Minor fixes and clean-up

checkpoint.py

@@ -23,7 +23,6 @@ import re
 import shutil
 import tempfile
 from concurrent.futures import ThreadPoolExecutor, wait
-from functools import partial
 from typing import Any, Optional
 
 import jax
@@ -143,7 +142,6 @@ def replace_with_load_state(
     load_exclude_rules: Optional[list[str]] = None,
     mesh_config: tuple = (1, 1),
 ) -> Any:
-    # re_pattern, re_substitue
     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}
@@ -177,7 +175,6 @@ def restore(
     checkpoint_path: str,
     state_shapes: Any,
     mesh,
-    local_mesh_config,
     between_hosts_config,
     params_only,
     state_sharding,

model.py

@@ -28,7 +28,6 @@ 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__)
@@ -226,7 +225,6 @@ class Router(hk.Module):
     def compute_routing_prob(
         self, inputs: jax.Array, padding_mask: Optional[jax.Array], num_experts: int
     ):
-        """During inference, we sort the inputs depending on which expert they get routed to."""
         return self._compute_routing_prob(inputs, padding_mask, num_experts)
 
     @hk.transparent
@@ -1171,7 +1169,7 @@ class LanguageModelConfig:
         # 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."
+            "Overriding model initialize scale is supported only for predefined models."
         )
         if self.model_size == 0:
             self.model_size = model_config.emb_size
@@ -1226,8 +1224,6 @@ class LanguageModel(hk.Module):
         config = self.config
 
         input_mask = jnp.greater(tokens, config.pad_token)
-        # Shift right by 1:
-        pad_width = ((0, 0), (1, 0))
 
         # Embed the input tokens and positions.
         in_out_embed = InOutEmbed(

pyproject.toml

@@ -8,7 +8,6 @@ ignore = [
     "E731",
     "E741",
     "F405",
-    # Re-enable these later on:
     "E402",
     "F403",
 ]

requirements.txt

@@ -1,4 +1,5 @@
 dm_haiku==0.0.12
-jax==0.4.25
+-f https://storage.googleapis.com/jax-releases/jax_cuda_releases.html
+jax[cuda12_pip]==0.4.25
 numpy==1.26.4
 sentencepiece==0.2.0

run.py

@@ -14,12 +14,14 @@
 
 import logging
 
-from model import LanguageModelConfig, TransformerConfig
-from runners import InferenceRunner, ModelRunner
-
+from model import LanguageModelConfig, TransformerConfig, QuantizedWeight8bit as QW8Bit
+from runners import InferenceRunner, ModelRunner, sample_from_model
 
 CKPT_PATH = "./checkpoints/"
 
+# This is required in order for unpickling to work.
+QuantizedWeight8bit = QW8Bit
+
 
 def main():
     grok_1_model = LanguageModelConfig(

runners.py

@@ -44,7 +44,6 @@ from model import (
 logger = logging.getLogger(__name__)
 rank_logger = logging.getLogger("rank")
 
-
 TOP_K = 8
 
 
@@ -71,7 +70,8 @@ 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), memory, slice)
+    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):
@@ -239,7 +239,6 @@ class ModelRunner:
                 checkpoint_path=self.checkpoint_path,
                 state_shapes=state_shapes,
                 mesh=self.mesh,
-                local_mesh_config=self.local_mesh_config,
                 between_hosts_config=self.between_hosts_config,
                 state_sharding=self.state_sharding,
                 init_state=init_state,