a932294627* mlx fixes * Fix studio integration, local dataset files, chat templates without the torch gpu imports * pass grad norm in mlx worker * fix(studio): pass MLX grad clipping settings * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * mlx: update grad value * fix(mlx): address ci and clipping review * fix backward compatibility and CI tests * unsloth local is mlx function * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * dont reference runtime * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci * studio mlx: hardcode value clipping, drop max_grad_value from frontend Simplifies the MLX grad-clipping plumbing now that we are standardising on elementwise value clipping at [-5, 5] for the compiled MLX path and norm clipping disabled. The MLX worker no longer reads max_grad_norm / max_grad_value from the request; both are pinned in one place. Frontend stops sending the field at all, and the TypeScript request type drops it to match. Non-MLX (CUDA/AMD/Intel) is untouched and continues to pick up HF TrainingArguments' default max_grad_norm = 1.0. --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com> Co-authored-by: Daniel Han <danielhanchen@gmail.com>
237 lines
8.7 KiB
Python
237 lines
8.7 KiB
Python
# SPDX-License-Identifier: AGPL-3.0-only
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"""
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Regression tests for the CUDA-vs-MLX dispatch gates Studio relies on.
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Two gates drive every dispatch decision in Studio's MLX path:
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1. ``unsloth._IS_MLX`` at the top of ``unsloth/__init__.py`` -- evaluated
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once at import time and read by Studio worker code to choose between
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the GPU and MLX trainer / inference / export paths. It delegates to
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the shared zoo MLX runtime gate, with a local import barrier while the
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paired unsloth-zoo runtime rollout is in flight.
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2. ``utils.hardware.detect_hardware()`` -- runtime probe in the Studio
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backend. Priority order: CUDA -> XPU -> MLX -> CPU. The MLX branch is
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reached only when both CUDA and XPU are unavailable AND the host is
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Apple Silicon AND mlx is importable.
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These gates are the canaries for "MLX support accidentally hijacks
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CUDA/AMD/Intel users". The tests here:
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* verify the source-level structure of the ``_IS_MLX`` helper so an
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accidental rewrite importing zoo before the local MLX precheck is caught,
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* exercise the runtime gate logic under a spoofed Darwin+arm64 platform
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with a fake ``mlx`` module in ``sys.modules`` to confirm both gates
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flip True together,
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* confirm that on the actual Linux+CUDA test host both gates remain in
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their CUDA-side state.
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No real MLX install is required; uses the same ``monkeypatch.setitem``
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fake-mlx pattern as ``test_mlx_inference_backend.py``.
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"""
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import ast
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import importlib
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import sys
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import types
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from pathlib import Path
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REPO_ROOT = Path(__file__).resolve().parents[2]
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UNSLOTH_INIT = REPO_ROOT / "unsloth" / "__init__.py"
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# ---------------------------------------------------------------------------
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# 1. Source-level structure check on _IS_MLX (no platform dependencies).
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# ---------------------------------------------------------------------------
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def test_is_mlx_gate_uses_three_required_predicates():
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"""The _IS_MLX assignment must AND together exactly the three checks
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that Studio depends on: Darwin OS, arm64 machine, and an importable
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mlx package. Dropping any one of them silently breaks dispatch.
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"""
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tree = ast.parse(UNSLOTH_INIT.read_text())
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target = None
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for node in ast.walk(tree):
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if (
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isinstance(node, ast.Assign)
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and len(node.targets) == 1
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and isinstance(node.targets[0], ast.Name)
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and node.targets[0].id == "_IS_MLX"
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):
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target = node.value
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break
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assert target is not None, "_IS_MLX assignment not found in unsloth/__init__.py"
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assert isinstance(target, ast.Call), "_IS_MLX must call the shared MLX helper"
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expr_src = ast.unparse(target)
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assert (
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expr_src == "_is_mlx_available()"
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), "_IS_MLX must delegate to the shared MLX runtime gate"
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helper = None
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for node in ast.walk(tree):
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if isinstance(node, ast.FunctionDef) and node.name == "_is_mlx_available":
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helper = node
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break
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assert helper is not None, "_is_mlx_available helper not found"
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helper_src = ast.unparse(helper)
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assert (
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"platform.system()" in helper_src
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and "'Darwin'" in helper_src
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and "platform.machine()" in helper_src
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and "'arm64'" in helper_src
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and "find_spec" in helper_src
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and "'mlx'" in helper_src
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and "from unsloth_zoo.mlx import is_mlx_available" in helper_src
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), "_IS_MLX helper must precheck local MLX predicates before importing zoo"
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assert (
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"from unsloth_zoo.mlx import is_mlx_available" in helper_src
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and "return is_mlx_available()" in helper_src
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), "_IS_MLX helper must delegate final detection to the shared zoo MLX runtime gate"
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assert helper_src.index("UNSLOTH_FORCE_GPU_PATH") < helper_src.index(
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"from unsloth_zoo.mlx import is_mlx_available"
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), "_IS_MLX helper must run the local MLX precheck before importing zoo"
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# ---------------------------------------------------------------------------
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# 2. Runtime gate behavior with the platform spoofed to Apple Silicon and a
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# fake mlx module in sys.modules. Re-evaluates the same expression
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# rather than reloading unsloth (which would cascade-reload torch).
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# ---------------------------------------------------------------------------
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def _evaluate_is_mlx_precheck(platform_module, importlib_util, os_module):
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"""Re-evaluate the local _is_mlx_available precheck using injected dependencies.
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Mirrors only the cheap import barrier before unsloth imports unsloth_zoo.
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"""
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return (
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os_module.environ.get("UNSLOTH_FORCE_GPU_PATH", "0") != "1"
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and platform_module.system() == "Darwin"
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and platform_module.machine() == "arm64"
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and importlib_util.find_spec("mlx") is not None
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)
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def test_is_mlx_gate_true_on_apple_silicon_with_mlx_present(monkeypatch):
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import platform
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import importlib.util
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# Inject a fake mlx package so find_spec returns a non-None ModuleSpec.
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fake_mlx = types.ModuleType("mlx")
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fake_mlx.__spec__ = importlib.machinery.ModuleSpec("mlx", loader = None)
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fake_mlx.__path__ = []
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monkeypatch.setitem(sys.modules, "mlx", fake_mlx)
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monkeypatch.setattr(platform, "system", lambda: "Darwin")
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monkeypatch.setattr(platform, "machine", lambda: "arm64")
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import os
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assert _evaluate_is_mlx_precheck(platform, importlib.util, os) is True
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def test_is_mlx_gate_false_when_mlx_missing(monkeypatch):
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import platform
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import importlib.util
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# Apple Silicon platform but no mlx package -> gate must be False.
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monkeypatch.delitem(sys.modules, "mlx", raising = False)
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monkeypatch.setattr(platform, "system", lambda: "Darwin")
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monkeypatch.setattr(platform, "machine", lambda: "arm64")
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real_find_spec = importlib.util.find_spec
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def _no_mlx(name, *args, **kwargs):
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if name == "mlx":
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return None
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return real_find_spec(name, *args, **kwargs)
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monkeypatch.setattr(importlib.util, "find_spec", _no_mlx)
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import os
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assert _evaluate_is_mlx_precheck(platform, importlib.util, os) is False
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def test_is_mlx_gate_false_on_non_apple_silicon():
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"""On the real Linux+CUDA / AMD / Intel test host, the gate stays False."""
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import platform
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import importlib.util
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if platform.system() == "Darwin" and platform.machine() == "arm64":
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# On a Mac CI runner this assertion would not apply; skip there.
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import pytest
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pytest.skip("Test host is Apple Silicon; CUDA-side canary doesn't apply.")
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import os
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assert _evaluate_is_mlx_precheck(platform, importlib.util, os) is False
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# ---------------------------------------------------------------------------
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# 3. Studio's runtime detect_hardware() picks MLX only when CUDA + XPU are
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# both unavailable AND the host is Apple Silicon AND mlx is importable.
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# ---------------------------------------------------------------------------
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def _import_studio_hardware():
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"""Lazy import for the Studio hardware module, with the bare-imports
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convention that Studio uses (studio/backend on sys.path).
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"""
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studio_backend = REPO_ROOT / "studio" / "backend"
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if str(studio_backend) not in sys.path:
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sys.path.insert(0, str(studio_backend))
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from utils.hardware import hardware as hw # type: ignore
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return hw
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def test_detect_hardware_picks_mlx_when_only_apple_silicon_available(monkeypatch):
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hw = _import_studio_hardware()
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# Force CUDA + XPU paths off so detect_hardware falls through to MLX.
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import torch
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monkeypatch.setattr(torch.cuda, "is_available", lambda: False)
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if hasattr(torch, "xpu"):
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monkeypatch.setattr(torch.xpu, "is_available", lambda: False)
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# Spoof Apple Silicon and provide an importable mlx.core for _has_mlx().
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import platform
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monkeypatch.setattr(platform, "system", lambda: "Darwin")
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monkeypatch.setattr(platform, "machine", lambda: "arm64")
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fake_mlx = types.ModuleType("mlx")
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fake_mlx_core = types.ModuleType("mlx.core")
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fake_mlx.core = fake_mlx_core
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monkeypatch.setitem(sys.modules, "mlx", fake_mlx)
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monkeypatch.setitem(sys.modules, "mlx.core", fake_mlx_core)
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detected = hw.detect_hardware()
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assert detected == hw.DeviceType.MLX, f"expected MLX, got {detected!r}"
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def test_detect_hardware_picks_cuda_on_real_host():
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"""Canary: on a real CUDA host the MLX branch must NOT be taken even
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if mlx happens to be importable. Protects CUDA/AMD/Intel users from
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accidental MLX dispatch when MLX support is added.
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"""
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import torch
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if not torch.cuda.is_available():
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import pytest
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pytest.skip("No CUDA available on this host; canary not applicable.")
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hw = _import_studio_hardware()
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detected = hw.detect_hardware()
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assert (
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detected == hw.DeviceType.CUDA
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), f"CUDA host must dispatch to CUDA, got {detected!r}"
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