiku==0.0.12
jax[cuda12-pip]==0.4.25 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
numpy==1.26.4
sentencepiece==0.2.0
================================================
FILE: run.py
================================================
# 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/"
def main():
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()
inp = "The answer to life the universe and everything is of course"
print(f"Output for prompt: {inp}", sample_from_model(gen, inp, max_len=100, temperature=0.01))
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
main()
================================================
FILE: runners.py
================================================
# 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 functools
import logging
import math
import re
from dataclasses import dataclass
from typing import Any, Callable, NamedTuple, Optional, Tuple
import haiku as hk
import jax
import jax.experimental.pjit as pjit
import jax.numpy as jnp
import numpy as np
import sentencepiece
from jax.experimental import mesh_utils
from jax.sharding import PartitionSpec as P
from jax.typing import ArrayLike
import checkpoint as xai_checkpoint
from model import (
LanguageModelConfig,
LanguageModelOutput,
TrainingState,
apply_rules,
Memory,
KVMemory,
)
logger = logging.getLogger(__name__)
rank_logger = logging.getLogger("rank")
TOP_K = 8
class SampleSettings(NamedTuple):
temperature: ArrayLike
nucleus_p: ArrayLike
mask: ArrayLike
# Whether a given batch element is actively used. [B]
active: ArrayLike
class SampleOutput(NamedTuple):
token_id: ArrayLike
prob: ArrayLike
top_k_token_ids: ArrayLike
top_k_probs: ArrayLike
def insert_slice(memory: Memory, slice, length, i):
slice = Memory(
layers=[
KVMemory(layer.k, layer.v, step=jnp.array([length]))
for layer in slice.layers
],
)
return jax.tree_map(lambda m, u: jax.lax.dynamic_update_index_in_dim(m, u[0], i, axis=0),
memory, slice)
def pad_to_size(x, size):
if x.shape[0] > size:
# Left truncate if the context is too long.
x = x[-size:]
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:
"""Performs nucleus filtering on logits."""
assert logits.ndim == top_p.ndim, f"Expected {logits.ndim} equal {top_p.ndim}"
sorted_logits = jax.lax.sort(logits, is_stable=False)
sorted_probs = jax.nn.softmax(sorted_logits)
threshold_idx = jnp.argmax(jnp.cumsum(sorted_probs, -1) >= 1 - top_p, axis=-1)
threshold_largest_logits = jnp.take_along_axis(
sorted_logits, threshold_idx[..., jnp.newaxis], axis=-1
)
assert threshold_largest_logits.shape == logits.shape[:-1] + (1,)
mask = logits >= threshold_largest_logits
# Set unused logits to -inf.
logits = jnp.where(mask, logits, -1e10)
return logits
def sample_token(
rngs: jax.random.PRNGKey,
lm_outputs: LanguageModelOutput,
settings: SampleSettings,
) -> SampleOutput:
# Expand the settings shape to match the logit shape.
settings = SampleSettings(
temperature=jnp.expand_dims(settings.temperature, (1, 2)), # Input [B], output [B, 1, 1].
nucleus_p=jnp.expand_dims(settings.nucleus_p, (1, 2)), # Input [B], output [B, 1, 1].
mask=jnp.expand_dims(settings.mask, 1), # Input [B, V], output [B, 1, V].
active=settings.active, # [B].
)
logits = lm_outputs.logits / settings.temperature.astype(lm_outputs.logits.dtype)
# Mask out all disallowed tokens by assigning them a near-zero probability.
logits = jnp.where(settings.mask, logits, -1e10)
# Mask out all tokens that don't fall into the p-th percentile.
logits = top_p_filter(logits, settings.nucleus_p.astype(logits.dtype))
new_token = jax.vmap(jax.random.categorical)(rngs, logits)
probabilities = jax.nn.softmax(logits)
token_prob = jnp.take_along_axis(probabilities, jnp.expand_dims(new_token, 1), axis=2)
token_prob = jnp.squeeze(token_prob, 1)
# Gather the top-k tokens and probabilities.
top_k_probs, top_k_token_ids = jax.lax.top_k(probabilities, TOP_K)
top_k_probs = jnp.squeeze(top_k_probs, 1)
top_k_token_ids = jnp.squeeze(top_k_token_ids, 1)
return SampleOutput(
new_token,
token_prob,
top_k_token_ids,
top_k_probs,
)
@dataclass
class ModelRunner:
model: LanguageModelConfig
bs_per_device: float = 2.0
load_rename_rules: Optional[list[tuple[str, str]]] = None
load_exclude_rules: Optional[list[str]] = None
rng_seed: int = 42 # Initial rng seed.
transform_forward: bool = False
checkpoint_path: str = ""
def make_forward_fn(self, mesh: Any):
def forward(tokens):
out = self.model.make(mesh=mesh)(tokens)
return out, None
if self.transform_forward:
forward = hk.transform(forward)
return forward
def initialize(
self,
init_data,
local_mesh_config: tuple[int, int],
between_hosts_config: tuple[int, int],
):
num_replicas = math.prod(between_hosts_config)
self.model.initialize()
self.model.fprop_dtype = jnp.bfloat16
num_local_gpus = len(jax.local_devices())
# Calculate the global batch size from the local batch size.
self.batch_size = int(self.bs_per_device * num_local_gpus * num_replicas)
# Calculate the batch size per host from the global batch size.
self.local_batch_size = self.batch_size // jax.process_count()
self.local_mesh_config = local_mesh_config
self.between_hosts_config = between_hosts_config
rank_logger.info(
f"Initializing mesh for {self.local_mesh_config=} {self.between_hosts_config=}..."
)
self.mesh = make_mesh(self.local_mesh_config, self.between_hosts_config)
self.forward = self.make_forward_fn(mesh=self.mesh)
self.logits_fn = hk.transform(lambda tokens: self.forward(tokens)[0])
self.eval_forward = self.make_forward_fn(mesh=self.mesh)
self.logits_eval_fn = hk.transform(lambda tokens: self.eval_forward(tokens)[0])
if self.transform_forward:
self.state_sharding = self.get_state_sharding(init_data)
rank_logger.info(f"State sharding type: {type(self.state_sharding)}")
self.init_fn = pjit.pjit(self.init, out_shardings=self.state_sharding)
def init(self, rng: jax.Array, data) -> TrainingState:
assert self.transform_forward
rng, init_rng = jax.random.split(rng)
params = self.forward.init(init_rng, data["inputs"])
return TrainingState(params=params)
def get_state_sharding(self, init_data):
assert self.transform_forward
rng = jax.random.PRNGKey(self.rng_seed)
rank_logger.info(f"partition rules: {self.model.partition_rules}")
with self.mesh:
shapes = jax.eval_shape(self.init, rng, init_data)
sharding = jax.tree_util.tree_map_with_path(
apply_rules(self.model.partition_rules()),
shapes,
)
return sharding
def load_or_init(
self,
init_data: Any,
from_checkpoint: bool = True,
init_fn: Optional[Callable] = None,
):
rng = jax.random.PRNGKey(self.rng_seed)
if not self.checkpoint_path or not from_checkpoint:
rank_logger.info("Initializing model...")
with self.mesh:
if init_fn is not None:
state = init_fn(rng, init_data)
else:
assert self.transform_forward
state = self.init_fn(rng, init_data)
rank_logger.info("Model state is newly initialized.")
else:
with self.mesh:
if init_fn:
state_shapes = jax.eval_shape(init_fn, rng, init_data)
else:
assert self.transform_forward
state_shapes = jax.eval_shape(self.init_fn, rng, init_data)
init_state = None
state = xai_checkpoint.restore(
checkpoint_path=self.checkpoint_path,
state_shapes=state_shapes,
mesh=self.mesh,
between_hosts_config=self.between_hosts_config,
state_sharding=self.state_sharding,
init_state=init_state,
params_only=True,
)
del init_state
return state
@dataclass
class Request:
prompt: str
temperature: float
nucleus_p: float
rng_seed: int
max_len: int
@dataclass
class InferenceRunner:
name: str
runner: Any
load: str
tokenizer_path: str = "/tmp/xai_data/tokenizer.model"
local_mesh_config: Tuple[int, int] = (1, 1)
between_hosts_config: Tuple[int, int] = (1, 1)
pad_sizes: tuple[int] = (1024,)
def get_pad_bucket(self, size):
i = bisect.bisect_left(self.pad_sizes, size)
return self.pad_sizes[min(i, len(self.pad_sizes) - 1)]
def initialize(self):
runner = self.runner
self.runner.transform_forward = True
dummy_data = dict(
inputs=np.zeros((1, 256), dtype=np.int32),
targets=np.zeros((1, 256), dtype=np.int32),
)
runner.initialize(
dummy_data,
local_mesh_config=self.local_mesh_config,
between_hosts_config=self.between_hosts_config,
)
self.tokenizer = sentencepiece.SentencePieceProcessor(model_file=self.tokenizer_path)
max_len = runner.model.sequence_len
self.vocab_size = self.runner.model.vocab_size
params = runner.load_or_init(dummy_data)
self.params = params
def pad_to_max_len(x):
if len(x.shape) > 1:
pad_width = max_len - x.shape[1]
return jnp.pad(x, [(0, 0), (0, pad_width), (0, 0), (0, 0)])
else:
return x
@functools.lru_cache
def lm():
return runner.model.make(mesh=runner.mesh)
def hk_forward(
tokens,
memory=None,
length=None,
active=None,
) -> LanguageModelOutput:
if memory is not None:
assert active is not None
layers = []
for l in memory.layers:
# Reset steps to 0 for inactive requests to avoid unnecessary computations.
step = jnp.where(active, l.step, jnp.zeros_like(l.step))
layers.append(l._replace(step=step))
memory = memory._replace(layers=layers)
return lm()(tokens, memory, length=length)
def hk_sample_step(rngs, last_output: SampleOutput, memory, settings):
rngs, rngs_ = jax.vmap(jax.random.split, out_axes=1)(rngs)
lm_outputs = hk_forward(last_output.token_id, memory=memory, active=settings.active)
sample_result = sample_token(rngs_, lm_outputs, settings)
return rngs, sample_result, lm_outputs.model_state
def hk_new_memory(batch_size, sequence_len):
return lm().init_memory(batch_size, sequence_len)
def hk_prefill_memory(
rngs,
memory,
settings,
last_output,
prompt,
length,
rng_seed,
new_settings,
i,
):
rng = jax.random.PRNGKey(seed=rng_seed)
rng, rng_ = jax.random.split(rng)
# Allocate new memory for this sample. The memory length is equal to the length of the
# prompt.
slice = hk_new_memory(1, prompt.shape[0])
# Move the settings for this individual batch entry into the joint settings tensor.
settings = jax.tree_map(
lambda o, v: jax.lax.dynamic_update_index_in_dim(o, v, i, axis=0),
settings,
new_settings,
)
# Get the settings for the batch entry from the joint settings tensor.
settings_slice = jax.tree_map(lambda t: jnp.expand_dims(t[i], axis=0), settings)
# Process the first n-1 tokens of the prompt.
lm_outputs = hk_forward(
jnp.expand_dims(prompt, 0),
memory=slice,
length=jnp.expand_dims(length, 0),
active=settings_slice.active,
)
# The forward pass doesn't correctly set the `step` counter inside the memory. Manually
# override it so `hk_forward` uses the correct context length in the next call.
slice = lm_outputs.model_state
slice = slice._replace(
layers=[l._replace(step=jnp.array([length])) for l in slice.layers]
)
# Sample the actual output token.
rng_ = jnp.expand_dims(rng_, 0)
new_output = sample_token(rng_, lm_outputs, settings_slice)
# Update the KV cache/memory.
slice = jax.tree_map(pad_to_max_len, slice)
memory = insert_slice(memory, slice, length, i)
rng = jnp.expand_dims(rng, 0)
rngs = jax.lax.dynamic_update_index_in_dim(rngs, rng, i, axis=0)
# Move the network outputs for this batch entry into the joint output tensor.
last_output = jax.tree_util.tree_map(
lambda last, new: jax.lax.dynamic_update_index_in_dim(last, new, i, axis=0),
last_output,
new_output,
)
return rngs, last_output, memory, settings
sample_step_ = hk.without_apply_rng(hk.transform(hk_sample_step))
prefill_memory_ = hk.without_apply_rng(hk.transform(hk_prefill_memory))
new_memory_ = hk.without_apply_rng(hk.transform(hk_new_memory))
forward_ = hk.without_apply_rng(hk.transform(hk_forward))
rng = jax.random.PRNGKey(42)
dummy_tokens = jnp.zeros((1, max_len), jnp.int32)
with runner.mesh:
shapes = jax.eval_shape(forward_.init, rng, dummy_tokens)
self.params_sharding = jax.tree_util.tree_map_with_path(
apply_rules(runner.model.partition_rules()),
shapes,
)
ds = P("data")
ms = runner.model.model.get_memory_sharding()
self.sample_step = pjit.pjit(
sample_step_.apply,
in_shardings=(self.params_sharding, None, ds, ms, None),
out_shardings=(None, ds, ms),
donate_argnums=3,
)
self.prefill_memory = pjit.pjit(
functools.partial(prefill_memory_.apply),
in_shardings=(
self.params_sharding,
None,
ms,
None,
ds,
None,
None,
None,
None,
None,
),
out_shardings=(None, ds, ms, None),
donate_argnums=(2,),
)
self.new_memory = pjit.pjit(
new_memory_.apply,
static_argnums=(1, 2),
out_shardings=ms,
)
def run(self):
"""Generator that accepts prompts."""
runner = self.runner
mesh = runner.mesh
max_len = runner.model.sequence_len
batch_size = runner.batch_size
params = self.params
rngs = jax.random.split(jax.random.PRNGKey(1), batch_size)
with mesh:
memory = self.new_memory(params, batch_size, max_len)
settings = SampleSettings(
temperature=np.zeros((batch_size,), dtype=np.float32),
nucleus_p=np.zeros((batch_size,), dtype=np.float32),
mask=np.ones((batch_size, self.vocab_size), dtype=np.int32),
active=np.zeros((batch_size), dtype=np.int32),
)
last_output = SampleOutput(
token_id=np.zeros((batch_size, 1), dtype=np.int32),
prob=np.zeros((batch_size, 1), dtype=jnp.bfloat16),
top_k_token_ids=np.zeros((batch_size, TOP_K), dtype=np.int32),
top_k_probs=np.zeros((batch_size, TOP_K), dtype=jnp.bfloat16),
)
prompt = np.array([300, 400, 500, 600, 600, 700, 800])
new_settings = SampleSettings(
temperature=np.float32(1),
nucleus_p=np.float32(1),
mask=np.ones((self.vocab_size,), dtype=np.int32),
active=np.zeros((), dtype=np.int32),
)
rng_seed = np.uint64(1)
for size in self.pad_sizes:
if size > runner.model.sequence_len:
break
logger.info("Precompile {}".format(size))
prompt_len = len(prompt)
prompt = pad_to_size(prompt, size)
rngs, last_output, memory, settings = self.prefill_memory(
params,
rngs,
memory,
settings,
last_output,
prompt,
prompt_len,
rng_seed,
new_settings,
0,
)
with runner.mesh:
logger.info("Compiling...")
rngs, last_output, memory = self.sample_step(
params, rngs, last_output, memory, settings
)
logger.info("Done compiling.")
all_tokens = []
free_slots = list(range(batch_size))
requests = [None] * batch_size
first_output = [None] * batch_size
jax.tree_map(lambda x: x.copy_to_host_async(), last_output)
prev_token = last_output
step = 0
total_num_tokens = 0
total_num_sequences = 0
with mesh:
while True:
while free_slots:
request: Optional[Request] = yield
tokens = self.tokenizer.encode(request.prompt)
temperature = request.temperature
nucleus_p = request.nucleus_p
rng_seed = request.rng_seed
i = free_slots.pop()
prompt = np.array(tokens, dtype=np.int32)
prompt_len = len(prompt)
prompt = pad_to_size(prompt, self.get_pad_bucket(prompt.shape[0]))
# All tokens are allowed.
mask = np.ones((self.vocab_size,), dtype=np.int32)
new_settings = SampleSettings(
temperature=np.float32(temperature),
nucleus_p=np.float32(nucleus_p),
mask=mask,
active=np.ones((), dtype=np.int32),
)
rng_seed = np.uint64(rng_seed)
rngs, last_output, memory, settings = self.prefill_memory(
params,
rngs,
memory,
settings,
last_output,
prompt,
prompt_len,
rng_seed,
new_settings,
i,
)
jax.tree_map(lambda x: x.copy_to_host_async(), last_output)
first_output[i] = last_output
requests[i] = request
total_num_sequences += 1
rngs, last_output, memory = self.sample_step(
params, rngs, last_output, memory, settings
)
total_num_tokens += batch_size - len(free_slots)
# prev_token should already be on the host.
prev_token = jax.tree_map(np.array, prev_token)
for i in range(batch_size):
if requests[i] is not None:
if first_output[i] is not None:
first_output_i = jax.tree_map(np.array, first_output[i])
all_tokens.append(int(first_output_i.token_id[i][0]))
first_output[i] = None
continue
all_tokens.append(int(prev_token.token_id[i][0]))
cont = len(all_tokens) < requests[i].max_len
if not cont:
output_str = self.tokenizer.decode(all_tokens)
requests[i] = None
free_slots.append(i)
all_tokens = []
settings = settings._replace(active=settings.active.at[i].set(0))
yield output_str
jax.tree_map(lambda x: x.copy_to_host_async(), last_output)
prev_token = last_output
step += 1
def make_mesh(
local_mesh_config: tuple[int, ...], between_hosts_config: tuple[int, ...]
) -> jax.sharding.Mesh:
assert len(local_mesh_config) == 2
assert len(between_hosts_config) == 2
rank_logger.info("Detected %s devices in mesh", jax.device_count())
device_mesh = mesh_utils.create_hybrid_device_mesh(
local_mesh_config,
between_hosts_config,
devices=jax.devices(),
process_is_granule=True,
)
rank_logger.debug(re.sub("\n+", "\n", f"Job device mesh is:\n{device_mesh}"))
return jax.sharding.Mesh(device_mesh, ("data", "model"))
def sample_from_model(server, prompt, max_len, temperature):
next(server)
inp = Request(
prompt=prompt,
temperature=temperature,
nucleus_p=1.0,
rng_seed=42,
max_len=max_len,
)
return server.send(inp)
gitextract_zbbrgjdc/ ├── .gitignore ├── CODE_OF_CONDUCT.md ├── LICENSE.txt ├── README.md ├── checkpoint.py ├── checkpoints/ │ └── README.md ├── model.py ├── pyproject.toml ├── requirements.txt ├── run.py ├── runners.py └── tokenizer.model
SYMBOL INDEX (96 symbols across 4 files)
FILE: checkpoint.py
function copy_to_shm (line 43) | def copy_to_shm(file: str):
function copy_from_shm (line 60) | def copy_from_shm(file: str):
function fast_unpickle (line 71) | def fast_unpickle(path: str) -> Any:
function fast_pickle (line 77) | def fast_pickle(obj: Any, path: str) -> None:
function load_tensors (line 83) | def load_tensors(shaped_arrays, directory, mesh_config, tensor_indices=N...
function path_tuple_to_string (line 110) | def path_tuple_to_string(path: tuple) -> str:
function get_load_path_str (line 122) | def get_load_path_str(
function replace_with_load_state (line 144) | def replace_with_load_state(
function restore (line 180) | def restore(
FILE: model.py
class QuantizedWeight8bit (line 38) | class QuantizedWeight8bit:
method shape (line 43) | def shape(self):
class TrainingState (line 54) | class TrainingState(NamedTuple):
function _match (line 60) | def _match(qs, ks):
function with_sharding_constraint (line 71) | def with_sharding_constraint(x, constraint):
function cast_bfloat16 (line 78) | def cast_bfloat16(x):
function ffn_size (line 85) | def ffn_size(emb_size, widening_factor):
function apply_rules (line 92) | def apply_rules(rules):
class KVMemory (line 178) | class KVMemory(NamedTuple):
function init_layer_memories (line 184) | def init_layer_memories(
class Memory (line 203) | class Memory(NamedTuple):
class Router (line 208) | class Router(hk.Module):
method __init__ (line 209) | def __init__(
method compute_routing_prob (line 225) | def compute_routing_prob(
method _compute_routing_prob (line 231) | def _compute_routing_prob(
method _router_weights (line 251) | def _router_weights(
class MoELayer (line 272) | class MoELayer(hk.Module):
method __init__ (line 273) | def __init__(
method _inference_call (line 294) | def _inference_call(self, inputs: jax.Array, padding_mask: Optional[ja...
method __call__ (line 399) | def __call__(self, inputs: jax.Array, padding_mask: jax.Array):
class MHAOutput (line 403) | class MHAOutput(NamedTuple):
class DecoderOutput (line 410) | class DecoderOutput(NamedTuple):
class TransformerOutput (line 415) | class TransformerOutput(NamedTuple):
class TransformerConfig (line 421) | class TransformerConfig:
method __post_init__ (line 445) | def __post_init__(self):
method partition_rules (line 451) | def partition_rules(self):
method make (line 454) | def make(self, mesh=None) -> "Transformer":
method get_memory_sharding (line 476) | def get_memory_sharding(self):
function hk_rms_norm (line 489) | def hk_rms_norm(
function make_attention_mask (line 499) | def make_attention_mask(
class Linear (line 525) | class Linear(hk.Linear):
method __init__ (line 526) | def __init__(
method __call__ (line 544) | def __call__(
class RMSNorm (line 587) | class RMSNorm(hk.RMSNorm):
method __init__ (line 589) | def __init__(
method __call__ (line 600) | def __call__(self, inputs: jax.Array):
function rotate_half (line 627) | def rotate_half(
class RotaryEmbedding (line 635) | class RotaryEmbedding(hk.Module):
method __init__ (line 644) | def __init__(
method __call__ (line 655) | def __call__(
class MultiHeadAttention (line 694) | class MultiHeadAttention(hk.Module):
method __init__ (line 695) | def __init__(
method __call__ (line 720) | def __call__(
method _linear_projection (line 894) | def _linear_projection(
class MHABlock (line 915) | class MHABlock(hk.Module):
method __call__ (line 927) | def __call__(
class DenseBlock (line 964) | class DenseBlock(hk.Module):
method __call__ (line 973) | def __call__(
class DecoderLayer (line 1011) | class DecoderLayer(hk.Module):
method __call__ (line 1030) | def __call__(
class LanguageModelOutput (line 1105) | class LanguageModelOutput(NamedTuple):
class InOutEmbed (line 1110) | class InOutEmbed(hk.Embed):
method __init__ (line 1113) | def __init__(
method embeddings (line 1128) | def embeddings(self):
method decode (line 1139) | def decode(
class LanguageModelConfig (line 1147) | class LanguageModelConfig:
method initialize (line 1167) | def initialize(self):
method make (line 1179) | def make(self, *args, **kwargs):
method partition_rules (line 1193) | def partition_rules(self):
function layer_norm (line 1197) | def layer_norm(x, model):
class LanguageModel (line 1202) | class LanguageModel(hk.Module):
method __call__ (line 1211) | def __call__(
method init_memory (line 1281) | def init_memory(self, batch_size: int, seq_len: int, dtype=jnp.bfloat16):
method prefill_memory (line 1284) | def prefill_memory(self, prompts, memory):
class Transformer (line 1292) | class Transformer(hk.Module):
method init_memory (line 1313) | def init_memory(self, batch_size: int, sequence_len: int, dtype=jnp.bf...
method __call__ (line 1326) | def __call__(
FILE: run.py
function main (line 24) | def main():
FILE: runners.py
class SampleSettings (line 50) | class SampleSettings(NamedTuple):
class SampleOutput (line 58) | class SampleOutput(NamedTuple):
function insert_slice (line 65) | def insert_slice(memory: Memory, slice, length, i):
function pad_to_size (line 77) | def pad_to_size(x, size):
function top_p_filter (line 84) | def top_p_filter(logits: jax.Array, top_p: jax.Array) -> jax.Array:
function sample_token (line 100) | def sample_token(
class ModelRunner (line 137) | class ModelRunner:
method make_forward_fn (line 150) | def make_forward_fn(self, mesh: Any):
method initialize (line 159) | def initialize(
method init (line 193) | def init(self, rng: jax.Array, data) -> TrainingState:
method get_state_sharding (line 199) | def get_state_sharding(self, init_data):
method load_or_init (line 212) | def load_or_init(
class Request (line 253) | class Request:
class InferenceRunner (line 262) | class InferenceRunner:
method get_pad_bucket (line 271) | def get_pad_bucket(self, size):
method initialize (line 275) | def initialize(self):
method run (line 442) | def run(self):
function make_mesh (line 580) | def make_mesh(
function sample_from_model (line 596) | def sample_from_model(server, prompt, max_len, temperature):
Condensed preview — 12 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (95K chars).
[
{
"path": ".gitignore",
"chars": 37,
"preview": "checkpoints/*\n!checkpoints/README.md\n"
},
{
"path": "CODE_OF_CONDUCT.md",
"chars": 28,
"preview": "Be excellent to each other.\n"
},
{
"path": "LICENSE.txt",
"chars": 11358,
"preview": "\n Apache License\n Version 2.0, January 2004\n "
},
{
"path": "README.md",
"chars": 2142,
"preview": "# Grok-1\n\nThis repository contains JAX example code for loading and running the Grok-1 open-weights model.\n\nMake sure to"
},
{
"path": "checkpoint.py",
"chars": 7392,
"preview": "# Copyright 2024 X.AI Corp.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this f"
},
{
"path": "checkpoints/README.md",
"chars": 99,
"preview": "# Checkpoint directory\n\nPlace Grok-1 checkpoints here so they can be loaded by the example script.\n"
},
{
"path": "model.py",
"chars": 45779,
"preview": "# Copyright 2024 X.AI Corp.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this f"
},
{
"path": "pyproject.toml",
"chars": 170,
"preview": "[tool.ruff]\nindent-width = 4\nline-length = 100\n\n[tool.ruff.lint]\nignore = [\n \"E722\",\n \"E731\",\n \"E741\",\n \"F40"
},
{
"path": "requirements.txt",
"chars": 146,
"preview": "dm_haiku==0.0.12\njax[cuda12-pip]==0.4.25 -f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html\nnumpy==1."
},
{
"path": "run.py",
"chars": 2264,
"preview": "# Copyright 2024 X.AI Corp.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this f"
},
{
"path": "runners.py",
"chars": 21845,
"preview": "# Copyright 2024 X.AI Corp.\n#\n# Licensed under the Apache License, Version 2.0 (the \"License\");\n# you may not use this f"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the xai-org/grok-1 GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 12 files (89.1 KB), approximately 21.4k tokens, and a symbol index with 96 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.