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Upgrade mlx-lm to 0.30.2 with transformers 5.x compatibility (#1125)
e5e74e1eef6849e71aa471d1e1a258becb9fa012 · 2026-01-13 12:06:04 +0000 · Alex Cheema
## Motivation
Upgrade mlx-lm to version 0.30.2 which requires transformers 5.0.0rc2 as
a prerelease dependency. This enables support for newer models like Kimi
K2 Thinking while maintaining compatibility with existing models.
The transformers 5.x release includes breaking changes that affect
custom tokenizers like Kimi's TikTokenTokenizer, requiring compatibility
fixes.
## Changes
### Core Changes
- **mlx-lm upgrade**: Bump to 0.30.2 with locked exact versions for
mlx/mlx-lm to prevent breaking changes
- **transformers 5.x compatibility**: Enable prerelease transformers
dependency
### Kimi K2 Tokenizer Fixes
- Add `bytes_to_unicode` monkey-patch to restore function moved in
transformers 5.0.0rc2
- Load `TikTokenTokenizer` directly instead of via `AutoTokenizer` to
bypass transformers 5.x bug with `auto_map` fallback
- Patch `encode()` to use tiktoken directly with `allowed_special="all"`
to handle special tokens from chat templates
### Other Changes
- Dashboard: Show disk usage for completed model downloads
- CI: Add `workflow_dispatch` trigger to build-app workflow
- Docs: Add basic API documentation
### Testing
- Add comprehensive tokenizer unit tests for all supported models
- Tests verify encode/decode, special token handling, and chat template
encoding
## Why It Works
**bytes_to_unicode issue**: transformers 5.0.0rc2 moved
`bytes_to_unicode` from `transformers.models.gpt2.tokenization_gpt2` to
`transformers.convert_slow_tokenizer`. Kimi's `tokenization_kimi.py`
imports from the old location. The monkey-patch restores it at module
load time.
**AutoTokenizer issue**: transformers 5.x has a bug where
`tokenizer_class_from_name('TikTokenTokenizer')` returns `None` for
custom tokenizers with `auto_map`. Loading the tokenizer directly
bypasses this.
**encode() issue**: transformers 5.x's `pad()` method fails for slow
tokenizers. Using tiktoken's encode directly with
`allowed_special="all"` avoids this path and properly handles special
tokens like `<|im_user|>` from chat templates.
## Test Plan
### Manual Testing
- Hardware: 2x Mac Studios connected via Thunderbolt 5 (mike22 and
james21)
- Tested Kimi K2 Thinking, GPT-OSS-120B, GPT-OSS-20B, LLama-3.1-8B-bf16, qwen3-30B-A3B-8bit model with pipeline parallelism across both
nodes
- Verified warmup inference completes successfully
- Verified chat completions work with special tokens
### Automated Testing
- Added `test_tokenizers.py` with 31 tests covering:
- Basic encode/decode for all model families (deepseek, kimi, llama,
qwen, gpt-oss, glm)
- Special token encoding (critical for chat templates)
- Chat template application and encoding
- Kimi-specific and GLM-specific edge cases
- All tests pass: `uv run pytest
src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py`
### Failing Tests
RDMA with all models.
---------
Co-authored-by: Evan <evanev7@gmail.com>
Files touched
A .mlx_typings/mlx_lm/models/deepseek_v3.pyiM .mlx_typings/mlx_lm/models/switch_layers.pyiM .mlx_typings/mlx_lm/tokenizer_utils.pyiA MISSED_THINGS.mdM pyproject.tomlM src/exo/worker/engines/mlx/auto_parallel.pyM src/exo/worker/engines/mlx/utils_mlx.pyA src/exo/worker/tests/unittests/test_mlx/test_tokenizers.pyM tests/headless_runner.pyM tests/start_distributed_test.shM uv.lock
Diff
commit e5e74e1eef6849e71aa471d1e1a258becb9fa012
Author: Alex Cheema <41707476+AlexCheema@users.noreply.github.com>
Date: Tue Jan 13 12:06:04 2026 +0000
Upgrade mlx-lm to 0.30.2 with transformers 5.x compatibility (#1125)
## Motivation
Upgrade mlx-lm to version 0.30.2 which requires transformers 5.0.0rc2 as
a prerelease dependency. This enables support for newer models like Kimi
K2 Thinking while maintaining compatibility with existing models.
The transformers 5.x release includes breaking changes that affect
custom tokenizers like Kimi's TikTokenTokenizer, requiring compatibility
fixes.
## Changes
### Core Changes
- **mlx-lm upgrade**: Bump to 0.30.2 with locked exact versions for
mlx/mlx-lm to prevent breaking changes
- **transformers 5.x compatibility**: Enable prerelease transformers
dependency
### Kimi K2 Tokenizer Fixes
- Add `bytes_to_unicode` monkey-patch to restore function moved in
transformers 5.0.0rc2
- Load `TikTokenTokenizer` directly instead of via `AutoTokenizer` to
bypass transformers 5.x bug with `auto_map` fallback
- Patch `encode()` to use tiktoken directly with `allowed_special="all"`
to handle special tokens from chat templates
### Other Changes
- Dashboard: Show disk usage for completed model downloads
- CI: Add `workflow_dispatch` trigger to build-app workflow
- Docs: Add basic API documentation
### Testing
- Add comprehensive tokenizer unit tests for all supported models
- Tests verify encode/decode, special token handling, and chat template
encoding
## Why It Works
**bytes_to_unicode issue**: transformers 5.0.0rc2 moved
`bytes_to_unicode` from `transformers.models.gpt2.tokenization_gpt2` to
`transformers.convert_slow_tokenizer`. Kimi's `tokenization_kimi.py`
imports from the old location. The monkey-patch restores it at module
load time.
**AutoTokenizer issue**: transformers 5.x has a bug where
`tokenizer_class_from_name('TikTokenTokenizer')` returns `None` for
custom tokenizers with `auto_map`. Loading the tokenizer directly
bypasses this.
**encode() issue**: transformers 5.x's `pad()` method fails for slow
tokenizers. Using tiktoken's encode directly with
`allowed_special="all"` avoids this path and properly handles special
tokens like `<|im_user|>` from chat templates.
## Test Plan
### Manual Testing
- Hardware: 2x Mac Studios connected via Thunderbolt 5 (mike22 and
james21)
- Tested Kimi K2 Thinking, GPT-OSS-120B, GPT-OSS-20B, LLama-3.1-8B-bf16, qwen3-30B-A3B-8bit model with pipeline parallelism across both
nodes
- Verified warmup inference completes successfully
- Verified chat completions work with special tokens
### Automated Testing
- Added `test_tokenizers.py` with 31 tests covering:
- Basic encode/decode for all model families (deepseek, kimi, llama,
qwen, gpt-oss, glm)
- Special token encoding (critical for chat templates)
- Chat template application and encoding
- Kimi-specific and GLM-specific edge cases
- All tests pass: `uv run pytest
src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py`
### Failing Tests
RDMA with all models.
---------
Co-authored-by: Evan <evanev7@gmail.com>
---
.mlx_typings/mlx_lm/models/deepseek_v3.pyi | 156 ++
.mlx_typings/mlx_lm/models/switch_layers.pyi | 5 +
.mlx_typings/mlx_lm/tokenizer_utils.pyi | 85 +-
MISSED_THINGS.md | 41 +
pyproject.toml | 8 +-
src/exo/worker/engines/mlx/auto_parallel.py | 3 +-
src/exo/worker/engines/mlx/utils_mlx.py | 97 +-
.../tests/unittests/test_mlx/test_tokenizers.py | 386 +++++
tests/headless_runner.py | 50 +-
tests/start_distributed_test.sh | 32 +-
uv.lock | 1612 +++++++++++---------
11 files changed, 1642 insertions(+), 833 deletions(-)
diff --git a/.mlx_typings/mlx_lm/models/deepseek_v3.pyi b/.mlx_typings/mlx_lm/models/deepseek_v3.pyi
new file mode 100644
index 00000000..8e640ac9
--- /dev/null
+++ b/.mlx_typings/mlx_lm/models/deepseek_v3.pyi
@@ -0,0 +1,156 @@
+"""Type stubs for mlx_lm.models.deepseek_v3"""
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs
+from .switch_layers import SwitchGLU
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+ model_type: str
+ vocab_size: int
+ hidden_size: int
+ intermediate_size: int
+ moe_intermediate_size: int
+ num_hidden_layers: int
+ num_attention_heads: int
+ num_key_value_heads: int
+ n_shared_experts: Optional[int]
+ n_routed_experts: Optional[int]
+ routed_scaling_factor: float
+ kv_lora_rank: int
+ q_lora_rank: Optional[int]
+ qk_rope_head_dim: int
+ v_head_dim: int
+ qk_nope_head_dim: int
+ topk_method: str
+ scoring_func: str
+ norm_topk_prob: bool
+ n_group: int
+ topk_group: int
+ num_experts_per_tok: int
+ moe_layer_freq: int
+ first_k_dense_replace: int
+ max_position_embeddings: int
+ rms_norm_eps: float
+ rope_theta: float
+ rope_scaling: Optional[Dict[str, Any]]
+ attention_bias: bool
+
+class DeepseekV3Attention(nn.Module):
+ config: ModelArgs
+ hidden_size: int
+ num_heads: int
+ max_position_embeddings: int
+ rope_theta: float
+ q_lora_rank: Optional[int]
+ qk_rope_head_dim: int
+ kv_lora_rank: int
+ v_head_dim: int
+ qk_nope_head_dim: int
+ q_head_dim: int
+ scale: float
+ q_proj: nn.Linear
+ q_a_proj: nn.Linear
+ q_a_layernorm: nn.RMSNorm
+ q_b_proj: nn.Linear
+ kv_a_proj_with_mqa: nn.Linear
+ kv_a_layernorm: nn.RMSNorm
+ kv_b_proj: nn.Linear
+ o_proj: nn.Linear
+ rope: Any
+
+ def __init__(self, config: ModelArgs) -> None: ...
+ def __call__(
+ self,
+ x: mx.array,
+ mask: Optional[mx.array] = None,
+ cache: Optional[Any] = None,
+ ) -> mx.array: ...
+
+class DeepseekV3MLP(nn.Module):
+ config: ModelArgs
+ hidden_size: int
+ intermediate_size: int
+ gate_proj: nn.Linear
+ up_proj: nn.Linear
+ down_proj: nn.Linear
+
+ def __init__(
+ self,
+ config: ModelArgs,
+ hidden_size: Optional[int] = None,
+ intermediate_size: Optional[int] = None,
+ ) -> None: ...
+ def __call__(self, x: mx.array) -> mx.array: ...
+
+class MoEGate(nn.Module):
+ config: ModelArgs
+ top_k: int
+ norm_topk_prob: bool
+ n_routed_experts: Optional[int]
+ routed_scaling_factor: float
+ n_group: int
+ topk_group: int
+ weight: mx.array
+ e_score_correction_bias: mx.array
+
+ def __init__(self, config: ModelArgs) -> None: ...
+ def __call__(self, x: mx.array) -> tuple[mx.array, mx.array]: ...
+
+class DeepseekV3MoE(nn.Module):
+ config: ModelArgs
+ num_experts_per_tok: int
+ switch_mlp: SwitchGLU
+ gate: MoEGate
+ shared_experts: DeepseekV3MLP
+ sharding_group: Optional[mx.distributed.Group]
+
+ def __init__(self, config: ModelArgs) -> None: ...
+ def __call__(self, x: mx.array) -> mx.array: ...
+
+class DeepseekV3DecoderLayer(nn.Module):
+ self_attn: DeepseekV3Attention
+ mlp: DeepseekV3MLP | DeepseekV3MoE
+ input_layernorm: nn.RMSNorm
+ post_attention_layernorm: nn.RMSNorm
+
+ def __init__(self, config: ModelArgs, layer_idx: int) -> None: ...
+ def __call__(
+ self,
+ x: mx.array,
+ mask: Optional[mx.array] = None,
+ cache: Optional[Any] = None,
+ ) -> mx.array: ...
+
+class DeepseekV3Model(nn.Module):
+ vocab_size: int
+ embed_tokens: nn.Embedding
+ layers: list[DeepseekV3DecoderLayer]
+ norm: nn.RMSNorm
+
+ def __init__(self, config: ModelArgs) -> None: ...
+ def __call__(
+ self,
+ x: mx.array,
+ cache: Optional[Any] = None,
+ ) -> mx.array: ...
+
+class Model(nn.Module):
+ model_type: str
+ model: DeepseekV3Model
+ lm_head: nn.Linear
+
+ def __init__(self, config: ModelArgs) -> None: ...
+ def __call__(
+ self,
+ inputs: mx.array,
+ cache: Optional[Any] = None,
+ ) -> mx.array: ...
+ def sanitize(self, weights: dict[str, Any]) -> dict[str, Any]: ...
+ @property
+ def layers(self) -> list[DeepseekV3DecoderLayer]: ...
diff --git a/.mlx_typings/mlx_lm/models/switch_layers.pyi b/.mlx_typings/mlx_lm/models/switch_layers.pyi
index c50c999a..d617c5b0 100644
--- a/.mlx_typings/mlx_lm/models/switch_layers.pyi
+++ b/.mlx_typings/mlx_lm/models/switch_layers.pyi
@@ -57,6 +57,11 @@ class SwiGLU(nn.Module):
def __call__(self, x, gate): ...
class SwitchGLU(nn.Module):
+ gate_proj: SwitchLinear
+ up_proj: SwitchLinear
+ down_proj: SwitchLinear
+ activation: SwiGLU
+
def __init__(
self,
input_dims: int,
diff --git a/.mlx_typings/mlx_lm/tokenizer_utils.pyi b/.mlx_typings/mlx_lm/tokenizer_utils.pyi
index a0a8355f..251e3d28 100644
--- a/.mlx_typings/mlx_lm/tokenizer_utils.pyi
+++ b/.mlx_typings/mlx_lm/tokenizer_utils.pyi
@@ -4,6 +4,7 @@ This type stub file was generated by pyright.
from functools import partial
from pathlib import Path
+from typing import Any
from transformers import PreTrainedTokenizerFast
@@ -103,37 +104,55 @@ class TokenizerWrapper:
Accessing any attribute other than the ``detokenizer`` is forwarded to the
huggingface tokenizer.
"""
- def __init__(self, tokenizer, detokenizer_class=..., eos_token_ids=...) -> None: ...
- def add_eos_token(self, token: str): # -> None:
- ...
+
+ _tokenizer: PreTrainedTokenizerFast
+ eos_token_id: int | None
+ eos_token: str | None
+ bos_token_id: int | None
+ bos_token: str | None
+ vocab_size: int
+ all_special_tokens: list[str]
+
+ def __init__(
+ self,
+ tokenizer: Any,
+ detokenizer_class: Any = ...,
+ eos_token_ids: list[int] | None = ...,
+ chat_template: Any = ...,
+ tool_parser: Any = ...,
+ tool_call_start: str | None = ...,
+ tool_call_end: str | None = ...,
+ ) -> None: ...
+ def encode(self, text: str, **kwargs: Any) -> list[int]: ...
+ def decode(self, token_ids: list[int], **kwargs: Any) -> str: ...
+ def apply_chat_template(
+ self,
+ messages: list[dict[str, Any]],
+ tokenize: bool = False,
+ add_generation_prompt: bool = False,
+ tools: Any = None,
+ **kwargs: Any,
+ ) -> str: ...
+ def get_vocab(self) -> dict[str, int]: ...
+ def add_eos_token(self, token: str) -> None: ...
@property
- def has_thinking(self): # -> bool:
- ...
+ def has_thinking(self) -> bool: ...
@property
- def think_start(self): # -> str | None:
- ...
+ def think_start(self) -> str | None: ...
@property
- def think_end(self): # -> str | None:
- ...
+ def think_end(self) -> str | None: ...
@property
- def has_tool_calling(self): # -> bool:
- ...
+ def has_tool_calling(self) -> bool: ...
@property
- def tool_call_start(self): # -> str | None:
- ...
+ def tool_call_start(self) -> str | None: ...
@property
- def tool_call_end(self): # -> str | None:
- ...
+ def tool_call_end(self) -> str | None: ...
@property
- def detokenizer(self): # -> NaiveStreamingDetokenizer:
- """
- Get a stateful streaming detokenizer.
- """
+ def detokenizer(self) -> NaiveStreamingDetokenizer:
+ """Get a stateful streaming detokenizer."""
- def __getattr__(self, attr): # -> set[Any] | Any:
- ...
- def __setattr__(self, attr, value): # -> None:
- ...
+ def __getattr__(self, attr: str) -> Any: ...
+ def __setattr__(self, attr: str, value: Any) -> None: ...
class NewlineTokenizer(PreTrainedTokenizerFast):
"""A tokenizer that replaces newlines with <n> and <n> with new line."""
@@ -146,18 +165,11 @@ class NewlineTokenizer(PreTrainedTokenizerFast):
def batch_decode(self, *args, **kwargs): # -> list[str]:
...
-def load_tokenizer(
+def load(
model_path: Path,
- tokenizer_config_extra=...,
- return_tokenizer=...,
- eos_token_ids=...,
-) -> (
- TokenizerWrapper
- | type[SPMStreamingDetokenizer]
- | partial[SPMStreamingDetokenizer]
- | type[BPEStreamingDetokenizer]
- | type[NaiveStreamingDetokenizer]
-):
+ tokenizer_config_extra: dict[str, Any] | None = None,
+ eos_token_ids: list[int] | int | None = None,
+) -> TokenizerWrapper:
"""Load a huggingface tokenizer and try to infer the type of streaming
detokenizer to use.
@@ -165,4 +177,7 @@ def load_tokenizer(
a Hugging Face repo ID.
"""
-def no_bos_or_eos(sequence: list, bos: int, eos: int) -> list: ...
+# Alias for backward compatibility
+load_tokenizer = load
+
+def no_bos_or_eos(sequence: list[int], bos: int, eos: int) -> list[int]: ...
diff --git a/MISSED_THINGS.md b/MISSED_THINGS.md
new file mode 100644
index 00000000..8cb11130
--- /dev/null
+++ b/MISSED_THINGS.md
@@ -0,0 +1,41 @@
+# Missed things
+[X] Log EXO_LIBP2P_NAMESPACE on start in exo/main.py
+[X] Ordering of warmup was changed, which is wrong. It was changed to rank < n-1, then rank=n-1. It should be rank!=0 then rank=0 (this matches the auto_parallel implementation. NOTE: we use a different convention to mlx-lm, our terminal rank is rank=n-1 whereas mlx-lm is rank=0 hence i can see why this was changed wrongly).
+[X] Downloads keying by model_id not shard_metadata (worker/plan.py, worker/main.py).
+[X] Fetching download status of all models on start
+[X] Deduplication of tasks in plan_step.
+[X] resolve_allow_patterns should just be wildcard now.
+[] no mx_barrier in genreate.py mlx_generate at the end.
+[] cache assertion not needed in auto_parallel.py PipelineLastLayer.
+[] GPTOSS support dropped in auto_parallel.py.
+[] sharding changed "all-to-sharded" became _all_to_sharded in auto_parallel.py.
+[] same as above with "sharded-to-all" became _sharded_to_all in auto_parallel.py.
+[] Dropped support for Ministral3Model, DeepseekV32Model, Glm4MoeModel, Qwen3NextModel, GptOssMode in auto_parallel.py.
+[] Dropped prefill/decode code in auto_parallel.py and utils_mlx.py.
+[X] KV_CACHE_BITS should be None to disable quantized KV cache.
+[] Dropped _set_nofile_limit in utils_mlx.py.
+[] We have group optional in load_mlx_items in utils_mlx.py.
+[] Dropped add_missing_chat_templates for GptOss in load_mlx_items in utils_mlx.py.
+[] Dropped model.make_cache in make_kv_cache in utils_mlx.py.
+[X] We put cache limit back in utils_mlx.py.
+[] topology.py remove_node removes the connections after checking if node is is in self._node_id_to_rx_id_map. on beta_1 it checks after, so would remove stale connections I guess?
+[] Missing Glm 4.7 model cards (this isn't ready yet but should be picked up, probably create an issue... the blocker is transforemrs version doesn't support the tokenizer for Glm 4.7. rc-1 does but we can't upgrade as it breaks other things.)
+[] try-except in _command_processor only excepts ValueError. This was silently failing leading to un-debuggable errors (we had a KeyError that was happening ). Changed this to catch Exception instead of ValueError. See exo-v2 89ae38405e0052e3c22405daf094b065878aa873 and fb99fea69b5a39017efc90c5dad0072e677455f0.
+[X] In placement.py, place_instance no longer looks at model_meta.supports_tensor and check if this tensor parallel number of nodes is supported by the model's tensor dimensions.
+[X] In placement.py, place_instanec, we no longer have the special case to exclude DeepSeek v3.1 pipeline parallel (it doesn't work).
+[] logger.warning("You have likely selected ibv for a single node instance; falling back to MlxRing") was changed to debug. That will spam this warning since it happens every time we query instance previews.
+[X] In placement_utils.py, get_mlx_jaccl_coordinators, We no longer prioritise Jaccl Coordinator IP. Now it picks the first one, which is unstable (Jaccl coordinator over TB5 is unstable).
+
+
+
+[X] Downloads keying by model_id not shard_metadata (worker/plan.py, worker/main.py).
+[X] Fetching download status of all models on start
+[X] Deduplication of tasks in plan_step.
+[X] resolve_allow_patterns should just be wildcard now.
+[X] KV_CACHE_BITS should be None to disable quantized KV cache.
+[X] We put cache limit back in utils_mlx.py.
+[X] In placement.py, place_instance no longer looks at model_meta.supports_tensor and check if this tensor parallel number of nodes is supported by the model's tensor dimensions.
+[X] In placement.py, place_instanec, we no longer have the special case to exclude DeepSeek v3.1 pipeline parallel (it doesn't work).
+[X] In placement_utils.py, get_mlx_jaccl_coordinators, We no longer prioritise Jaccl Coordinator IP. Now it picks the first one, which is unstable (Jaccl coordinator over TB5 is unstable).
+
+
diff --git a/pyproject.toml b/pyproject.toml
index 188899c1..99cccfeb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,9 +17,9 @@ dependencies = [
"loguru>=0.7.3",
"exo_pyo3_bindings", # rust bindings
"anyio==4.11.0",
- "mlx>=0.30.1; sys_platform == 'darwin'",
- "mlx[cpu]>=0.30.1; sys_platform == 'linux'",
- "mlx-lm>=0.28.3",
+ "mlx==0.30.1; sys_platform == 'darwin'",
+ "mlx[cpu]==0.30.1; sys_platform == 'linux'",
+ "mlx-lm @ git+https://github.com/AlexCheema/mlx-lm.git@fix-transformers-5.0.0rc2",
"tiktoken>=0.12.0", # required for kimi k2 tokenizer
"hypercorn>=0.18.0",
"openai-harmony>=0.0.8",
@@ -33,6 +33,7 @@ exo = "exo.main:main"
# dependencies only required for development
[dependency-groups]
dev = [
+ "basedpyright>=1.29.0",
"pyinstaller>=6.17.0",
"pytest>=8.4.0",
"pytest-asyncio>=1.0.0",
@@ -98,6 +99,7 @@ root = "src"
# supported platforms for this project
[tool.uv]
+prerelease = "allow"
environments = [
"sys_platform == 'darwin'",
"sys_platform == 'linux'",
diff --git a/src/exo/worker/engines/mlx/auto_parallel.py b/src/exo/worker/engines/mlx/auto_parallel.py
index 1a6542f1..b04c8add 100644
--- a/src/exo/worker/engines/mlx/auto_parallel.py
+++ b/src/exo/worker/engines/mlx/auto_parallel.py
@@ -289,8 +289,7 @@ class DeepSeekShardingStrategy(TensorParallelShardingStrategy):
model = cast(DeepseekV3Model, model)
for layer in model.layers:
# Shard the self attention
- if layer.self_attn.q_lora_rank is None: # pyright: ignore[reportUnnecessaryComparison]
- # Unfortunately, q_lora_rank can be None despite typing hints.
+ if layer.self_attn.q_lora_rank is None:
layer.self_attn.q_proj = self.all_to_sharded_linear(
layer.self_attn.q_proj
)
diff --git a/src/exo/worker/engines/mlx/utils_mlx.py b/src/exo/worker/engines/mlx/utils_mlx.py
index bbd0b38f..4a4e45cf 100644
--- a/src/exo/worker/engines/mlx/utils_mlx.py
+++ b/src/exo/worker/engines/mlx/utils_mlx.py
@@ -1,10 +1,23 @@
import json
import os
import resource
+import sys
import time
from pathlib import Path
from typing import Any, cast
+# Monkey-patch for transformers 5.x compatibility
+# Kimi's tokenization_kimi.py imports bytes_to_unicode from the old location
+# which was moved in transformers 5.0.0rc2
+try:
+ import transformers.models.gpt2.tokenization_gpt2 as gpt2_tokenization
+ from transformers.convert_slow_tokenizer import bytes_to_unicode
+
+ if not hasattr(gpt2_tokenization, "bytes_to_unicode"):
+ gpt2_tokenization.bytes_to_unicode = bytes_to_unicode # type: ignore[attr-defined]
+except ImportError:
+ pass # transformers < 5.0 or bytes_to_unicode not available
+
from mlx_lm.models.cache import KVCache, QuantizedKVCache, RotatingKVCache
from mlx_lm.models.deepseek_v3 import DeepseekV3Model
from mlx_lm.tokenizer_utils import TokenizerWrapper
@@ -18,7 +31,7 @@ from exo.worker.engines.mlx.constants import (
try:
from mlx_lm.tokenizer_utils import load_tokenizer
except ImportError:
- from mlx_lm.tokenizer_utils import load as load_tokenizer # type: ignore
+ from mlx_lm.tokenizer_utils import load as load_tokenizer
import contextlib
import mlx.core as mx
@@ -252,26 +265,70 @@ def shard_and_load(
return model, tokenizer
-def get_tokenizer(model_path: Path, shard_metadata: ShardMetadata):
- # TODO: Let's move away from this custom logic to mlx_lm.load()
- if "kimi-k2" in shard_metadata.model_meta.model_id.lower():
- eos_token_ids = [163586]
+def get_tokenizer(model_path: Path, shard_metadata: ShardMetadata) -> TokenizerWrapper:
+ """Load tokenizer for a model shard. Delegates to load_tokenizer_for_model_id."""
+ return load_tokenizer_for_model_id(shard_metadata.model_meta.model_id, model_path)
- elif "glm" in shard_metadata.model_meta.model_id.lower():
- eos_token_ids = [151336, 151329, 151338]
- else:
- eos_token_ids = None
-
- tokenizer = cast(
- TokenizerWrapper,
- load_tokenizer(
- model_path,
- tokenizer_config_extra={"trust_remote_code": TRUST_REMOTE_CODE},
- eos_token_ids=eos_token_ids,
- ),
+def get_eos_token_ids_for_model(model_id: str) -> list[int] | None:
+ """
+ Get the EOS token IDs for a model based on its ID.
+
+ Some models require explicit EOS token configuration that isn't in their
+ tokenizer config. This function returns the known EOS token IDs for such models.
+
+ Args:
+ model_id: The HuggingFace model ID
+
+ Returns:
+ List of EOS token IDs, or None if the model uses standard tokenizer config
+ """
+ model_id_lower = model_id.lower()
+ if "kimi-k2" in model_id_lower:
+ return [163586]
+ elif "glm" in model_id_lower:
+ return [151336, 151329, 151338]
+ return None
+
+
+def load_tokenizer_for_model_id(model_id: str, model_path: Path) -> TokenizerWrapper:
+ """
+ Load tokenizer for a model given its ID and local path.
+
+ This is the core tokenizer loading logic, handling special cases for different
+ model families (Kimi, GLM, etc.) and transformers 5.x compatibility.
+
+ Args:
+ model_id: The HuggingFace model ID (e.g., "moonshotai/Kimi-K2-Instruct")
+ model_path: Local path where the model/tokenizer files are stored
+
+ Returns:
+ TokenizerWrapper instance configured for the model
+ """
+ model_id_lower = model_id.lower()
+ eos_token_ids = get_eos_token_ids_for_model(model_id)
+
+ # Kimi uses a custom TikTokenTokenizer that transformers 5.x can't load via AutoTokenizer
+ if "kimi-k2" in model_id_lower:
+ sys.path.insert(0, str(model_path))
+ from tokenization_kimi import TikTokenTokenizer # type: ignore[import-not-found] # noqa: I001
+
+ hf_tokenizer: Any = TikTokenTokenizer.from_pretrained(model_path) # pyright: ignore[reportUnknownVariableType,reportUnknownMemberType]
+
+ # Patch encode to use internal tiktoken model directly
+ # transformers 5.x has a bug in the encode->pad path for slow tokenizers
+ def _patched_encode(text: str, **_kwargs: object) -> list[int]:
+ # Pass allowed_special="all" to handle special tokens like <|im_user|>
+ return list(hf_tokenizer.model.encode(text, allowed_special="all")) # pyright: ignore[reportUnknownMemberType,reportUnknownArgumentType]
+
+ hf_tokenizer.encode = _patched_encode
+ return TokenizerWrapper(hf_tokenizer, eos_token_ids=eos_token_ids)
+
+ tokenizer = load_tokenizer(
+ model_path,
+ tokenizer_config_extra={"trust_remote_code": TRUST_REMOTE_CODE},
+ eos_token_ids=eos_token_ids,
)
- assert isinstance(tokenizer, TokenizerWrapper)
return tokenizer
@@ -301,14 +358,14 @@ def apply_chat_template(
{k: v for k, v in message.model_dump().items() if v is not None} # type: ignore
)
- prompt: str = tokenizer.apply_chat_template( # type: ignore
+ prompt: str = tokenizer.apply_chat_template(
formatted_messages,
tokenize=False,
add_generation_prompt=True,
tools=chat_task_data.tools,
)
- return prompt # type: ignore
+ return prompt
class NullKVCache(KVCache):
diff --git a/src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py b/src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py
new file mode 100644
index 00000000..5eef4ede
--- /dev/null
+++ b/src/exo/worker/tests/unittests/test_mlx/test_tokenizers.py
@@ -0,0 +1,386 @@
+"""
+Unit tests for tokenizer loading and functionality across all supported models.
+
+This test downloads only tokenizer-related files (not full model weights) to verify
+that tokenizers can be loaded and used correctly for encoding/decoding.
+"""
+
+import asyncio
+import contextlib
+from pathlib import Path
+
+import pytest
+
+from exo.shared.models.model_cards import MODEL_CARDS, ModelCard
+from exo.worker.download.download_utils import (
+ download_file_with_retry,
+ ensure_models_dir,
+ fetch_file_list_with_cache,
+)
+from exo.worker.engines.mlx.utils_mlx import (
+ get_eos_token_ids_for_model,
+ load_tokenizer_for_model_id,
+)
+
+# Files needed for tokenizer functionality
+TOKENIZER_FILE_PATTERNS = [
+ "tokenizer.json",
+ "tokenizer_config.json",
+ "special_tokens_map.json",
+ "vocab.json",
+ "vocab.txt",
+ "merges.txt",
+ "tiktoken.model",
+ "added_tokens.json",
+ "tokenizer.model",
+ "tokenization_*.py", # Custom tokenizer implementations
+]
+
+
+def is_tokenizer_file(filename: str) -> bool:
+ """Check if a file is needed for tokenizer functionality."""
+ for pattern in TOKENIZER_FILE_PATTERNS:
+ if "*" in pattern:
+ prefix = pattern.split("*")[0]
+ suffix = pattern.split("*")[1]
+ if filename.startswith(prefix) and filename.endswith(suffix):
+ return True
+ elif filename == pattern:
+ return True
+ return False
+
+
+async def download_tokenizer_files(model_id: str) -> Path:
+ """Download only the tokenizer-related files for a model."""
+ target_dir = await ensure_models_dir() / model_id.replace("/", "--")
+ target_dir.mkdir(parents=True, exist_ok=True)
+
+ file_list = await fetch_file_list_with_cache(model_id, "main", recursive=True)
+
+ tokenizer_files = [f for f in file_list if is_tokenizer_file(f.path)]
+
+ if not tokenizer_files:
+ pytest.skip(f"No tokenizer files found for {model_id}")
+
+ for file_entry in tokenizer_files:
+ with contextlib.suppress(FileNotFoundError):
+ await download_file_with_retry(
+ model_id, "main", file_entry.path, target_dir
+ )
+
+ return target_dir
+
+
+# Get a sample of models to test (one per family to keep tests fast)
+def get_test_models() -> list[tuple[str, ModelCard]]:
+ """Get a representative sample of models to test."""
+ # Pick one model from each family to test
+ families: dict[str, tuple[str, ModelCard]] = {}
+ for short_id, card in MODEL_CARDS.items():
+ # Extract family name (e.g., "llama-3.1" from "llama-3.1-8b")
+ parts = short_id.split("-")
+ family = "-".join(parts[:2]) if len(parts) >= 2 else parts[0]
+
+ if family not in families:
+ families[family] = (short_id, card)
+
+ return list(families.values())
+
+
+TEST_MODELS: list[tuple[str, ModelCard]] = get_test_models()
+
+
+@pytest.fixture(scope="module")
+def event_loop():
+ """Create event loop for async tests."""
+ loop = asyncio.new_event_loop()
+ yield loop
+ loop.close()
+
+
+@pytest.mark.parametrize(
+ "short_id,model_card",
+ TEST_MODELS,
+ ids=[m[0] for m in TEST_MODELS],
+)
+@pytest.mark.asyncio
+async def test_tokenizer_encode_decode(short_id: str, model_card: ModelCard) -> None:
+ """Test that tokenizer can encode and decode text correctly."""
+ model_id = str(model_card.model_id)
+
+ # Download tokenizer files
+ model_path = await download_tokenizer_files(model_id)
+
+ # Verify required files exist
+ has_tokenizer = (
+ (model_path / "tokenizer.json").exists()
+ or (model_path / "tokenizer_config.json").exists()
+ or (model_path / "tiktoken.model").exists()
+ or (model_path / "tokenizer.model").exists()
+ )
+ if not has_tokenizer:
+ pytest.skip(f"Required tokenizer files not found for {model_id}")
+
+ # Load tokenizer
+ tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+
+ # Test basic encoding
+ test_text = "Hello, world!"
+ encoded = tokenizer.encode(test_text)
+ assert isinstance(encoded, list), f"encode() should return a list for {model_id}"
+ assert len(encoded) > 0, f"encode() should return non-empty list for {model_id}"
+ assert all(isinstance(t, int) for t in encoded), (
+ f"All tokens should be integers for {model_id}"
+ )
+
+ # Test decoding
+ decoded = tokenizer.decode(encoded)
+ assert isinstance(decoded, str), f"decode() should return a string for {model_id}"
+ assert test_text in decoded or decoded.strip() == test_text.strip(), (
+ f"decode(encode(x)) should preserve text for {model_id}: got {decoded!r}"
+ )
+
+ # Test with longer text
+ long_text = "The quick brown fox jumps over the lazy dog. " * 10
+ long_encoded = tokenizer.encode(long_text)
+ assert len(long_encoded) > len(encoded), (
+ f"Longer text should produce more tokens for {model_id}"
+ )
+
+ # Test empty string
+ empty_encoded = tokenizer.encode("")
+ assert isinstance(empty_encoded, list), (
+ f"encode('') should return a list for {model_id}"
+ )
+
+ # Test special characters
+ special_text = 'Hello!\n\tWorld? <test> & "quotes"'
+ special_encoded = tokenizer.encode(special_text)
+ assert len(special_encoded) > 0, f"Special chars should encode for {model_id}"
+
+ # Test unicode
+ unicode_text = "Hello 世界 🌍"
+ unicode_encoded = tokenizer.encode(unicode_text)
+ assert len(unicode_encoded) > 0, f"Unicode should encode for {model_id}"
+
+
+@pytest.mark.parametrize(
+ "short_id,model_card",
+ TEST_MODELS,
+ ids=[m[0] for m in TEST_MODELS],
+)
+@pytest.mark.asyncio
+async def test_tokenizer_has_required_attributes(
+ short_id: str, model_card: ModelCard
+) -> None:
+ """Test that tokenizer has required attributes for inference."""
+ model_id = str(model_card.model_id)
+
+ model_path = await download_tokenizer_files(model_id)
+
+ has_tokenizer = (
+ (model_path / "tokenizer.json").exists()
+ or (model_path / "tokenizer_config.json").exists()
+ or (model_path / "tiktoken.model").exists()
+ or (model_path / "tokenizer.model").exists()
+ )
+ if not has_tokenizer:
+ pytest.skip(f"Required tokenizer files not found for {model_id}")
+
+ tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+ eos_token_ids = get_eos_token_ids_for_model(model_id)
+
+ # Check for vocabulary size
+ empty_vocab: dict[str, int] = {}
+ vocab_size: int = getattr(tokenizer, "vocab_size", None) or len(
+ getattr(tokenizer, "get_vocab", lambda: empty_vocab)()
+ )
+ assert vocab_size > 0, f"Tokenizer should have vocab_size > 0 for {model_id}"
+
+ # Check for EOS token (either from tokenizer or explicitly provided)
+ has_eos = (
+ eos_token_ids is not None
+ or getattr(tokenizer, "eos_token_id", None) is not None
+ or getattr(tokenizer, "eos_token", None) is not None
+ )
+ assert has_eos, f"Tokenizer should have EOS token for {model_id}"
+
+
+@pytest.mark.parametrize(
+ "short_id,model_card",
+ TEST_MODELS,
+ ids=[m[0] for m in TEST_MODELS],
+)
+@pytest.mark.asyncio
+async def test_tokenizer_special_tokens(short_id: str, model_card: ModelCard) -> None:
+ """Test that tokenizer can encode text containing special tokens.
+
+ This is critical because the actual inference path uses prompts with
+ special tokens from chat templates. If special tokens aren't handled
+ correctly, encoding will fail.
+ """
+ model_id = str(model_card.model_id)
+
+ model_path = await download_tokenizer_files(model_id)
+
+ has_tokenizer = (
+ (model_path / "tokenizer.json").exists()
+ or (model_path / "tokenizer_config.json").exists()
+ or (model_path / "tiktoken.model").exists()
+ or (model_path / "tokenizer.model").exists()
+ )
+ assert has_tokenizer, f"Required tokenizer files not found for {model_id}"
+
+ tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+
+ # Get special tokens from the tokenizer
+ special_tokens: list[str] = []
+
+ # Try to get special tokens from various sources
+ if hasattr(tokenizer, "all_special_tokens"):
+ special_tokens.extend(tokenizer.all_special_tokens)
+ elif hasattr(tokenizer, "_tokenizer") and hasattr(
+ tokenizer._tokenizer,
+ "all_special_tokens",
+ ):
+ special_tokens.extend(tokenizer._tokenizer.all_special_tokens)
+
+ # Also check for common special token attributes
+ for attr in [
+ "bos_token",
+ "eos_token",
+ "pad_token",
+ "unk_token",
+ "sep_token",
+ "cls_token",
+ ]:
+ token = getattr(tokenizer, attr, None)
+ if token is None and hasattr(tokenizer, "_tokenizer"):
+ token = getattr(tokenizer._tokenizer, attr, None)
+ if token and isinstance(token, str) and token not in special_tokens:
+ special_tokens.append(token)
+
+ # If we found special tokens, test encoding text that contains them
+ if special_tokens:
+ # Create text with special tokens interspersed
+ test_with_special = f"{special_tokens[0]}Hello world"
+ if len(special_tokens) > 1:
+ test_with_special += f"{special_tokens[1]}"
+
+ encoded = tokenizer.encode(test_with_special)
+ assert isinstance(encoded, list), (
+ f"encode() with special tokens should return list for {model_id}"
+ )
+ assert len(encoded) > 0, (
+ f"encode() with special tokens should return non-empty list for {model_id}"
+ )
+ assert all(isinstance(t, int) for t in encoded), (
+ f"All tokens should be integers for {model_id}"
+ )
+
+ # Verify we can decode
+ decoded = tokenizer.decode(encoded)
+ assert isinstance(decoded, str), f"decode() should return string for {model_id}"
+
+ # Test with angle-bracket tokens (common format for special tokens)
+ # These should not raise errors even if they're not actual special tokens
+ angle_bracket_text = "<|test|>Hello<|end|>"
+ encoded = tokenizer.encode(angle_bracket_text)
+ assert isinstance(encoded, list), (
+ f"encode() with angle brackets should return list for {model_id}"
+ )
+ assert len(encoded) > 0, (
+ f"encode() with angle brackets should be non-empty for {model_id}"
+ )
+
+
+# Specifically test Kimi tokenizer since it has special handling
+@pytest.mark.asyncio
+async def test_kimi_tokenizer_specifically():
+ """Test Kimi tokenizer with its specific patches and quirks."""
+ kimi_models = [
+ (short_id, card)
+ for short_id, card in MODEL_CARDS.items()
+ if "kimi" in short_id.lower()
+ ]
+
+ if not kimi_models:
+ pytest.skip("No Kimi models found in MODEL_CARDS")
+
+ _, model_card = kimi_models[0]
+ model_id = str(model_card.model_id)
+
+ model_path = await download_tokenizer_files(model_id)
+
+ # Ensure the custom tokenizer file exists
+ if not (model_path / "tokenization_kimi.py").exists():
+ pytest.skip("tokenization_kimi.py not found")
+
+ tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+ eos_token_ids = get_eos_token_ids_for_model(model_id)
+
+ # Test encode/decode cycle
+ test_text = "Hello, world!"
+ encoded = tokenizer.encode(test_text)
+ decoded = tokenizer.decode(encoded)
+
+ assert len(encoded) > 0, "Kimi tokenizer should encode text"
+ assert isinstance(decoded, str), "Kimi tokenizer should decode to string"
+
+ # Test that the patched encode works (returns list of ints)
+ assert all(isinstance(t, int) for t in encoded), "Tokens should be integers"
+
+ # Test encoding text with special tokens (like from chat templates)
+ # This is critical - the warmup inference uses prompts with special tokens
+ special_token_text = "<|im_user|>user<|im_middle|>Hello<|im_end|><|im_assistant|>"
+ special_encoded = tokenizer.encode(special_token_text)
+ assert len(special_encoded) > 0, "Kimi tokenizer should handle special tokens"
+ assert all(isinstance(t, int) for t in special_encoded), (
+ "Special token encoding should return integers"
+ )
+
+ # Verify EOS token is set
+ assert eos_token_ids == [163586], "Kimi EOS token should be [163586]"
+
+
+# Test GLM tokenizer since it also has special handling
+@pytest.mark.asyncio
+async def test_glm_tokenizer_specifically():
+ """Test GLM tokenizer with its specific EOS tokens."""
+ glm_models = [
+ (short_id, card)
+ for short_id, card in MODEL_CARDS.items()
+ if "glm" in short_id.lower()
+ ]
+
+ if not glm_models:
+ pytest.skip("No GLM models found in MODEL_CARDS")
+
+ _, model_card = glm_models[0]
+ model_id = str(model_card.model_id)
+
+ model_path = await download_tokenizer_files(model_id)
+
+ has_tokenizer = (model_path / "tokenizer.json").exists() or (
+ model_path / "tokenizer_config.json"
+ ).exists()
+ if not has_tokenizer:
+ pytest.skip("GLM tokenizer files not found")
+
+ tokenizer = load_tokenizer_for_model_id(model_id, model_path)
+ eos_token_ids = get_eos_token_ids_for_model(model_id)
+
+ # Test encode/decode
+ test_text = "Hello, world!"
+ encoded = tokenizer.encode(test_text)
+ decoded = tokenizer.decode(encoded)
+
+ assert len(encoded) > 0, "GLM tokenizer should encode text"
+ assert isinstance(decoded, str), "GLM tokenizer should decode to string"
+
+ # Verify EOS tokens
+ assert eos_token_ids == [
+ 151336,
+ 151329,
+ 151338,
+ ], "GLM EOS tokens should be correct"
diff --git a/tests/headless_runner.py b/tests/headless_runner.py
index 6728d143..5cef5069 100644
--- a/tests/headless_runner.py
+++ b/tests/headless_runner.py
@@ -49,14 +49,12 @@ class Tests(BaseModel):
kind: typing.Literal["init", "warmup", "inference"]
-hn = socket.gethostname()
mp.set_start_method("spawn", force=True)
logger_setup(None)
async def main():
logger.info("starting cool server majig")
- logger.info(hn)
await assert_downloads()
cfg = Config()
cfg.bind = "0.0.0.0:52415"
@@ -81,20 +79,35 @@ async def main():
async def assert_downloads():
sd = exo_shard_downloader()
# await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-0.6b"].model_id))
- await sd.ensure_shard(await build_full_shard(MODEL_CARDS["llama-3.2-1b"].model_id))
+ await sd.ensure_shard(
+ await build_full_shard(MODEL_CARDS["llama-3.1-8b-bf16"].model_id)
+ )
+ await sd.ensure_shard(await build_full_shard(MODEL_CARDS["qwen3-30b"].model_id))
+ await sd.ensure_shard(
+ await build_full_shard(MODEL_CARDS["gpt-oss-120b-MXFP4-Q8"].model_id)
+ )
+ await sd.ensure_shard(
+ await build_full_shard(MODEL_CARDS["gpt-oss-20b-4bit"].model_id)
+ )
async def ring_backend(test: Tests):
iid = InstanceId(str(hash(str(test.devs))))
- return await execute_test(test, ring_instance(test, iid))
+ weird_hn = socket.gethostname()
+ for dev in test.devs:
+ if weird_hn.startswith(dev[0]) or dev[0].startswith(weird_hn):
+ hn = dev[0]
+ break
+ else:
+ raise ValueError(f"{weird_hn} not in {test.devs}")
+ return await execute_test(test, ring_instance(test, iid, hn), hn)
-def ring_instance(test: Tests, iid: InstanceId) -> Instance:
- global hn
+def ring_instance(test: Tests, iid: InstanceId, hn: str) -> Instance:
hbn = [Host(ip="i dont care", port=52416) for _ in test.devs]
world_size = len(test.devs)
for i in range(world_size):
- if hn.startswith(test.devs[i][0]):
+ if test.devs[i][0] == hn:
hn = test.devs[i][0]
if i - 1 >= 0:
hbn[i - 1] = Host(ip=test.devs[i - 1][1], port=52416)
@@ -102,6 +115,8 @@ def ring_instance(test: Tests, iid: InstanceId) -> Instance:
hbn[i + 1] = Host(ip=test.devs[i + 1][1], port=52416)
hbn[i] = Host(ip="0.0.0.0", port=52416)
break
+ else:
+ raise ValueError(f"{hn} not in {test.devs}")
meta = MODEL_CARDS[test.model_id].metadata
instance = MlxRingInstance(
@@ -131,10 +146,10 @@ def ring_instance(test: Tests, iid: InstanceId) -> Instance:
return instance
-async def execute_test(test: Tests, instance: Instance):
+async def execute_test(test: Tests, instance: Instance, hn: str):
world_size = len(test.devs)
iid = InstanceId(str(hash(str(test.devs))))
- _handle, recv, send = new_runner(instance)
+ _handle, recv, send = new_runner(instance, hn)
if world_size > 1:
send.send(ConnectToGroup(instance_id=iid))
send.send(LoadModel(instance_id=iid))
@@ -181,17 +196,19 @@ async def execute_test(test: Tests, instance: Instance):
async def jaccl_backend(test: Tests):
iid = InstanceId(str(hash(str(test.devs))))
- return await execute_test(test, jaccl_instance(test, iid))
+ weird_hn = socket.gethostname()
+ for dev in test.devs:
+ if weird_hn.startswith(dev[0]) or dev[0].startswith(weird_hn):
+ hn = dev[0]
+ break
+ else:
+ raise ValueError(f"{weird_hn} not in {test.devs}")
+ return await execute_test(test, jaccl_instance(test, iid, hn), hn)
-def jaccl_instance(test: Tests, iid: InstanceId):
- global hn
+def jaccl_instance(test: Tests, iid: InstanceId, hn: str):
meta = MODEL_CARDS[test.model_id].metadata
world_size = len(test.devs)
- for name, _ in test.devs:
- if hn.startswith(name):
- hn = name
- break
return MlxJacclInstance(
instance_id=iid,
@@ -220,6 +237,7 @@ def jaccl_instance(test: Tests, iid: InstanceId):
def new_runner(
instance: Instance,
+ hn: str,
) -> tuple[mp.Process, MpReceiver[Event], MpSender[Task]]:
bound_instance = BoundInstance(
instance=instance, bound_runner_id=RunnerId(hn), bound_node_id=NodeId(hn)
diff --git a/tests/start_distributed_test.sh b/tests/start_distributed_test.sh
index 33781e04..7130f1ff 100755
--- a/tests/start_distributed_test.sh
+++ b/tests/start_distributed_test.sh
@@ -34,19 +34,23 @@ done
devs_raw=$(printf "[\"%s\", \"%s\"], " "${weaved[@]}")
devs="[${devs_raw%, }]"
-for i in "${!ips[@]}"; do
- {
- req="{
- \"model_id\": \"llama-3.2-1b\",
- \"devs\": ${devs},
- \"kind\": \"inference\"
- }"
- echo "req $req"
- curl -sN \
- -X POST "http://${ips[$i]}:52415/${kind}" \
- -H "Content-Type: application/json" -d "$req" \
- 2>&1 | sed "s/^/\n${hostnames[$i]}@${ips[$i]}: /" || echo "curl to ${hostnames[$i]} failed"
- } &
+model_ids=("qwen3-30b" "gpt-oss-120b-MXFP4-Q8" "kimi-k2-thinking")
+
+for model_id in "${model_ids[@]}"; do
+ for i in "${!ips[@]}"; do
+ {
+ req="{
+ \"model_id\": \"${model_id}\",
+ \"devs\": ${devs},
+ \"kind\": \"inference\"
+ }"
+ echo "req $req"
+ curl -sN \
+ -X POST "http://${ips[$i]}:52415/${kind}" \
+ -H "Content-Type: application/json" -d "$req" \
+ 2>&1 | sed "s/^/\n${hostnames[$i]}@${ips[$i]}: /" || echo "curl to ${hostnames[$i]} failed" && exit 1
+ } &
+ done
+ wait
done
-wait
diff --git a/uv.lock b/uv.lock
index d1a6644f..39fca469 100644
--- a/uv.lock
+++ b/uv.lock
@@ -1,5 +1,5 @@
version = 1
-revision = 3
+revision = 1
requires-python = ">=3.13"
resolution-markers = [
"sys_platform == 'darwin'",
@@ -10,6 +10,9 @@ supported-markers = [
"sys_platform == 'linux'",
]
+[options]
+prerelease-mode = "allow"
+
[manifest]
members = [
"exo",
@@ -20,23 +23,23 @@ members = [
name = "aiofiles"
version = "25.1.0"
source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354, upload-time = "2025-10-09T20:51:04.358Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/41/c3/534eac40372d8ee36ef40df62ec129bee4fdb5ad9706e58a29be53b2c970/aiofiles-25.1.0.tar.gz", hash = "sha256:a8d728f0a29de45dc521f18f07297428d56992a742f0cd2701ba86e44d23d5b2", size = 46354 }
wheels = [
- { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668, upload-time = "2025-10-09T20:51:03.174Z" },
+ { url = "https://files.pythonhosted.org/packages/bc/8a/340a1555ae33d7354dbca4faa54948d76d89a27ceef032c8c3bc661d003e/aiofiles-25.1.0-py3-none-any.whl", hash = "sha256:abe311e527c862958650f9438e859c1fa7568a141b22abcd015e120e86a85695", size = 14668 },
]
[[package]]
name = "aiohappyeyeballs"
version = "2.6.1"
source = { registry = "https://pypi.org/simple" }
-sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760, upload-time = "2025-03-12T01:42:48.764Z" }
+sdist = { url = "https://files.pythonhosted.org/packages/26/30/f84a107a9c4331c14b2b586036f40965c128aa4fee4dda5d3d51cb14ad54/aiohappyeyeballs-2.6.1.tar.gz", hash = "sha256:c3f9d0113123803ccadfdf3f0faa505bc78e6a72d1cc4806cbd719826e943558", size = 22760 }
wheels = [
- { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265, upload-time = "2025-03-12T01:42:47.083Z" },
+ { url = "https://files.pythonhosted.org/packages/0f/15/5bf3b99495fb160b63f95972b81750f18f7f4e02ad051373b669d17d44f2/aiohappyeyeballs-2.6.1-py3-none-any.whl", hash = "sha256:f349ba8f4b75cb25c99c5c2d84e997e485204d2902a9597802b0371f09331fb8", size = 15265 },
]
[[package]]
name = "aiohttp"
-version = "3.13.2"
+version = "3.13.3"
source = { registry = "https://pypi.org/simple" }
dependencies = [
{ name = "aiohappyeyeballs", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
@@ -47,53 +50,53 @@ dependencies = [
{ name = "propcache", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
{ name = "yarl", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
]
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@@ -228,6 +252,7 @@ dependencies = [
[package.dev-dependencies]
dev = [
+ { name = "basedpyright", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
{ name = "pyinstaller", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
{ name = "pytest", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
{ name = "pytest-asyncio", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" },
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