From 43b62c003e5ad7756b1395d011212e2448e5eecb Mon Sep 17 00:00:00 2001 From: ccckblaze Date: Mon, 23 Mar 2026 20:47:53 +0800 Subject: [PATCH] 1. Expert Double-Buffered 2. Remove KV cache, it makes context confusion in some condition 3. Allow specific model directly from PATH --- .python-version | 2 +- AGENTS.md | 83 ++++ README.md | 48 +- docs/server.md | 2 +- mlx_moe/cli.py | 3 - mlx_moe/lazy_experts/core.py | 24 + mlx_moe/lazy_experts/generate.py | 93 +++- mlx_moe/lazy_experts/modules.py | 172 ++++++- mlx_moe/server.py | 102 ++-- pyproject.toml | 6 +- uv.lock | 767 +++++++++++++++++-------------- 11 files changed, 854 insertions(+), 448 deletions(-) diff --git a/.python-version b/.python-version index 24ee5b1..e4fba21 100644 --- a/.python-version +++ b/.python-version @@ -1 +1 @@ -3.13 +3.12 diff --git a/AGENTS.md b/AGENTS.md index 20164a9..419e7e4 100644 --- a/AGENTS.md +++ b/AGENTS.md @@ -214,3 +214,86 @@ Limits: - No defensive checks for impossible states. - No silent fallbacks that hide failures. - Remove dead code paths cleanly; no compatibility shims for deleted behavior. + +### Python Style Guidelines + +**Imports** (ordered by PEP 8 with custom groupings): +1. Standard library (`asyncio`, `json`, `pathlib`, `re`, `time`, `uuid`) +2. Third-party packages (`mlx`, `mlx.nn`, `mlx_lm`, `numpy`, `pytest`, `starlette`, `uvicorn`) +3. Local imports (`from mlx_moe.lazy_experts import ...`) + +Use absolute imports for the package (`from mlx_moe.lazy_experts.modules import ...`), not relative (`from .modules import ...`). + +**Formatting**: +- Maximum line length: 100 characters +- Use 4 spaces for indentation (no tabs) +- Use hanging indents for long function signatures +- Put imports in a single block (no multiple `import` statements scattered) + +**Types**: +- Use Python 3.12+ type hints: `def foo(x: int) -> str:` +- Use `X | None` instead of `Optional[X]` +- Use `dict[str, int]` instead of `Dict[str, int]` +- For internal/private functions, type hints are optional but encouraged +- Use `Path` from `pathlib` for file paths + +**Naming Conventions**: +- `snake_case` for functions, methods, and variables +- `SCREAMING_SNAKE_CASE` for constants +- `PascalCase` for classes +- Leading underscore (`_func`) for private functions +- Double leading underscore (`__method`) for name mangling (use sparingly) +- Suffix `_cb` for callback functions +- Suffix `_map` for dict-based mappings +- Prefix `num_` or `n_` for counts + +**Error Handling**: +- Raise specific exceptions with clear messages +- Do not catch bare `Exception` unless re-raising or logging +- Use `assert` for internal invariants, not for runtime validation +- Fail fast with descriptive errors, not silent fallbacks + +**MLX-Specific**: +- Never call `mx.eval()` in the forward pass of predictive modules (critical for performance) +- Use `mx.stop_gradient()` where needed to prevent unwanted gradient flow +- Prefer in-place operations when possible to reduce memory allocation +- Remember MLX GPU eval is not thread-safe; serialization in server is intentional + +## Testing + +**Run all tests**: +```bash +uv run pytest +``` + +**Run a single test file**: +```bash +uv run pytest tests/test_unit_core.py +``` + +**Run a single test class**: +```bash +uv run pytest tests/test_unit_core.py::TestExpertCache +``` + +**Run a single test function**: +```bash +uv run pytest tests/test_unit_core.py::TestExpertCache::test_put_and_lookup -v +``` + +**Run tests matching a pattern**: +```bash +uv run pytest -k "test_lcp" +``` + +**Run with output capture disabled** (see print statements): +```bash +uv run pytest -s +``` + +**Run integration tests only**: +```bash +uv run pytest tests/test_integration.py +``` + +Note: Some tests in `test_unit_core.py` use synthetic mocks and don't require model files. Integration tests may require a real model to be available. diff --git a/README.md b/README.md index f746df6..3fc666f 100644 --- a/README.md +++ b/README.md @@ -227,25 +227,45 @@ session.close() # release model For full control over the loading pipeline: -```python +```bash +uv run python -c " import mlx.core as mx import mlx_lm -from mlx_lm.utils import hf_repo_to_path -from mlx_moe.lazy_experts import ( - enable_lazy_experts, upgrade_to_predictive, get_fallback_stats -) - -model, tokenizer = mlx_lm.load("mlx-community/Qwen3-Coder-Next-4bit", lazy=True) -model_path = hf_repo_to_path("mlx-community/Qwen3-Coder-Next-4bit") - +from mlx_moe.lazy_experts import enable_lazy_experts, upgrade_to_predictive, get_fallback_stats +from mlx_moe.lazy_experts.loading import _find_switch_mlp +from mlx_moe.lazy_experts.discovery import router_only_discovery +from mlx_moe.lazy_experts.core import dynamic_cache_update, enable_skip_fallback +model_path = '/Users/steven/.lmstudio/models/lmstudio-community/Qwen3-Coder-Next-MLX-4bit' +print('Loading model...') +model, tokenizer = mlx_lm.load(model_path, lazy=True) +print('Model loaded') +print('Enable cached capacity=208...') enable_lazy_experts(model, model_path, cache_capacity_per_layer=208, predictive=True) mx.eval(model.parameters()) - -mlx_lm.generate(model, tokenizer, prompt="Hello", max_tokens=10, verbose=False) +switch, _ = _find_switch_mlp(model.layers[0], 0) +print('Module type:', type(switch.gate_proj).__name__) +print('=== Router-only discovery (does NOT load experts) ===') +router_only_discovery(model, tokenizer, 'git pull origin main', max_tokens=10) +print('Discovery done') +print('Upgrade to predictive...') upgrade_to_predictive(model, model_path, 208) - -output = mlx_lm.generate(model, tokenizer, prompt="Write a Flask server", - max_tokens=200, verbose=False) +switch, _ = _find_switch_mlp(model.layers[0], 0) +print('After upgrade:', type(switch.gate_proj).__name__) +print('Enable skip fallback...') +enable_skip_fallback(model) +print('=== Hybrid warmup (10 tokens) ===') +for resp in mlx_lm.stream_generate(model, tokenizer, prompt='git pull origin main', max_tokens=10): + pass +dynamic_cache_update(model, max_layer_updates=48) +print('Hybrid done') +print() +print('=== First generate ===') +output = mlx_lm.generate(model, tokenizer, prompt='git pull origin main', max_tokens=50, verbose=False) +print('Output:', repr(output[:150])) +stats = get_fallback_stats(model) +rate = stats['fallback_rate'] * 100 +print('Fallback rate:', rate, '%') +" ``` ## Testing diff --git a/docs/server.md b/docs/server.md index fbf8f97..e901986 100644 --- a/docs/server.md +++ b/docs/server.md @@ -50,7 +50,7 @@ flowchart TD G --> F F --> H["stream generation"] H --> I{"completed successfully?"} - I -- yes --> J["store (prompt_tokens + generated, prompt_cache) in keyed LRU"] + I -- yes --> J["store (prompt_tokens, prompt_cache) in keyed LRU"] I -- no --> K["do not restore cache entry"] ``` diff --git a/mlx_moe/cli.py b/mlx_moe/cli.py index e8280c0..825764b 100644 --- a/mlx_moe/cli.py +++ b/mlx_moe/cli.py @@ -21,8 +21,6 @@ def main(): help="Max input tokens — rejects requests over this (default: 16384)") serve.add_argument("--kv-bits", type=int, default=None, help="Quantize KV cache to N bits (8 recommended). Saves ~45%% KV memory.") - serve.add_argument("--kv-cache-slots", type=int, default=1, - help="Number of keyed KV cache entries to keep (default: 1).") serve.add_argument("--shutdown-timeout", type=int, default=5, help="Graceful shutdown timeout in seconds before cancelling active requests (default: 5).") serve.add_argument("--warmup", choices=["hybrid", "full", "none"], default="hybrid", @@ -44,7 +42,6 @@ def main(): max_input_tokens=args.max_input_tokens, kv_bits=args.kv_bits, warmup=args.warmup, - kv_cache_slots=args.kv_cache_slots, shutdown_timeout=args.shutdown_timeout, ) else: diff --git a/mlx_moe/lazy_experts/core.py b/mlx_moe/lazy_experts/core.py index 6c70623..ca33129 100644 --- a/mlx_moe/lazy_experts/core.py +++ b/mlx_moe/lazy_experts/core.py @@ -637,6 +637,30 @@ def dynamic_cache_update(model, max_layer_updates: int = 12) -> list[dict]: layer_stats["layer"] = i stats.append(layer_stats) + + # Prefetch: trigger next layer prefetch based on current layer requests + # This runs after current layer updates to predict next layer needs + for i in range(len(model.layers) - 1): + curr_proj = getattr(model.layers[i].mlp.switch_mlp, "up_proj", None) + if not curr_proj or not isinstance(curr_proj, PredictiveCachedSwitchLinear): + continue + + next_layer = model.layers[i + 1] + next_switch, _ = _find_switch_mlp(next_layer, i + 1) + if next_switch is None: + continue + + next_proj = getattr(next_switch, "up_proj", None) + if not next_proj or not isinstance(next_proj, PredictiveCachedSwitchLinear): + continue + + # Use current layer requests to predict next layer + predicted_experts = curr_proj._cache.predict_next_experts() + if predicted_experts: + next_proj._cache.prefetch_async(predicted_experts) + # Check if we can swap buffers for next layer + next_proj._cache.check_prefetch_and_swap() + return stats diff --git a/mlx_moe/lazy_experts/generate.py b/mlx_moe/lazy_experts/generate.py index d9dd453..7ff50af 100644 --- a/mlx_moe/lazy_experts/generate.py +++ b/mlx_moe/lazy_experts/generate.py @@ -31,6 +31,29 @@ _WARMUP_CACHE = 256 * 1024 * 1024 +def _fix_group_size(model): + """Fix group_size mismatch by inferring from weight/scales shapes. + + Some models (e.g., Qwen3-Coder-Next-MLX-8bit) have incorrect group_size + in config.json. The actual group_size must be computed from tensor shapes. + """ + for layer in model.layers: + switch, _ = _find_switch_mlp(layer) + if switch is None: + continue + for name in ("gate_proj", "up_proj", "down_proj"): + proj = getattr(switch, name, None) + if proj is None or not hasattr(proj, "weight") or not hasattr(proj, "scales"): + continue + # Infer actual group_size from shapes + # weight shape: (num_experts, output_dim, input_dim) + # scales shape: (num_experts, output_dim, num_groups) + # group_size = input_dim / num_groups + actual = proj.weight.shape[-1] // proj.scales.shape[-1] + if actual != proj.group_size: + proj.group_size = actual + + def _startup( model_name, prompt, @@ -54,12 +77,66 @@ def _startup( from .core import upgrade_to_predictive import mlx_lm as _mlx_lm from mlx_lm.utils import hf_repo_to_path + from huggingface_hub.errors import HFValidationError t_total_start = time.perf_counter() - model_path = hf_repo_to_path(model_name) + # First check if the path exists at all + if os.path.exists(model_name): + if not os.path.isdir(model_name): + raise FileNotFoundError( + f"Model path exists but is not a directory: {model_name}" + ) + model_path = Path(model_name) + elif "/" not in model_name: + # Local path without slash - try as relative path + raise FileNotFoundError( + f"Model path does not exist: {model_name}\n" + f"Use a valid local path or HuggingFace model ID (e.g., mlx-community/Qwen3-Coder-Next-4bit)" + ) + else: + # HuggingFace model ID - verify it looks like one (has namespace/repo format) + # and doesn't look like a file path + if model_name.startswith("/") or model_name.startswith("."): + raise FileNotFoundError( + f"Model path does not exist: {model_name}\n" + f"Use a valid local path or HuggingFace model ID (e.g., mlx-community/Qwen3-Coder-Next-4bit)" + ) + try: + model_path = hf_repo_to_path(model_name) + except HFValidationError: + raise FileNotFoundError( + f"Invalid model ID or model not found: {model_name}\n" + f"HuggingFace model IDs should be in the format 'namespace/repo_name'" + ) + except Exception as e: + raise FileNotFoundError( + f"Failed to download model: {model_name}\n{str(e)}" + ) + + if not model_path.is_dir(): + raise FileNotFoundError(f"Model path does not exist: {model_path}") + + config_path = model_path / "config.json" + if not config_path.is_file(): + safetensors_files = list(model_path.glob("*.safetensors")) + if not safetensors_files: + raise FileNotFoundError( + f"Model directory is empty or missing model files: {model_path}\n" + f"Please download the model first." + ) + else: + raise FileNotFoundError( + f"config.json not found in: {model_path}\n" + f"This may not be a valid HuggingFace-compatible model directory." + ) + t0 = time.perf_counter() - model, tokenizer = _mlx_lm.load(model_name, lazy=True) + model, tokenizer = _mlx_lm.load(str(model_path), lazy=True) + + # Fix group_size mismatch: config.json may declare wrong group_size + # Actual group_size must be inferred from weight/scales shapes + # _fix_group_size(model) # Detect MoE architecture num_moe_layers = 0 @@ -120,9 +197,17 @@ def _startup( if cache_dir is not None: cache_dir = os.path.expanduser(cache_dir) os.makedirs(cache_dir, exist_ok=True) - safe_name = model_name.replace("/", "--") - cache_path = os.path.join(cache_dir, f"{safe_name}.json") + model_path_str = str(model_path).rstrip("/") + safe_name_base = model_path_str.replace("/", "--").replace("\\", "--") + + cache_path = os.path.join(cache_dir, f"{safe_name_base}.json") prepacked_path = cache_path.replace(".json", ".weights.safetensors") + meta_path = prepacked_path + ".meta.json" + + if not os.path.exists(meta_path): + safe_name_alt = safe_name_base + "--" + cache_path = os.path.join(cache_dir, f"{safe_name_alt}.json") + prepacked_path = cache_path.replace(".json", ".weights.safetensors") used_saved_state = False mx.reset_peak_memory() diff --git a/mlx_moe/lazy_experts/modules.py b/mlx_moe/lazy_experts/modules.py index 72f2c91..690e487 100644 --- a/mlx_moe/lazy_experts/modules.py +++ b/mlx_moe/lazy_experts/modules.py @@ -243,7 +243,14 @@ class PredictiveExpertCache: '_shard_paths', '_key_prefixes', '_shard_map', '_st_map', 'total_requests', 'total_fallbacks', - 'pinned_set') + 'pinned_set', + # Double buffering: Buffer A and Buffer B + 'weights_A', 'scales_A', 'biases_A', 'cached_ids_A', + 'weights_B', 'scales_B', 'biases_B', 'cached_ids_B', + '_active_buffer', + # Prefetch state + '_prefetch_buffer', '_pending_prefetch', + '_prefetch_in_progress') def __init__(self, capacity: int, num_experts: int = 512): self.capacity = capacity @@ -266,6 +273,20 @@ def __init__(self, capacity: int, num_experts: int = 512): self.total_requests: int = 0 self.total_fallbacks: int = 0 self.pinned_set: set[int] = set() + # Double buffering: Buffer A and Buffer B + self.weights_A: dict[str, mx.array] = {} + self.scales_A: dict[str, mx.array] = {} + self.biases_A: dict[str, mx.array | None] = {} + self.cached_ids_A: list[int] = [] # Expert IDs in Buffer A + self.weights_B: dict[str, mx.array] = {} + self.scales_B: dict[str, mx.array] = {} + self.biases_B: dict[str, mx.array | None] = {} + self.cached_ids_B: list[int] = [] # Expert IDs in Buffer B + self._active_buffer = 'A' + # Prefetch state + self._prefetch_buffer = 'B' # Target buffer for prefetch + self._pending_prefetch: list[int] = [] + self._prefetch_in_progress = False def build_lookup(self, cached_ids: list[int]) -> None: """Build GPU-resident lookup table and hit mask from cached expert IDs. @@ -294,6 +315,137 @@ def remap(self, indices: mx.array) -> mx.array: """Map global expert IDs to cache slots. Pure mx.array op, no eval.""" return self.lookup[indices] + def get_weights(self, proj_name: str) -> mx.array: + """Get weights from current active buffer.""" + if self._active_buffer == 'A': + return self.weights_A.get(proj_name) or self.weights.get(proj_name) + return self.weights_B.get(proj_name) or self.weights.get(proj_name) + + def get_scales(self, proj_name: str) -> mx.array: + """Get scales from current active buffer.""" + if self._active_buffer == 'A': + return self.scales_A.get(proj_name) or self.scales.get(proj_name) + return self.scales_B.get(proj_name) or self.scales.get(proj_name) + + def get_biases(self, proj_name: str) -> mx.array | None: + """Get biases from current active buffer.""" + if self._active_buffer == 'A': + return self.biases_A.get(proj_name) if proj_name in self.biases_A else self.biases.get(proj_name) + return self.biases_B.get(proj_name) if proj_name in self.biases_B else self.biases.get(proj_name) + + def swap_buffers(self) -> None: + """Swap active buffer (A <-> B).""" + self._active_buffer = 'B' if self._active_buffer == 'A' else 'A' + + def predict_next_experts(self) -> list[int]: + """Predict next layer experts using current layer requests.""" + if not self._indices_buffer: + return [] + recent = self._indices_buffer[-1] + flat = np.asarray(recent.reshape(-1)) + return [int(x) for x in np.unique(flat)] + + def prefetch_async(self, expert_ids: list[int]) -> None: + """Initiate prefetch of experts to prefetch buffer. + + This loads experts into the inactive buffer (Buffer B if active is A). + The data is loaded synchronously but happens during the token gap, + potentially overlapping with GPU computation. + """ + if self._prefetch_in_progress: + return # Already prefetching + + # Filter to experts not in current active buffer + current_buffer_set = set() + if self._active_buffer == 'A': + for proj in ('gate_proj', 'up_proj', 'down_proj'): + if proj in self.weights_A: + current_buffer_set.update(self.cached_ids[:len(self.weights_A)]) + else: + for proj in ('gate_proj', 'up_proj', 'down_proj'): + if proj in self.weights_B: + current_buffer_set.update(self.cached_ids[:len(self.weights_B)]) + + missing = [eid for eid in expert_ids if eid not in self.cached_set] + if not missing or not self._shard_paths: + return + + # Limit prefetch count + MAX_PREFETCH = 8 + prefetch_ids = missing[:MAX_PREFETCH] + + # Determine target buffer + target_weights = self.weights_A if self._prefetch_buffer == 'A' else self.weights_B + target_scales = self.scales_A if self._prefetch_buffer == 'A' else self.scales_B + target_biases = self.biases_A if self._prefetch_buffer == 'A' else self.biases_B + + # If target buffer is empty, we need to allocate slots + if not target_weights: + # Use slot indices 0 to len(prefetch_ids)-1 for prefetch + slot_indices = list(range(len(prefetch_ids))) + new_eids = mx.array(prefetch_ids) + + for proj_name in ('gate_proj', 'up_proj', 'down_proj'): + if proj_name not in self._shard_paths: + continue + + shard_path = self._shard_paths[proj_name] + key_prefix = self._key_prefixes[proj_name] + shard = mx.load(shard_path) if self._st_map is None else None + new_w, new_s, new_b = _load_experts( + key_prefix, new_eids, + shard=shard, shard_map=self._shard_map, + st_map=self._st_map + ) + del shard + + if new_b is None: + mx.eval(new_w, new_s) + else: + mx.eval(new_w, new_s, new_b) + + target_weights[proj_name] = new_w + target_scales[proj_name] = new_s + if new_b is not None: + target_biases[proj_name] = new_b + + # Update cached_ids for prefetch buffer + if self._prefetch_buffer == 'A': + self.weights_A = target_weights + self.scales_A = target_scales + self.biases_A = target_biases + # Update cached_ids_A tracking + self.cached_ids_A = list(prefetch_ids) + else: + self.weights_B = target_weights + self.scales_B = target_scales + self.biases_B = target_biases + self.cached_ids_B = list(prefetch_ids) + + self._prefetch_in_progress = True + self._pending_prefetch = prefetch_ids + + def check_prefetch_and_swap(self) -> bool: + """Check if prefetch completed and swap buffers if ready. + + Returns True if buffers were swapped. + """ + if not self._prefetch_in_progress or not self._pending_prefetch: + return False + + # Check if pending experts are loaded in prefetch buffer + prefetch_target = self._prefetch_buffer + weights = self.weights_A if prefetch_target == 'A' else self.weights_B + + # Simple check: if prefetch buffer has any data, consider it ready + if weights: + self.swap_buffers() + self._prefetch_in_progress = False + self._pending_prefetch = [] + return True + + return False + def _lcp_priority(self, eid: int) -> float: mu = self.frequency.get(eid, 0) nu = self.step - self.last_active.get(eid, 0) @@ -428,11 +580,12 @@ def __call__(self, x, indices, sorted_indices=False): self._cache._indices_buffer.append(indices) local_indices = self._cache.remap(indices) # Remap breaks sorted order — always pass sorted_indices=False + # Use double buffer methods + w = self._cache.get_weights(self._proj_name) + s = self._cache.get_scales(self._proj_name) + b = self._cache.get_biases(self._proj_name) return mx.gather_qmm( - x, - self._cache.weights[self._proj_name], - self._cache.scales[self._proj_name], - self._cache.biases[self._proj_name], + x, w, s, b, rhs_indices=local_indices, transpose=True, group_size=self.group_size, @@ -465,11 +618,12 @@ def __call__(self, x, indices, sorted_indices=False): mx.eval(indices) local_indices = self._cache.remap(indices) # Remap breaks sorted order — always pass sorted_indices=False + # Use double buffer methods + w = self._cache.get_weights(self._proj_name) + s = self._cache.get_scales(self._proj_name) + b = self._cache.get_biases(self._proj_name) return mx.gather_qmm( - x, - self._cache.weights[self._proj_name], - self._cache.scales[self._proj_name], - self._cache.biases[self._proj_name], + x, w, s, b, rhs_indices=local_indices, transpose=True, group_size=self.group_size, diff --git a/mlx_moe/server.py b/mlx_moe/server.py index 10837f7..1477df8 100644 --- a/mlx_moe/server.py +++ b/mlx_moe/server.py @@ -111,7 +111,25 @@ def _convert_tools_anthropic_to_openai(tools: list) -> list: } -def _sampling_defaults(model_name: str) -> dict: +def _sampling_defaults(model_name: str, model_path: Path | None = None) -> dict: + if model_path is not None: + config_path = model_path / "generation_config.json" + if config_path.is_file(): + try: + with open(config_path) as f: + config = json.load(f) + defaults = {} + if "temperature" in config: + defaults["temp"] = config["temperature"] + if "top_p" in config: + defaults["top_p"] = config["top_p"] + if "top_k" in config: + defaults["top_k"] = config["top_k"] + if defaults: + return defaults + except (json.JSONDecodeError, OSError): + pass + slug = model_name.split("/")[-1].lower() for prefix, defaults in MODEL_SAMPLING_DEFAULTS.items(): if prefix in slug: @@ -119,8 +137,8 @@ def _sampling_defaults(model_name: str) -> dict: return {} -def _sampling_kwargs(body: dict, model_name: str) -> dict: - defaults = _sampling_defaults(model_name) +def _sampling_kwargs(body: dict, model_name: str, model_path: Path | None = None) -> dict: + defaults = _sampling_defaults(model_name, model_path) if "temperature" in body: defaults["temp"] = float(body["temperature"]) if "top_p" in body: @@ -185,27 +203,25 @@ def __init__(self, model_name: str, capacity: int | None = None, max_tokens: int = MAX_TOKENS_CAP, max_input_tokens: int = MAX_INPUT_TOKENS, kv_bits: int | None = None, - warmup: str = "hybrid", - kv_cache_slots: int = 1): + warmup: str = "hybrid"): self._model_name = model_name self._capacity = capacity self._profile_path = profile_path or _find_profile(model_name) self._pin_top_k = pin_top_k self._model = None self._tokenizer = None + self._model_path = None self._lock = asyncio.Lock() self._model_id = model_name.split("/")[-1] self._max_tokens = max_tokens self._max_input_tokens = max_input_tokens self._kv_bits = kv_bits self._warmup = warmup - self._kv_cache_slots = max(1, kv_cache_slots) - self._kv_cache = OrderedDict() def load(self): from .lazy_experts.generate import _startup warmup_prompt = "Write a Python function that implements binary search on a sorted array." - model, tokenizer, _ = _startup( + model, tokenizer, model_path = _startup( self._model_name, warmup_prompt, cache_dir=str(Path.home() / ".cache" / "mlx-moe"), profile_path=self._profile_path, @@ -215,6 +231,7 @@ def load(self): ) self._model = model self._tokenizer = tokenizer + self._model_path = model_path self._memory_gb = mx.get_active_memory() / 1e9 if self._warmup == "hybrid": self._run_startup_refinement() @@ -299,12 +316,6 @@ def _cache_key_from_body(self, body: dict) -> str: key = str(body["user"]) return str(key) if key is not None else "default" - def _put_kv_cache(self, cache_key: str, tokens: list[int], kv_cache) -> None: - self._kv_cache[cache_key] = (tokens, kv_cache) - self._kv_cache.move_to_end(cache_key) - while len(self._kv_cache) > self._kv_cache_slots: - self._kv_cache.popitem(last=False) - @staticmethod def _dynamic_update_policy( generation_tokens: int, @@ -343,38 +354,11 @@ def _stream(self, prompt_tokens: list[int], max_tokens: int, cache_key: str, **sampling): import mlx_lm as _mlx_lm from mlx_lm.sample_utils import make_sampler - from mlx_lm.models import cache as cache_module from .lazy_experts.core import dynamic_cache_update - # Find longest common prefix with cached KV from previous request + # No KV cache - always generate from scratch + suffix = prompt_tokens prompt_cache = None - prefix_len = 0 - cached = self._kv_cache.get(cache_key) - cached_tokens = None - cached_kv = None - if cached is not None: - cached_tokens, cached_kv = cached - self._kv_cache.move_to_end(cache_key) - min_len = min(len(cached_tokens), len(prompt_tokens)) - while prefix_len < min_len and cached_tokens[prefix_len] == prompt_tokens[prefix_len]: - prefix_len += 1 - - if prefix_len > 0: - # generate_step requires at least 1 input token - if prefix_len >= len(prompt_tokens): - prefix_len = len(prompt_tokens) - 1 - trim_amount = len(cached_tokens) - prefix_len - if trim_amount > 0: - cache_module.trim_prompt_cache(cached_kv, trim_amount) - prompt_cache = cached_kv - suffix = prompt_tokens[prefix_len:] - print(f" [kv cache: reusing {prefix_len}, processing {len(suffix)} new tokens]") - else: - suffix = prompt_tokens - prompt_cache = cache_module.make_prompt_cache(self._model) - - # Invalidate cache before generation — restored on successful completion - self._kv_cache.pop(cache_key, None) kv_kwargs = {} if self._kv_bits is not None: @@ -397,8 +381,10 @@ def _stream(self, prompt_tokens: list[int], max_tokens: int, complete = False try: + # When suffix is empty (exact cache match), don't pass prompt to avoid empty array error + prompt_arg = mx.array(suffix) if suffix else None for resp in _mlx_lm.stream_generate( - self._model, self._tokenizer, prompt=mx.array(suffix), + self._model, self._tokenizer, prompt=prompt_arg, max_tokens=max_tokens, sampler=make_sampler(**sampling), prompt_cache=prompt_cache, **kv_kwargs, ): @@ -445,9 +431,6 @@ def _stream(self, prompt_tokens: list[int], max_tokens: int, complete = True finally: - if complete: - self._put_kv_cache(cache_key, prompt_tokens + generated_tokens, prompt_cache) - if first_token_at is not None: prefill_ms = (prompt_len / prompt_tps * 1000.0) if prompt_tps > 0 else 0.0 ttft_ms = (first_token_at - request_t0) * 1000.0 @@ -505,7 +488,7 @@ async def handle_chat_completions(self, request: Request): max_tokens = min(raw if raw is not None else self._max_tokens, self._max_tokens) stream = body.get("stream", False) tools = body.get("tools") - sampling = _sampling_kwargs(body, self._model_name) + sampling = _sampling_kwargs(body, self._model_name, self._model_path) cache_key = self._cache_key_from_body(body) formatted = _format_messages(messages) @@ -559,7 +542,7 @@ async def handle_messages(self, request: Request): ) max_tokens = min(body.get("max_tokens", self._max_tokens), self._max_tokens) stream = body.get("stream", False) - sampling = _sampling_kwargs(body, self._model_name) + sampling = _sampling_kwargs(body, self._model_name, self._model_path) cache_key = self._cache_key_from_body(body) tools_anthropic = body.get("tools") @@ -939,19 +922,17 @@ async def _stream_anthropic(self, prompt_tokens: list[int], max_tokens: int, def run_server(model_name: str, host: str = "127.0.0.1", port: int = 8080, capacity: int | None = None, profile_path: str | None = None, pin_top_k: int | None = None, - max_tokens: int = MAX_TOKENS_CAP, - max_input_tokens: int = MAX_INPUT_TOKENS, - kv_bits: int | None = None, - warmup: str = "hybrid", - kv_cache_slots: int = 1, - shutdown_timeout: int = DEFAULT_SHUTDOWN_TIMEOUT): + max_tokens: int = MAX_TOKENS_CAP, + max_input_tokens: int = MAX_INPUT_TOKENS, + kv_bits: int | None = None, + warmup: str = "hybrid", + shutdown_timeout: int = DEFAULT_SHUTDOWN_TIMEOUT): import uvicorn server = Server(model_name, capacity=capacity, profile_path=profile_path, pin_top_k=pin_top_k, max_tokens=max_tokens, max_input_tokens=max_input_tokens, - kv_bits=kv_bits, warmup=warmup, - kv_cache_slots=kv_cache_slots) + kv_bits=kv_bits, warmup=warmup) print(f"mlx-moe serve") print(f" Model: {model_name}") @@ -959,15 +940,14 @@ def run_server(model_name: str, host: str = "127.0.0.1", port: int = 8080, if pin_top_k is not None: print(f" Pin top-k: {pin_top_k}") print(f" Endpoint: http://{host}:{port}") - defaults = _sampling_defaults(model_name) print(f" Limits: {max_input_tokens} input, {max_tokens} output") - print(f" KV cache: {kv_cache_slots} slot(s)") print(f" Shutdown: {shutdown_timeout}s graceful timeout") - if defaults: - print(f" Sampling: {defaults}") print() print("Loading model...") server.load() + defaults = _sampling_defaults(model_name, server._model_path) + if defaults: + print(f" Sampling: {defaults}") print(f" Memory: {server._memory_gb:.1f} GB") print(f" Ready.") print() diff --git a/pyproject.toml b/pyproject.toml index 0faef15..502be26 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -3,10 +3,10 @@ name = "mlx-moe" version = "0.1.0" description = "Run large MoE models on memory-constrained Macs via on-demand expert loading" readme = "README.md" -requires-python = ">=3.13" +requires-python = ">=3.12" dependencies = [ - "mlx>=0.22.0", - "mlx-lm>=0.30.0", + "mlx>=0.31.1", + "mlx-lm>=0.31.1", "numpy>=2.0.0", "starlette>=0.46.0", "uvicorn>=0.34.0", diff --git a/uv.lock b/uv.lock index b017e17..78ae2c5 100644 --- a/uv.lock +++ b/uv.lock @@ -1,6 +1,6 @@ version = 1 -revision = 3 -requires-python = ">=3.13" +revision = 1 +requires-python = ">=3.12" [[package]] name = "anyio" @@ -8,19 +8,20 @@ version = "4.12.1" source = { registry = "https://pypi.org/simple" } dependencies = [ { name = "idna" }, + { name = "typing-extensions", marker = 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