LocalAI/backend/python/mlx-distributed/test.py

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feat: refactor shared helpers and enhance MLX backend functionality (#9335) * refactor(backends): extract python_utils + add mlx_utils shared helpers Move parse_options() and messages_to_dicts() out of vllm_utils.py into a new framework-agnostic python_utils.py, and re-export them from vllm_utils so existing vllm / vllm-omni imports keep working. Add mlx_utils.py with split_reasoning() and parse_tool_calls() — ported from mlx_vlm/server.py's process_tool_calls. These work with any mlx-lm / mlx-vlm tool module (anything exposing tool_call_start, tool_call_end, parse_tool_call). Used by the mlx and mlx-vlm backends in later commits to emit structured ChatDelta.tool_calls without reimplementing per-model parsing. Shared smoke tests confirm: - parse_options round-trips bool/int/float/string - vllm_utils re-exports are identity-equal to python_utils originals - mlx_utils parse_tool_calls handles <tool_call>...</tool_call> with a shim module and produces a correctly-indexed list with JSON arguments - mlx_utils split_reasoning extracts <think> blocks and leaves clean content * feat(mlx): wire native tool parsers + ChatDelta + token usage + logprobs Bring the MLX backend up to the same structured-output contract as vLLM and llama.cpp: emit Reply.chat_deltas so the OpenAI HTTP layer sees tool_calls and reasoning_content, not just raw text. Key insight: mlx_lm.load() returns a TokenizerWrapper that already auto- detects the right tool parser from the model's chat template (_infer_tool_parser in mlx_lm/tokenizer_utils.py). The wrapper exposes has_tool_calling, has_thinking, tool_parser, tool_call_start, tool_call_end, think_start, think_end — no user configuration needed, unlike vLLM. Changes in backend/python/mlx/backend.py: - Imports: replace inline parse_options / messages_to_dicts with the shared helpers from python_utils. Pull split_reasoning / parse_tool_calls from the new mlx_utils shared module. - LoadModel: log the auto-detected has_tool_calling / has_thinking / tool_parser_type for observability. Drop the local is_float / is_int duplicates. - _prepare_prompt: run request.Messages through messages_to_dicts so tool_call_id / tool_calls / reasoning_content survive the conversion, and pass tools=json.loads(request.Tools) + enable_thinking=True (when request.Metadata says so) to apply_chat_template. Falls back on TypeError for tokenizers whose template doesn't accept those kwargs. - _build_generation_params: return an additional (logits_params, stop_words) pair. Maps RepetitionPenalty / PresencePenalty / FrequencyPenalty to mlx_lm.sample_utils.make_logits_processors and threads StopPrompts through to post-decode truncation. - New _tool_module_from_tokenizer / _finalize_output / _truncate_at_stop helpers. _finalize_output runs split_reasoning when has_thinking is true and parse_tool_calls (using a SimpleNamespace shim around the wrapper's tool_parser callable) when has_tool_calling is true, then extracts prompt_tokens, generation_tokens and (best-effort) logprobs from the last GenerationResponse chunk. - Predict: use make_logits_processors, accumulate text + last_response, finalize into a structured Reply carrying chat_deltas, prompt_tokens, tokens, logprobs. Early-stops on user stop sequences. - PredictStream: per-chunk Reply still carries raw message bytes for back-compat but now also emits chat_deltas=[ChatDelta(content=delta)]. On loop exit, emit a terminal Reply with structured reasoning_content / tool_calls / token counts / logprobs — so the Go side sees tool calls without needing the regex fallback. - TokenizeString RPC: uses the TokenizerWrapper's encode(); returns length + tokens or FAILED_PRECONDITION if the model isn't loaded. - Free RPC: drops model / tokenizer / lru_cache, runs gc.collect(), calls mx.metal.clear_cache() when available, and best-effort clears torch.cuda as a belt-and-suspenders. * feat(mlx-vlm): mirror MLX parity (tool parsers + ChatDelta + samplers) Same treatment as the MLX backend: emit structured Reply.chat_deltas, tool_calls, reasoning_content, token counts and logprobs, and extend sampling parameter coverage beyond the temp/top_p pair the backend used to handle. - Imports: drop the inline is_float/is_int helpers, pull parse_options / messages_to_dicts from python_utils and split_reasoning / parse_tool_calls from mlx_utils. Also import make_sampler and make_logits_processors from mlx_lm.sample_utils — mlx-vlm re-uses them. - LoadModel: use parse_options; call mlx_vlm.tool_parsers._infer_tool_parser / load_tool_module to auto-detect a tool module from the processor's chat_template. Stash think_start / think_end / has_thinking so later finalisation can split reasoning blocks without duck-typing on each call. Logs the detected parser type. - _prepare_prompt: convert proto Messages via messages_to_dicts (so tool_call_id / tool_calls survive), pass tools=json.loads(request.Tools) and enable_thinking=True to apply_chat_template when present, fall back on TypeError for older mlx-vlm versions. Also handle the prompt-only + media and empty-prompt + media paths consistently. - _build_generation_params: return (max_tokens, sampler_params, logits_params, stop_words). Maps repetition_penalty / presence_penalty / frequency_penalty and passes them through make_logits_processors. - _finalize_output / _truncate_at_stop: common helper used by Predict and PredictStream to split reasoning, run parse_tool_calls against the auto-detected tool module, build ToolCallDelta list, and extract token counts + logprobs from the last GenerationResult. - Predict / PredictStream: switch from mlx_vlm.generate to mlx_vlm.stream_generate in both paths, accumulate text + last_response, pass sampler and logits_processors through, emit content-only ChatDelta per streaming chunk followed by a terminal Reply carrying reasoning_content, tool_calls, prompt_tokens, tokens and logprobs. Non-streaming Predict returns the same structured Reply shape. - New helper _collect_media extracted from the duplicated base64 image / audio decode loop. - New TokenizeString RPC using the processor's tokenizer.encode and Free RPC that drops model/processor/config, runs gc + Metal cache clear + best-effort torch.cuda cache clear. * feat(importer/mlx): auto-set tool_parser/reasoning_parser on import Mirror what core/gallery/importers/vllm.go does: after applying the shared inference defaults, look up the model URI in parser_defaults.json and append matching tool_parser:/reasoning_parser: entries to Options. The MLX backends auto-detect tool parsers from the chat template at runtime so they don't actually consume these options — but surfacing them in the generated YAML: - keeps the import experience consistent with vllm - gives users a single visible place to override - documents the intended parser for a given model family * test(mlx): add helper unit tests + TokenizeString/Free + e2e make targets - backend/python/mlx/test.py: add TestSharedHelpers with server-less unit tests for parse_options, messages_to_dicts, split_reasoning and parse_tool_calls (using a SimpleNamespace shim to fake a tool module without requiring a model). Plus test_tokenize_string and test_free RPC tests that load a tiny MLX-quantized Llama and exercise the new RPCs end-to-end. - backend/python/mlx-vlm/test.py: same helper unit tests + cleanup of the duplicated import block at the top of the file. - Makefile: register BACKEND_MLX and BACKEND_MLX_VLM (they were missing from the docker-build-target eval list — only mlx-distributed had a generated target before). Add test-extra-backend-mlx and test-extra-backend-mlx-vlm convenience targets that build the respective image and run tests/e2e-backends with the tools capability against mlx-community/Qwen2.5-0.5B-Instruct-4bit. The MLX backend auto-detects the tool parser from the chat template so no BACKEND_TEST_OPTIONS is needed (unlike vllm). * fix(libbackend): don't pass --copies to venv unless PORTABLE_PYTHON=true backend/python/common/libbackend.sh:ensureVenv() always invoked 'python -m venv --copies', but macOS system python (and some other builds) refuses with: Error: This build of python cannot create venvs without using symlinks --copies only matters when _makeVenvPortable later relocates the venv, which only happens when PORTABLE_PYTHON=true. Make --copies conditional on that flag and fall back to default (symlinked) venv otherwise. Caught while bringing up the mlx backend on Apple Silicon — the same build path is used by every Python backend with USE_PIP=true. * fix(mlx): support mlx-lm 0.29.x tool calling + drop deprecated clear_cache The released mlx-lm 0.29.x ships a much simpler tool-calling API than HEAD: TokenizerWrapper detects the <tool_call>...</tool_call> markers from the tokenizer vocab and exposes has_tool_calling / tool_call_start / tool_call_end, but does NOT expose a tool_parser callable on the wrapper and does NOT ship a mlx_lm.tool_parsers subpackage at all (those only exist on main). Caught while running the smoke test on Apple Silicon with the released mlx-lm 0.29.1: tokenizer.tool_parser raised AttributeError (falling through to the underlying HF tokenizer), so _tool_module_from_tokenizer always returned None and tool calls slipped through as raw <tool_call>...</tool_call> text in Reply.message instead of being parsed into ChatDelta.tool_calls. Fix: when has_tool_calling is True but tokenizer.tool_parser is missing, default the parse_tool_call callable to json.loads(body.strip()) — that's exactly what mlx_lm.tool_parsers.json_tools.parse_tool_call does on HEAD and covers the only format 0.29 detects (<tool_call>JSON</tool_call>). Future mlx-lm releases that ship more parsers will be picked up automatically via the tokenizer.tool_parser attribute when present. Also tighten the LoadModel logging — the old log line read init_kwargs.get('tool_parser_type') which doesn't exist on 0.29 and showed None even when has_tool_calling was True. Log the actual tool_call_start / tool_call_end markers instead. While here, switch Free()'s Metal cache clear from the deprecated mx.metal.clear_cache to mx.clear_cache (mlx >= 0.30), with a fallback for older releases. Mirrored to the mlx-vlm backend. * feat(mlx-distributed): mirror MLX parity (tool calls + ChatDelta + sampler) Same treatment as the mlx and mlx-vlm backends: emit Reply.chat_deltas with structured tool_calls / reasoning_content / token counts / logprobs, expand sampling parameter coverage beyond temp+top_p, and add the missing TokenizeString and Free RPCs. Notes specific to mlx-distributed: - Rank 0 is the only rank that owns a sampler — workers participate in the pipeline-parallel forward pass via mx.distributed and don't re-implement sampling. So the new logits_params (repetition_penalty, presence_penalty, frequency_penalty) and stop_words apply on rank 0 only; we don't need to extend coordinator.broadcast_generation_params, which still ships only max_tokens / temperature / top_p to workers (everything else is a rank-0 concern). - Free() now broadcasts CMD_SHUTDOWN to workers when a coordinator is active, so they release the model on their end too. The constant is already defined and handled by the existing worker loop in backend.py:633 (CMD_SHUTDOWN = -1). - Drop the locally-defined is_float / is_int / parse_options trio in favor of python_utils.parse_options, re-exported under the module name for back-compat with anything that imported it directly. - _prepare_prompt: route through messages_to_dicts so tool_call_id / tool_calls / reasoning_content survive, pass tools=json.loads( request.Tools) and enable_thinking=True to apply_chat_template, fall back on TypeError for templates that don't accept those kwargs. - New _tool_module_from_tokenizer (with the json.loads fallback for mlx-lm 0.29.x), _finalize_output, _truncate_at_stop helpers — same contract as the mlx backend. - LoadModel logs the auto-detected has_tool_calling / has_thinking / tool_call_start / tool_call_end so users can see what the wrapper picked up for the loaded model. - backend/python/mlx-distributed/test.py: add the same TestSharedHelpers unit tests (parse_options, messages_to_dicts, split_reasoning, parse_tool_calls) that exist for mlx and mlx-vlm.
2026-04-13 16:44:03 +00:00
import os
import sys
import types
import unittest
import subprocess
import time
import grpc
import backend_pb2
import backend_pb2_grpc
feat: refactor shared helpers and enhance MLX backend functionality (#9335) * refactor(backends): extract python_utils + add mlx_utils shared helpers Move parse_options() and messages_to_dicts() out of vllm_utils.py into a new framework-agnostic python_utils.py, and re-export them from vllm_utils so existing vllm / vllm-omni imports keep working. Add mlx_utils.py with split_reasoning() and parse_tool_calls() — ported from mlx_vlm/server.py's process_tool_calls. These work with any mlx-lm / mlx-vlm tool module (anything exposing tool_call_start, tool_call_end, parse_tool_call). Used by the mlx and mlx-vlm backends in later commits to emit structured ChatDelta.tool_calls without reimplementing per-model parsing. Shared smoke tests confirm: - parse_options round-trips bool/int/float/string - vllm_utils re-exports are identity-equal to python_utils originals - mlx_utils parse_tool_calls handles <tool_call>...</tool_call> with a shim module and produces a correctly-indexed list with JSON arguments - mlx_utils split_reasoning extracts <think> blocks and leaves clean content * feat(mlx): wire native tool parsers + ChatDelta + token usage + logprobs Bring the MLX backend up to the same structured-output contract as vLLM and llama.cpp: emit Reply.chat_deltas so the OpenAI HTTP layer sees tool_calls and reasoning_content, not just raw text. Key insight: mlx_lm.load() returns a TokenizerWrapper that already auto- detects the right tool parser from the model's chat template (_infer_tool_parser in mlx_lm/tokenizer_utils.py). The wrapper exposes has_tool_calling, has_thinking, tool_parser, tool_call_start, tool_call_end, think_start, think_end — no user configuration needed, unlike vLLM. Changes in backend/python/mlx/backend.py: - Imports: replace inline parse_options / messages_to_dicts with the shared helpers from python_utils. Pull split_reasoning / parse_tool_calls from the new mlx_utils shared module. - LoadModel: log the auto-detected has_tool_calling / has_thinking / tool_parser_type for observability. Drop the local is_float / is_int duplicates. - _prepare_prompt: run request.Messages through messages_to_dicts so tool_call_id / tool_calls / reasoning_content survive the conversion, and pass tools=json.loads(request.Tools) + enable_thinking=True (when request.Metadata says so) to apply_chat_template. Falls back on TypeError for tokenizers whose template doesn't accept those kwargs. - _build_generation_params: return an additional (logits_params, stop_words) pair. Maps RepetitionPenalty / PresencePenalty / FrequencyPenalty to mlx_lm.sample_utils.make_logits_processors and threads StopPrompts through to post-decode truncation. - New _tool_module_from_tokenizer / _finalize_output / _truncate_at_stop helpers. _finalize_output runs split_reasoning when has_thinking is true and parse_tool_calls (using a SimpleNamespace shim around the wrapper's tool_parser callable) when has_tool_calling is true, then extracts prompt_tokens, generation_tokens and (best-effort) logprobs from the last GenerationResponse chunk. - Predict: use make_logits_processors, accumulate text + last_response, finalize into a structured Reply carrying chat_deltas, prompt_tokens, tokens, logprobs. Early-stops on user stop sequences. - PredictStream: per-chunk Reply still carries raw message bytes for back-compat but now also emits chat_deltas=[ChatDelta(content=delta)]. On loop exit, emit a terminal Reply with structured reasoning_content / tool_calls / token counts / logprobs — so the Go side sees tool calls without needing the regex fallback. - TokenizeString RPC: uses the TokenizerWrapper's encode(); returns length + tokens or FAILED_PRECONDITION if the model isn't loaded. - Free RPC: drops model / tokenizer / lru_cache, runs gc.collect(), calls mx.metal.clear_cache() when available, and best-effort clears torch.cuda as a belt-and-suspenders. * feat(mlx-vlm): mirror MLX parity (tool parsers + ChatDelta + samplers) Same treatment as the MLX backend: emit structured Reply.chat_deltas, tool_calls, reasoning_content, token counts and logprobs, and extend sampling parameter coverage beyond the temp/top_p pair the backend used to handle. - Imports: drop the inline is_float/is_int helpers, pull parse_options / messages_to_dicts from python_utils and split_reasoning / parse_tool_calls from mlx_utils. Also import make_sampler and make_logits_processors from mlx_lm.sample_utils — mlx-vlm re-uses them. - LoadModel: use parse_options; call mlx_vlm.tool_parsers._infer_tool_parser / load_tool_module to auto-detect a tool module from the processor's chat_template. Stash think_start / think_end / has_thinking so later finalisation can split reasoning blocks without duck-typing on each call. Logs the detected parser type. - _prepare_prompt: convert proto Messages via messages_to_dicts (so tool_call_id / tool_calls survive), pass tools=json.loads(request.Tools) and enable_thinking=True to apply_chat_template when present, fall back on TypeError for older mlx-vlm versions. Also handle the prompt-only + media and empty-prompt + media paths consistently. - _build_generation_params: return (max_tokens, sampler_params, logits_params, stop_words). Maps repetition_penalty / presence_penalty / frequency_penalty and passes them through make_logits_processors. - _finalize_output / _truncate_at_stop: common helper used by Predict and PredictStream to split reasoning, run parse_tool_calls against the auto-detected tool module, build ToolCallDelta list, and extract token counts + logprobs from the last GenerationResult. - Predict / PredictStream: switch from mlx_vlm.generate to mlx_vlm.stream_generate in both paths, accumulate text + last_response, pass sampler and logits_processors through, emit content-only ChatDelta per streaming chunk followed by a terminal Reply carrying reasoning_content, tool_calls, prompt_tokens, tokens and logprobs. Non-streaming Predict returns the same structured Reply shape. - New helper _collect_media extracted from the duplicated base64 image / audio decode loop. - New TokenizeString RPC using the processor's tokenizer.encode and Free RPC that drops model/processor/config, runs gc + Metal cache clear + best-effort torch.cuda cache clear. * feat(importer/mlx): auto-set tool_parser/reasoning_parser on import Mirror what core/gallery/importers/vllm.go does: after applying the shared inference defaults, look up the model URI in parser_defaults.json and append matching tool_parser:/reasoning_parser: entries to Options. The MLX backends auto-detect tool parsers from the chat template at runtime so they don't actually consume these options — but surfacing them in the generated YAML: - keeps the import experience consistent with vllm - gives users a single visible place to override - documents the intended parser for a given model family * test(mlx): add helper unit tests + TokenizeString/Free + e2e make targets - backend/python/mlx/test.py: add TestSharedHelpers with server-less unit tests for parse_options, messages_to_dicts, split_reasoning and parse_tool_calls (using a SimpleNamespace shim to fake a tool module without requiring a model). Plus test_tokenize_string and test_free RPC tests that load a tiny MLX-quantized Llama and exercise the new RPCs end-to-end. - backend/python/mlx-vlm/test.py: same helper unit tests + cleanup of the duplicated import block at the top of the file. - Makefile: register BACKEND_MLX and BACKEND_MLX_VLM (they were missing from the docker-build-target eval list — only mlx-distributed had a generated target before). Add test-extra-backend-mlx and test-extra-backend-mlx-vlm convenience targets that build the respective image and run tests/e2e-backends with the tools capability against mlx-community/Qwen2.5-0.5B-Instruct-4bit. The MLX backend auto-detects the tool parser from the chat template so no BACKEND_TEST_OPTIONS is needed (unlike vllm). * fix(libbackend): don't pass --copies to venv unless PORTABLE_PYTHON=true backend/python/common/libbackend.sh:ensureVenv() always invoked 'python -m venv --copies', but macOS system python (and some other builds) refuses with: Error: This build of python cannot create venvs without using symlinks --copies only matters when _makeVenvPortable later relocates the venv, which only happens when PORTABLE_PYTHON=true. Make --copies conditional on that flag and fall back to default (symlinked) venv otherwise. Caught while bringing up the mlx backend on Apple Silicon — the same build path is used by every Python backend with USE_PIP=true. * fix(mlx): support mlx-lm 0.29.x tool calling + drop deprecated clear_cache The released mlx-lm 0.29.x ships a much simpler tool-calling API than HEAD: TokenizerWrapper detects the <tool_call>...</tool_call> markers from the tokenizer vocab and exposes has_tool_calling / tool_call_start / tool_call_end, but does NOT expose a tool_parser callable on the wrapper and does NOT ship a mlx_lm.tool_parsers subpackage at all (those only exist on main). Caught while running the smoke test on Apple Silicon with the released mlx-lm 0.29.1: tokenizer.tool_parser raised AttributeError (falling through to the underlying HF tokenizer), so _tool_module_from_tokenizer always returned None and tool calls slipped through as raw <tool_call>...</tool_call> text in Reply.message instead of being parsed into ChatDelta.tool_calls. Fix: when has_tool_calling is True but tokenizer.tool_parser is missing, default the parse_tool_call callable to json.loads(body.strip()) — that's exactly what mlx_lm.tool_parsers.json_tools.parse_tool_call does on HEAD and covers the only format 0.29 detects (<tool_call>JSON</tool_call>). Future mlx-lm releases that ship more parsers will be picked up automatically via the tokenizer.tool_parser attribute when present. Also tighten the LoadModel logging — the old log line read init_kwargs.get('tool_parser_type') which doesn't exist on 0.29 and showed None even when has_tool_calling was True. Log the actual tool_call_start / tool_call_end markers instead. While here, switch Free()'s Metal cache clear from the deprecated mx.metal.clear_cache to mx.clear_cache (mlx >= 0.30), with a fallback for older releases. Mirrored to the mlx-vlm backend. * feat(mlx-distributed): mirror MLX parity (tool calls + ChatDelta + sampler) Same treatment as the mlx and mlx-vlm backends: emit Reply.chat_deltas with structured tool_calls / reasoning_content / token counts / logprobs, expand sampling parameter coverage beyond temp+top_p, and add the missing TokenizeString and Free RPCs. Notes specific to mlx-distributed: - Rank 0 is the only rank that owns a sampler — workers participate in the pipeline-parallel forward pass via mx.distributed and don't re-implement sampling. So the new logits_params (repetition_penalty, presence_penalty, frequency_penalty) and stop_words apply on rank 0 only; we don't need to extend coordinator.broadcast_generation_params, which still ships only max_tokens / temperature / top_p to workers (everything else is a rank-0 concern). - Free() now broadcasts CMD_SHUTDOWN to workers when a coordinator is active, so they release the model on their end too. The constant is already defined and handled by the existing worker loop in backend.py:633 (CMD_SHUTDOWN = -1). - Drop the locally-defined is_float / is_int / parse_options trio in favor of python_utils.parse_options, re-exported under the module name for back-compat with anything that imported it directly. - _prepare_prompt: route through messages_to_dicts so tool_call_id / tool_calls / reasoning_content survive, pass tools=json.loads( request.Tools) and enable_thinking=True to apply_chat_template, fall back on TypeError for templates that don't accept those kwargs. - New _tool_module_from_tokenizer (with the json.loads fallback for mlx-lm 0.29.x), _finalize_output, _truncate_at_stop helpers — same contract as the mlx backend. - LoadModel logs the auto-detected has_tool_calling / has_thinking / tool_call_start / tool_call_end so users can see what the wrapper picked up for the loaded model. - backend/python/mlx-distributed/test.py: add the same TestSharedHelpers unit tests (parse_options, messages_to_dicts, split_reasoning, parse_tool_calls) that exist for mlx and mlx-vlm.
2026-04-13 16:44:03 +00:00
# Make the shared helpers importable so we can unit-test them without a
# running gRPC server.
sys.path.insert(0, os.path.join(os.path.dirname(__file__), '..', 'common'))
from python_utils import messages_to_dicts, parse_options
from mlx_utils import parse_tool_calls, split_reasoning
class TestBackendServicer(unittest.TestCase):
def setUp(self):
self.service = subprocess.Popen(
["python", "backend.py", "--addr", "localhost:50051"]
)
time.sleep(10)
def tearDown(self) -> None:
self.service.terminate()
self.service.wait()
def test_server_startup(self):
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.Health(backend_pb2.HealthMessage())
self.assertEqual(response.message, b'OK')
except Exception as err:
print(err)
self.fail("Server failed to start")
finally:
self.tearDown()
def test_load_model(self):
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="mlx-community/Llama-3.2-1B-Instruct-4bit"))
self.assertTrue(response.success)
self.assertEqual(response.message, "Model loaded successfully")
except Exception as err:
print(err)
self.fail("LoadModel service failed")
finally:
self.tearDown()
def test_text(self):
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="mlx-community/Llama-3.2-1B-Instruct-4bit"))
self.assertTrue(response.success)
req = backend_pb2.PredictOptions(Prompt="The capital of France is")
resp = stub.Predict(req)
self.assertIsNotNone(resp.message)
except Exception as err:
print(err)
self.fail("text service failed")
finally:
self.tearDown()
def test_sampling_params(self):
try:
self.setUp()
with grpc.insecure_channel("localhost:50051") as channel:
stub = backend_pb2_grpc.BackendStub(channel)
response = stub.LoadModel(backend_pb2.ModelOptions(Model="mlx-community/Llama-3.2-1B-Instruct-4bit"))
self.assertTrue(response.success)
req = backend_pb2.PredictOptions(
Prompt="The capital of France is",
TopP=0.8,
Tokens=50,
Temperature=0.7,
TopK=40,
MinP=0.05,
Seed=42,
)
resp = stub.Predict(req)
self.assertIsNotNone(resp.message)
except Exception as err:
print(err)
self.fail("sampling params service failed")
finally:
self.tearDown()
feat: refactor shared helpers and enhance MLX backend functionality (#9335) * refactor(backends): extract python_utils + add mlx_utils shared helpers Move parse_options() and messages_to_dicts() out of vllm_utils.py into a new framework-agnostic python_utils.py, and re-export them from vllm_utils so existing vllm / vllm-omni imports keep working. Add mlx_utils.py with split_reasoning() and parse_tool_calls() — ported from mlx_vlm/server.py's process_tool_calls. These work with any mlx-lm / mlx-vlm tool module (anything exposing tool_call_start, tool_call_end, parse_tool_call). Used by the mlx and mlx-vlm backends in later commits to emit structured ChatDelta.tool_calls without reimplementing per-model parsing. Shared smoke tests confirm: - parse_options round-trips bool/int/float/string - vllm_utils re-exports are identity-equal to python_utils originals - mlx_utils parse_tool_calls handles <tool_call>...</tool_call> with a shim module and produces a correctly-indexed list with JSON arguments - mlx_utils split_reasoning extracts <think> blocks and leaves clean content * feat(mlx): wire native tool parsers + ChatDelta + token usage + logprobs Bring the MLX backend up to the same structured-output contract as vLLM and llama.cpp: emit Reply.chat_deltas so the OpenAI HTTP layer sees tool_calls and reasoning_content, not just raw text. Key insight: mlx_lm.load() returns a TokenizerWrapper that already auto- detects the right tool parser from the model's chat template (_infer_tool_parser in mlx_lm/tokenizer_utils.py). The wrapper exposes has_tool_calling, has_thinking, tool_parser, tool_call_start, tool_call_end, think_start, think_end — no user configuration needed, unlike vLLM. Changes in backend/python/mlx/backend.py: - Imports: replace inline parse_options / messages_to_dicts with the shared helpers from python_utils. Pull split_reasoning / parse_tool_calls from the new mlx_utils shared module. - LoadModel: log the auto-detected has_tool_calling / has_thinking / tool_parser_type for observability. Drop the local is_float / is_int duplicates. - _prepare_prompt: run request.Messages through messages_to_dicts so tool_call_id / tool_calls / reasoning_content survive the conversion, and pass tools=json.loads(request.Tools) + enable_thinking=True (when request.Metadata says so) to apply_chat_template. Falls back on TypeError for tokenizers whose template doesn't accept those kwargs. - _build_generation_params: return an additional (logits_params, stop_words) pair. Maps RepetitionPenalty / PresencePenalty / FrequencyPenalty to mlx_lm.sample_utils.make_logits_processors and threads StopPrompts through to post-decode truncation. - New _tool_module_from_tokenizer / _finalize_output / _truncate_at_stop helpers. _finalize_output runs split_reasoning when has_thinking is true and parse_tool_calls (using a SimpleNamespace shim around the wrapper's tool_parser callable) when has_tool_calling is true, then extracts prompt_tokens, generation_tokens and (best-effort) logprobs from the last GenerationResponse chunk. - Predict: use make_logits_processors, accumulate text + last_response, finalize into a structured Reply carrying chat_deltas, prompt_tokens, tokens, logprobs. Early-stops on user stop sequences. - PredictStream: per-chunk Reply still carries raw message bytes for back-compat but now also emits chat_deltas=[ChatDelta(content=delta)]. On loop exit, emit a terminal Reply with structured reasoning_content / tool_calls / token counts / logprobs — so the Go side sees tool calls without needing the regex fallback. - TokenizeString RPC: uses the TokenizerWrapper's encode(); returns length + tokens or FAILED_PRECONDITION if the model isn't loaded. - Free RPC: drops model / tokenizer / lru_cache, runs gc.collect(), calls mx.metal.clear_cache() when available, and best-effort clears torch.cuda as a belt-and-suspenders. * feat(mlx-vlm): mirror MLX parity (tool parsers + ChatDelta + samplers) Same treatment as the MLX backend: emit structured Reply.chat_deltas, tool_calls, reasoning_content, token counts and logprobs, and extend sampling parameter coverage beyond the temp/top_p pair the backend used to handle. - Imports: drop the inline is_float/is_int helpers, pull parse_options / messages_to_dicts from python_utils and split_reasoning / parse_tool_calls from mlx_utils. Also import make_sampler and make_logits_processors from mlx_lm.sample_utils — mlx-vlm re-uses them. - LoadModel: use parse_options; call mlx_vlm.tool_parsers._infer_tool_parser / load_tool_module to auto-detect a tool module from the processor's chat_template. Stash think_start / think_end / has_thinking so later finalisation can split reasoning blocks without duck-typing on each call. Logs the detected parser type. - _prepare_prompt: convert proto Messages via messages_to_dicts (so tool_call_id / tool_calls survive), pass tools=json.loads(request.Tools) and enable_thinking=True to apply_chat_template when present, fall back on TypeError for older mlx-vlm versions. Also handle the prompt-only + media and empty-prompt + media paths consistently. - _build_generation_params: return (max_tokens, sampler_params, logits_params, stop_words). Maps repetition_penalty / presence_penalty / frequency_penalty and passes them through make_logits_processors. - _finalize_output / _truncate_at_stop: common helper used by Predict and PredictStream to split reasoning, run parse_tool_calls against the auto-detected tool module, build ToolCallDelta list, and extract token counts + logprobs from the last GenerationResult. - Predict / PredictStream: switch from mlx_vlm.generate to mlx_vlm.stream_generate in both paths, accumulate text + last_response, pass sampler and logits_processors through, emit content-only ChatDelta per streaming chunk followed by a terminal Reply carrying reasoning_content, tool_calls, prompt_tokens, tokens and logprobs. Non-streaming Predict returns the same structured Reply shape. - New helper _collect_media extracted from the duplicated base64 image / audio decode loop. - New TokenizeString RPC using the processor's tokenizer.encode and Free RPC that drops model/processor/config, runs gc + Metal cache clear + best-effort torch.cuda cache clear. * feat(importer/mlx): auto-set tool_parser/reasoning_parser on import Mirror what core/gallery/importers/vllm.go does: after applying the shared inference defaults, look up the model URI in parser_defaults.json and append matching tool_parser:/reasoning_parser: entries to Options. The MLX backends auto-detect tool parsers from the chat template at runtime so they don't actually consume these options — but surfacing them in the generated YAML: - keeps the import experience consistent with vllm - gives users a single visible place to override - documents the intended parser for a given model family * test(mlx): add helper unit tests + TokenizeString/Free + e2e make targets - backend/python/mlx/test.py: add TestSharedHelpers with server-less unit tests for parse_options, messages_to_dicts, split_reasoning and parse_tool_calls (using a SimpleNamespace shim to fake a tool module without requiring a model). Plus test_tokenize_string and test_free RPC tests that load a tiny MLX-quantized Llama and exercise the new RPCs end-to-end. - backend/python/mlx-vlm/test.py: same helper unit tests + cleanup of the duplicated import block at the top of the file. - Makefile: register BACKEND_MLX and BACKEND_MLX_VLM (they were missing from the docker-build-target eval list — only mlx-distributed had a generated target before). Add test-extra-backend-mlx and test-extra-backend-mlx-vlm convenience targets that build the respective image and run tests/e2e-backends with the tools capability against mlx-community/Qwen2.5-0.5B-Instruct-4bit. The MLX backend auto-detects the tool parser from the chat template so no BACKEND_TEST_OPTIONS is needed (unlike vllm). * fix(libbackend): don't pass --copies to venv unless PORTABLE_PYTHON=true backend/python/common/libbackend.sh:ensureVenv() always invoked 'python -m venv --copies', but macOS system python (and some other builds) refuses with: Error: This build of python cannot create venvs without using symlinks --copies only matters when _makeVenvPortable later relocates the venv, which only happens when PORTABLE_PYTHON=true. Make --copies conditional on that flag and fall back to default (symlinked) venv otherwise. Caught while bringing up the mlx backend on Apple Silicon — the same build path is used by every Python backend with USE_PIP=true. * fix(mlx): support mlx-lm 0.29.x tool calling + drop deprecated clear_cache The released mlx-lm 0.29.x ships a much simpler tool-calling API than HEAD: TokenizerWrapper detects the <tool_call>...</tool_call> markers from the tokenizer vocab and exposes has_tool_calling / tool_call_start / tool_call_end, but does NOT expose a tool_parser callable on the wrapper and does NOT ship a mlx_lm.tool_parsers subpackage at all (those only exist on main). Caught while running the smoke test on Apple Silicon with the released mlx-lm 0.29.1: tokenizer.tool_parser raised AttributeError (falling through to the underlying HF tokenizer), so _tool_module_from_tokenizer always returned None and tool calls slipped through as raw <tool_call>...</tool_call> text in Reply.message instead of being parsed into ChatDelta.tool_calls. Fix: when has_tool_calling is True but tokenizer.tool_parser is missing, default the parse_tool_call callable to json.loads(body.strip()) — that's exactly what mlx_lm.tool_parsers.json_tools.parse_tool_call does on HEAD and covers the only format 0.29 detects (<tool_call>JSON</tool_call>). Future mlx-lm releases that ship more parsers will be picked up automatically via the tokenizer.tool_parser attribute when present. Also tighten the LoadModel logging — the old log line read init_kwargs.get('tool_parser_type') which doesn't exist on 0.29 and showed None even when has_tool_calling was True. Log the actual tool_call_start / tool_call_end markers instead. While here, switch Free()'s Metal cache clear from the deprecated mx.metal.clear_cache to mx.clear_cache (mlx >= 0.30), with a fallback for older releases. Mirrored to the mlx-vlm backend. * feat(mlx-distributed): mirror MLX parity (tool calls + ChatDelta + sampler) Same treatment as the mlx and mlx-vlm backends: emit Reply.chat_deltas with structured tool_calls / reasoning_content / token counts / logprobs, expand sampling parameter coverage beyond temp+top_p, and add the missing TokenizeString and Free RPCs. Notes specific to mlx-distributed: - Rank 0 is the only rank that owns a sampler — workers participate in the pipeline-parallel forward pass via mx.distributed and don't re-implement sampling. So the new logits_params (repetition_penalty, presence_penalty, frequency_penalty) and stop_words apply on rank 0 only; we don't need to extend coordinator.broadcast_generation_params, which still ships only max_tokens / temperature / top_p to workers (everything else is a rank-0 concern). - Free() now broadcasts CMD_SHUTDOWN to workers when a coordinator is active, so they release the model on their end too. The constant is already defined and handled by the existing worker loop in backend.py:633 (CMD_SHUTDOWN = -1). - Drop the locally-defined is_float / is_int / parse_options trio in favor of python_utils.parse_options, re-exported under the module name for back-compat with anything that imported it directly. - _prepare_prompt: route through messages_to_dicts so tool_call_id / tool_calls / reasoning_content survive, pass tools=json.loads( request.Tools) and enable_thinking=True to apply_chat_template, fall back on TypeError for templates that don't accept those kwargs. - New _tool_module_from_tokenizer (with the json.loads fallback for mlx-lm 0.29.x), _finalize_output, _truncate_at_stop helpers — same contract as the mlx backend. - LoadModel logs the auto-detected has_tool_calling / has_thinking / tool_call_start / tool_call_end so users can see what the wrapper picked up for the loaded model. - backend/python/mlx-distributed/test.py: add the same TestSharedHelpers unit tests (parse_options, messages_to_dicts, split_reasoning, parse_tool_calls) that exist for mlx and mlx-vlm.
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class TestSharedHelpers(unittest.TestCase):
"""Server-less unit tests for the helpers the mlx-distributed backend depends on."""
def test_parse_options_typed(self):
opts = parse_options(["temperature:0.7", "max_tokens:128", "trust:true"])
self.assertEqual(opts["temperature"], 0.7)
self.assertEqual(opts["max_tokens"], 128)
self.assertIs(opts["trust"], True)
def test_messages_to_dicts_roundtrip(self):
msgs = [
backend_pb2.Message(role="user", content="hi"),
backend_pb2.Message(
role="assistant",
content="",
tool_calls='[{"id":"call_1","type":"function","function":{"name":"f","arguments":"{}"}}]',
),
backend_pb2.Message(role="tool", content="42", tool_call_id="call_1", name="f"),
]
out = messages_to_dicts(msgs)
self.assertEqual(out[0], {"role": "user", "content": "hi"})
self.assertEqual(out[1]["tool_calls"][0]["function"]["name"], "f")
self.assertEqual(out[2]["tool_call_id"], "call_1")
def test_split_reasoning(self):
r, c = split_reasoning("<think>plan</think>final", "<think>", "</think>")
self.assertEqual(r, "plan")
self.assertEqual(c, "final")
def test_parse_tool_calls_with_shim(self):
tm = types.SimpleNamespace(
tool_call_start="<tool_call>",
tool_call_end="</tool_call>",
parse_tool_call=lambda body, tools: {"name": "get_weather", "arguments": {"location": body.strip()}},
)
calls, remaining = parse_tool_calls("<tool_call>Paris</tool_call>", tm, tools=None)
self.assertEqual(len(calls), 1)
self.assertEqual(calls[0]["name"], "get_weather")
self.assertEqual(calls[0]["arguments"], '{"location": "Paris"}')