diff --git a/.cuda_typings/openai_harmony/__init__.pyi b/.cuda_typings/openai_harmony/__init__.pyi new file mode 100644 index 0000000000..ec5d67cc03 --- /dev/null +++ b/.cuda_typings/openai_harmony/__init__.pyi @@ -0,0 +1,20 @@ +from enum import Enum + +class HarmonyEncodingName(Enum): + HARMONY_GPT_OSS = ... + +class HarmonyEncoding: ... +class HarmonyError(Exception): ... + +class Role(Enum): + ASSISTANT = ... + +class StreamableParser: + last_content_delta: str + current_channel: str | None + current_recipient: str | None + + def __init__(self, encoding: HarmonyEncoding, role: Role = ...) -> None: ... + def process(self, token_id: int) -> None: ... + +def load_harmony_encoding(name: HarmonyEncodingName) -> HarmonyEncoding: ... diff --git a/.cuda_typings/pynvml/__init__.pyi b/.cuda_typings/pynvml/__init__.pyi new file mode 100644 index 0000000000..b63c35bb69 --- /dev/null +++ b/.cuda_typings/pynvml/__init__.pyi @@ -0,0 +1,17 @@ +class NvmlMemoryInfo: + used: int + total: int + free: int + +class NvmlUtilizationRates: + gpu: int + memory: int + +def nvmlInit() -> None: ... +def nvmlShutdown() -> None: ... +def nvmlDeviceGetCount() -> int: ... +def nvmlDeviceGetHandleByIndex(index: int) -> object: ... +def nvmlDeviceGetUtilizationRates(handle: object) -> NvmlUtilizationRates: ... +def nvmlDeviceGetTemperature(handle: object, sensor_type: int) -> int: ... +def nvmlDeviceGetPowerUsage(handle: object) -> int: ... +def nvmlDeviceGetMemoryInfo(handle: object) -> NvmlMemoryInfo: ... diff --git a/.cuda_typings/torch/__init__.pyi b/.cuda_typings/torch/__init__.pyi new file mode 100644 index 0000000000..70525dc521 --- /dev/null +++ b/.cuda_typings/torch/__init__.pyi @@ -0,0 +1,61 @@ +from typing import Any, Sequence + +from torch import backends as backends +from torch import cuda as cuda +from torch import distributed as distributed + +__version__: str + +class version: + cuda: str + +class dtype: ... + +bfloat16: dtype +float16: dtype +float32: dtype +int8: dtype +int32: dtype +int64: dtype +long: dtype +float8_e4m3fn: dtype + +class Tensor: + shape: Sequence[int] + dtype: dtype + def __getitem__(self, key: Any) -> Tensor: ... + def __setitem__(self, key: Any, value: Any) -> None: ... + def to(self, *args: Any, **kwargs: Any) -> Tensor: ... + def cpu(self) -> Tensor: ... + def detach(self) -> Tensor: ... + def clone(self) -> Tensor: ... + def flatten(self, start_dim: int = 0, end_dim: int = -1) -> Tensor: ... + def view(self, *shape: Any) -> Tensor: ... + def squeeze(self, dim: int = ...) -> Tensor: ... + def unsqueeze(self, dim: int) -> Tensor: ... + def permute(self, *dims: int) -> Tensor: ... + def float(self) -> Tensor: ... + def numpy(self) -> Any: ... + def numel(self) -> int: ... + def nelement(self) -> int: ... + @property + def is_cuda(self) -> bool: ... + @property + def device(self) -> device: ... + def __len__(self) -> int: ... + def data_ptr(self) -> int: ... + def tolist(self) -> Any: ... + def abs(self) -> Tensor: ... + def max(self) -> Tensor: ... + def mean(self) -> Tensor: ... + def sum(self, dim: int = ...) -> Tensor: ... + def item(self) -> float: ... + +def tensor(data: Any, dtype: dtype | None = None, device: Any = None) -> Tensor: ... +def zeros(*size: Any, dtype: dtype | None = None, device: Any = None) -> Tensor: ... +def empty(*size: Any, dtype: dtype | None = None, device: Any = None) -> Tensor: ... +def from_numpy(ndarray: Any) -> Tensor: ... +def inference_mode() -> Any: ... + +class device: + def __init__(self, type: str, index: int = ...) -> None: ... diff --git a/.cuda_typings/torch/backends/__init__.pyi b/.cuda_typings/torch/backends/__init__.pyi new file mode 100644 index 0000000000..a3f9d6095c --- /dev/null +++ b/.cuda_typings/torch/backends/__init__.pyi @@ -0,0 +1 @@ +from torch.backends import cuda as cuda diff --git a/.cuda_typings/torch/backends/cuda/__init__.pyi b/.cuda_typings/torch/backends/cuda/__init__.pyi new file mode 100644 index 0000000000..89e2839b3e --- /dev/null +++ b/.cuda_typings/torch/backends/cuda/__init__.pyi @@ -0,0 +1 @@ +def is_built() -> bool: ... diff --git a/.cuda_typings/torch/cuda/__init__.pyi b/.cuda_typings/torch/cuda/__init__.pyi new file mode 100644 index 0000000000..a7d471017b --- /dev/null +++ b/.cuda_typings/torch/cuda/__init__.pyi @@ -0,0 +1,10 @@ +class _DeviceProperties: + total_memory: int + +def is_available() -> bool: ... +def get_device_name(device: int) -> str: ... +def get_device_properties(device: int) -> _DeviceProperties: ... +def empty_cache() -> None: ... +def mem_get_info() -> tuple[int, int]: ... +def synchronize() -> None: ... +def max_memory_allocated() -> int: ... diff --git a/.cuda_typings/torch/distributed/__init__.pyi b/.cuda_typings/torch/distributed/__init__.pyi new file mode 100644 index 0000000000..4d35303464 --- /dev/null +++ b/.cuda_typings/torch/distributed/__init__.pyi @@ -0,0 +1,2 @@ +def is_initialized() -> bool: ... +def destroy_process_group() -> None: ... diff --git a/.cuda_typings/vllm/__init__.pyi b/.cuda_typings/vllm/__init__.pyi new file mode 100644 index 0000000000..bda5b5a7f4 --- /dev/null +++ b/.cuda_typings/vllm/__init__.pyi @@ -0,0 +1 @@ +__version__: str diff --git a/.cuda_typings/vllm/config.pyi b/.cuda_typings/vllm/config.pyi new file mode 100644 index 0000000000..97f53ceb16 --- /dev/null +++ b/.cuda_typings/vllm/config.pyi @@ -0,0 +1,2 @@ +class ModelConfig: + max_model_len: int diff --git a/.cuda_typings/vllm/engine/__init__.pyi b/.cuda_typings/vllm/engine/__init__.pyi new file mode 100644 index 0000000000..e69de29bb2 diff --git a/.cuda_typings/vllm/engine/arg_utils.pyi b/.cuda_typings/vllm/engine/arg_utils.pyi new file mode 100644 index 0000000000..ff14df4506 --- /dev/null +++ b/.cuda_typings/vllm/engine/arg_utils.pyi @@ -0,0 +1,18 @@ +from dataclasses import dataclass + +@dataclass +class EngineArgs: + model: str = ... + served_model_name: str | list[str] | None = ... + tokenizer: str | None = ... + trust_remote_code: bool = ... + dtype: str = ... + seed: int = ... + max_model_len: int | None = ... + gpu_memory_utilization: float = ... + enforce_eager: bool = ... + tensor_parallel_size: int = ... + pipeline_parallel_size: int = ... + quantization: str | None = ... + load_format: str = ... + enable_sleep_mode: bool = ... diff --git a/.cuda_typings/vllm/outputs.pyi b/.cuda_typings/vllm/outputs.pyi new file mode 100644 index 0000000000..3ea71343ce --- /dev/null +++ b/.cuda_typings/vllm/outputs.pyi @@ -0,0 +1,17 @@ +class CompletionOutput: + index: int + text: str + token_ids: list[int] + cumulative_logprob: float | None + logprobs: object | None + finish_reason: str | None + stop_reason: int | str | None + + def finished(self) -> bool: ... + +class RequestOutput: + request_id: str + prompt: str | None + prompt_token_ids: list[int] | None + outputs: list[CompletionOutput] + finished: bool diff --git a/.cuda_typings/vllm/sampling_params.pyi b/.cuda_typings/vllm/sampling_params.pyi new file mode 100644 index 0000000000..e2a08ea418 --- /dev/null +++ b/.cuda_typings/vllm/sampling_params.pyi @@ -0,0 +1,11 @@ +class SamplingParams: + n: int + temperature: float + top_p: float + top_k: int + min_p: float + seed: int | None + stop: str | list[str] | None + max_tokens: int | None + logprobs: int | None + repetition_penalty: float diff --git a/.cuda_typings/vllm/tokenizers/__init__.pyi b/.cuda_typings/vllm/tokenizers/__init__.pyi new file mode 100644 index 0000000000..3619246f2c --- /dev/null +++ b/.cuda_typings/vllm/tokenizers/__init__.pyi @@ -0,0 +1,3 @@ +from vllm.tokenizers.protocol import TokenizerLike + +__all__ = ["TokenizerLike"] diff --git a/.cuda_typings/vllm/tokenizers/protocol.pyi b/.cuda_typings/vllm/tokenizers/protocol.pyi new file mode 100644 index 0000000000..1464c88daa --- /dev/null +++ b/.cuda_typings/vllm/tokenizers/protocol.pyi @@ -0,0 +1,15 @@ +from typing import Protocol + +class TokenizerLike(Protocol): + @property + def eos_token_id(self) -> int: ... + @property + def vocab_size(self) -> int: ... + def encode(self, text: str, add_special_tokens: bool = ...) -> list[int]: ... + def decode(self, ids: list[int] | int, skip_special_tokens: bool = ...) -> str: ... + def apply_chat_template( + self, + messages: list[dict[str, str]], + tools: list[dict[str, object]] | None = ..., + **kwargs: object, + ) -> str | list[int]: ... diff --git a/.cuda_typings/vllm/v1/__init__.pyi b/.cuda_typings/vllm/v1/__init__.pyi new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/.cuda_typings/vllm/v1/__init__.pyi @@ -0,0 +1 @@ + diff --git a/.cuda_typings/vllm/v1/core/__init__.pyi b/.cuda_typings/vllm/v1/core/__init__.pyi new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/.cuda_typings/vllm/v1/core/__init__.pyi @@ -0,0 +1 @@ + diff --git a/.cuda_typings/vllm/v1/core/kv_cache_manager.pyi b/.cuda_typings/vllm/v1/core/kv_cache_manager.pyi new file mode 100644 index 0000000000..a36a944b89 --- /dev/null +++ b/.cuda_typings/vllm/v1/core/kv_cache_manager.pyi @@ -0,0 +1,24 @@ +from collections.abc import Sequence + +from vllm.v1.core.kv_cache_utils import BlockPool, KVCacheBlock +from vllm.v1.kv_cache_interface import KVCacheConfig + +class KVCacheBlocks: + blocks: tuple[Sequence[KVCacheBlock], ...] + def __init__(self, blocks: tuple[Sequence[KVCacheBlock], ...]) -> None: ... + def get_block_ids(self) -> tuple[list[int], ...]: ... + +class KVCacheManager: + block_pool: BlockPool + kv_cache_config: KVCacheConfig + enable_caching: bool + num_kv_cache_groups: int + coordinator: object + def __init__(self, *args: object, **kwargs: object) -> None: ... + def allocate_slots( + self, request: object, num_new_tokens: int, *args: object, **kwargs: object + ) -> KVCacheBlocks | None: ... + def get_computed_blocks(self, request: object) -> tuple[KVCacheBlocks, int]: ... + def create_kv_cache_blocks( + self, blocks: tuple[list[KVCacheBlock], ...] + ) -> KVCacheBlocks: ... diff --git a/.cuda_typings/vllm/v1/core/kv_cache_utils.pyi b/.cuda_typings/vllm/v1/core/kv_cache_utils.pyi new file mode 100644 index 0000000000..f6b0607fe0 --- /dev/null +++ b/.cuda_typings/vllm/v1/core/kv_cache_utils.pyi @@ -0,0 +1,16 @@ +class KVCacheBlock: + block_id: int + ref_cnt: int + def __init__(self, block_id: int) -> None: ... + +class FreeKVCacheBlockQueue: + def append_n(self, blocks: list[KVCacheBlock]) -> None: ... + def popleft_n(self, n: int) -> list[KVCacheBlock]: ... + +class BlockPool: + blocks: list[KVCacheBlock] + free_block_queue: FreeKVCacheBlockQueue + num_gpu_blocks: int + enable_caching: bool + def get_num_free_blocks(self) -> int: ... + def get_new_blocks(self, num_blocks: int) -> list[KVCacheBlock]: ... diff --git a/.cuda_typings/vllm/v1/engine/__init__.pyi b/.cuda_typings/vllm/v1/engine/__init__.pyi new file mode 100644 index 0000000000..e69de29bb2 diff --git a/.cuda_typings/vllm/v1/engine/llm_engine.pyi b/.cuda_typings/vllm/v1/engine/llm_engine.pyi new file mode 100644 index 0000000000..5c1a6b1902 --- /dev/null +++ b/.cuda_typings/vllm/v1/engine/llm_engine.pyi @@ -0,0 +1,22 @@ +from vllm.config import ModelConfig +from vllm.engine.arg_utils import EngineArgs +from vllm.outputs import RequestOutput +from vllm.sampling_params import SamplingParams +from vllm.tokenizers import TokenizerLike + +class LLMEngine: + tokenizer: TokenizerLike | None + model_config: ModelConfig + + @classmethod + def from_engine_args(cls, engine_args: EngineArgs) -> LLMEngine: ... + def add_request( + self, + request_id: str, + prompt: str, + params: SamplingParams, + arrival_time: float | None = ..., + ) -> None: ... + def step(self) -> list[RequestOutput]: ... + def has_unfinished_requests(self) -> bool: ... + def get_tokenizer(self) -> TokenizerLike: ... diff --git a/.cuda_typings/vllm/v1/kv_cache_interface.pyi b/.cuda_typings/vllm/v1/kv_cache_interface.pyi new file mode 100644 index 0000000000..32b101d866 --- /dev/null +++ b/.cuda_typings/vllm/v1/kv_cache_interface.pyi @@ -0,0 +1,23 @@ +from dataclasses import dataclass + +@dataclass +class KVCacheSpec: + block_size: int + num_kv_heads: int + head_size: int + +@dataclass +class KVCacheGroupSpec: + layer_names: list[str] + kv_cache_spec: KVCacheSpec + +@dataclass +class KVCacheTensorSpec: + shared_by: list[str] + size: int + +@dataclass +class KVCacheConfig: + num_blocks: int + kv_cache_groups: list[KVCacheGroupSpec] + kv_cache_tensors: list[KVCacheTensorSpec] diff --git a/.cuda_typings/vllm/v1/request.pyi b/.cuda_typings/vllm/v1/request.pyi new file mode 100644 index 0000000000..3e1e91cde9 --- /dev/null +++ b/.cuda_typings/vllm/v1/request.pyi @@ -0,0 +1,6 @@ +class Request: + request_id: str + prompt_token_ids: list[int] | None + num_prompt_tokens: int + num_computed_tokens: int + num_tokens: int diff --git a/.cuda_typings/vllm/v1/worker/__init__.pyi b/.cuda_typings/vllm/v1/worker/__init__.pyi new file mode 100644 index 0000000000..8b13789179 --- /dev/null +++ b/.cuda_typings/vllm/v1/worker/__init__.pyi @@ -0,0 +1 @@ + diff --git a/.cuda_typings/vllm/v1/worker/gpu_model_runner.pyi b/.cuda_typings/vllm/v1/worker/gpu_model_runner.pyi new file mode 100644 index 0000000000..515983f4a9 --- /dev/null +++ b/.cuda_typings/vllm/v1/worker/gpu_model_runner.pyi @@ -0,0 +1,24 @@ +import torch + +class _CompilationConfig: + static_forward_context: dict[str, object] + +class _ModelConfig: + hf_config: object + +class GPUModelRunner: + kv_caches: list[torch.Tensor] + compilation_config: _CompilationConfig + model_config: _ModelConfig | None + def _allocate_kv_cache_tensors( + self, kv_cache_config: object + ) -> dict[str, torch.Tensor]: ... + def initialize_kv_cache_tensors( + self, kv_cache_config: object, kernel_block_sizes: list[int] + ) -> dict[str, torch.Tensor]: ... + def _reshape_kv_cache_tensors( + self, + kv_cache_config: object, + raw_tensors: dict[str, torch.Tensor], + kernel_block_sizes: list[int], + ) -> dict[str, torch.Tensor]: ... diff --git a/.cuda_typings/vllm/v1/worker/gpu_worker.pyi b/.cuda_typings/vllm/v1/worker/gpu_worker.pyi new file mode 100644 index 0000000000..57781a74d5 --- /dev/null +++ b/.cuda_typings/vllm/v1/worker/gpu_worker.pyi @@ -0,0 +1,6 @@ +from vllm.v1.worker.gpu_model_runner import GPUModelRunner + +class Worker: + model_runner: GPUModelRunner + def determine_available_memory(self) -> int: ... + def initialize_from_config(self, kv_cache_config: object) -> None: ... diff --git a/.cuda_typings/vllm/v1/worker/utils.pyi b/.cuda_typings/vllm/v1/worker/utils.pyi new file mode 100644 index 0000000000..4c1c45bf12 --- /dev/null +++ b/.cuda_typings/vllm/v1/worker/utils.pyi @@ -0,0 +1 @@ +def extract_layer_index(layer_name: str, num_attn_module: int) -> int: ... diff --git a/bench/eval_tool_calls.py b/bench/eval_tool_calls.py index 13cb2537ef..8dbebe81ed 100644 --- a/bench/eval_tool_calls.py +++ b/bench/eval_tool_calls.py @@ -17,11 +17,13 @@ ExoClient, ExoHttpError, add_common_instance_args, + ensure_cuda_available, instance_id_from_instance, nodes_used_in_instance, resolve_model_short_id, run_planning_phase, settle_and_fetch_placements, + validate_vllm_args, wait_for_instance_gone, wait_for_instance_ready, ) @@ -933,6 +935,7 @@ def main() -> None: help="Write JSON results to stdout instead of file", ) args = parser.parse_args() + validate_vllm_args(args) all_scenarios = load_scenarios(SCENARIOS_PATH) if args.scenarios: @@ -952,6 +955,8 @@ def main() -> None: log = sys.stderr if args.stdout else sys.stdout exo = ExoClient(args.host, args.port, timeout_s=args.timeout) + if args.ensure_cuda: + ensure_cuda_available(exo) _short_id, full_model_id = resolve_model_short_id(exo, args.model) selected = settle_and_fetch_placements( diff --git a/bench/exo_bench.py b/bench/exo_bench.py index 20b0a5460d..26bf6c4733 100644 --- a/bench/exo_bench.py +++ b/bench/exo_bench.py @@ -33,11 +33,13 @@ ExoClient, ExoHttpError, add_common_instance_args, + ensure_cuda_available, instance_id_from_instance, nodes_used_in_instance, resolve_model_short_id, run_planning_phase, settle_and_fetch_placements, + validate_vllm_args, wait_for_instance_gone, wait_for_instance_ready, ) @@ -280,6 +282,7 @@ def main() -> int: help="Force all pp×tg combinations (cartesian product) even when lists have equal length.", ) args = ap.parse_args() + validate_vllm_args(args) pp_list = parse_int_list(args.pp) tg_list = parse_int_list(args.tg) @@ -304,6 +307,8 @@ def main() -> int: logger.info(f"pp/tg mode: tandem (zip) - {len(pp_list)} pairs") client = ExoClient(args.host, args.port, timeout_s=args.timeout) + if args.ensure_cuda: + ensure_cuda_available(client) short_id, full_model_id = resolve_model_short_id( client, args.model, force_download=args.force_download ) diff --git a/bench/exo_eval.py b/bench/exo_eval.py index e0770e75e8..8cab8f560d 100644 --- a/bench/exo_eval.py +++ b/bench/exo_eval.py @@ -46,11 +46,13 @@ ExoClient, ExoHttpError, add_common_instance_args, + ensure_cuda_available, instance_id_from_instance, nodes_used_in_instance, resolve_model_short_id, run_planning_phase, settle_and_fetch_placements, + validate_vllm_args, wait_for_instance_gone, wait_for_instance_ready, ) @@ -1157,6 +1159,7 @@ def main() -> int: ) args, _ = ap.parse_known_args() + validate_vllm_args(args) # Resolve tasks if args.tasks: @@ -1173,6 +1176,8 @@ def main() -> int: # Instance management client = ExoClient(args.host, args.port, timeout_s=args.timeout) + if args.ensure_cuda: + ensure_cuda_available(client) instance_id: str | None = None if not args.skip_instance_setup: diff --git a/bench/harness.py b/bench/harness.py index 366de660a0..573cf99d36 100644 --- a/bench/harness.py +++ b/bench/harness.py @@ -196,6 +196,31 @@ def resolve_model_short_id( raise ValueError(f"Model not found in /models: {model_arg}") +def validate_vllm_args(args: argparse.Namespace) -> None: + if args.instance_meta != "vllm": + return + if args.sharding == "tensor": + raise SystemExit( + "--instance-meta vllm is incompatible with --sharding tensor (vllm is pipeline-only)" + ) + if args.min_nodes > 1: + raise SystemExit( + "--instance-meta vllm is incompatible with --min-nodes > 1 (vllm is single-node)" + ) + if args.max_nodes > 1: + raise SystemExit( + "--instance-meta vllm is incompatible with --max-nodes > 1 (vllm is single-node)" + ) + + +def ensure_cuda_available(client: ExoClient) -> None: + capabilities = client.request_json("GET", "/capabilities") + if not capabilities or not capabilities.get("vllm_available"): + raise SystemExit( + "--ensure-cuda: vllm is not available on the exo cluster (no CUDA capability)" + ) + + def placement_filter(instance_meta: str, wanted: str) -> bool: s = (instance_meta or "").lower() if wanted == "both": @@ -475,7 +500,7 @@ def add_common_instance_args(ap: argparse.ArgumentParser) -> None: help="Only consider placements using >= this many nodes.", ) ap.add_argument( - "--instance-meta", choices=["ring", "jaccl", "both"], default="both" + "--instance-meta", choices=["ring", "jaccl", "vllm", "both"], default="both" ) ap.add_argument( "--sharding", choices=["pipeline", "tensor", "both"], default="both" @@ -504,3 +529,8 @@ def add_common_instance_args(ap: argparse.ArgumentParser) -> None: action="store_true", help="Delete existing models from smallest to largest to make room for benchmark model.", ) + ap.add_argument( + "--ensure-cuda", + action="store_true", + help="Verify the exo cluster has CUDA/vllm capability; error if not.", + ) diff --git a/bench/test_mlx_bandwidth.py b/bench/test_mlx_bandwidth.py new file mode 100644 index 0000000000..6b165b8b00 --- /dev/null +++ b/bench/test_mlx_bandwidth.py @@ -0,0 +1,377 @@ +# type: ignore +import argparse +import json +import os +import statistics +import sys +import tempfile +import time + +import mlx.core as mx + +DTYPE_MAP = { + "float32": (mx.float32, 4), + "float16": (mx.float16, 2), + "bfloat16": (mx.bfloat16, 2), +} + +SIZES = [ + 1 * 1024, + 4 * 1024, + 16 * 1024, + 64 * 1024, + 256 * 1024, + 1 * 1024 * 1024, + 4 * 1024 * 1024, + 16 * 1024 * 1024, + 64 * 1024 * 1024, + 256 * 1024 * 1024, + 1 * 1024 * 1024 * 1024, + 2 * 1024 * 1024 * 1024, + 4 * 1024 * 1024 * 1024, + 8 * 1024 * 1024 * 1024, +] + + +def format_bytes(n: int) -> str: + if n >= 1024 * 1024 * 1024: + return f"{n / (1024 * 1024 * 1024):.0f} GB" + if n >= 1024 * 1024: + return f"{n / (1024 * 1024):.0f} MB" + if n >= 1024: + return f"{n / 1024:.0f} KB" + return f"{n} B" + + +def format_time(seconds: float) -> str: + if seconds >= 1.0: + return f"{seconds:.3f} s" + if seconds >= 0.001: + return f"{seconds * 1000:.2f} ms" + return f"{seconds * 1_000_000:.1f} us" + + +def format_bandwidth(bytes_per_sec: float) -> str: + if bytes_per_sec >= 1024 * 1024 * 1024: + return f"{bytes_per_sec / (1024 * 1024 * 1024):.2f} GB/s" + if bytes_per_sec >= 1024 * 1024: + return f"{bytes_per_sec / (1024 * 1024):.1f} MB/s" + return f"{bytes_per_sec / 1024:.1f} KB/s" + + +def barrier(group: mx.distributed.Group) -> None: + mx.eval(mx.distributed.all_sum(mx.array(1.0), group=group)) + + +def init_ring( + rank: int, self_ip: str, peer_ip: str, port: int, tmpdir: str +) -> mx.distributed.Group: + if rank == 0: + hosts = [f"{self_ip}:{port}", f"{peer_ip}:{port}"] + else: + hosts = [f"{peer_ip}:{port}", f"{self_ip}:{port}"] + + hostfile = os.path.join(tmpdir, "hosts.json") + with open(hostfile, "w") as f: + json.dump(hosts, f) + + for var in ("MLX_HOSTFILE", "MLX_RANK", "MLX_IBV_DEVICES", "MLX_JACCL_COORDINATOR"): + os.environ.pop(var, None) + + os.environ["MLX_HOSTFILE"] = hostfile + os.environ["MLX_RANK"] = str(rank) + return mx.distributed.init(backend="ring", strict=True) + + +def init_jaccl( + rank: int, interface: str, coordinator: str, port: int, tmpdir: str +) -> mx.distributed.Group: + devices = [[None, interface], [interface, None]] + devfile = os.path.join(tmpdir, "devices.json") + with open(devfile, "w") as f: + json.dump(devices, f) + + for var in ("MLX_HOSTFILE", "MLX_RANK", "MLX_IBV_DEVICES", "MLX_JACCL_COORDINATOR"): + os.environ.pop(var, None) + + os.environ["MLX_IBV_DEVICES"] = devfile + os.environ["MLX_RANK"] = str(rank) + if rank == 0: + os.environ["MLX_JACCL_COORDINATOR"] = f"0.0.0.0:{port}" + else: + os.environ["MLX_JACCL_COORDINATOR"] = coordinator + + return mx.distributed.init(backend="jaccl", strict=True) + + +def bench_unidirectional( + group: mx.distributed.Group, + rank: int, + size_bytes: int, + dtype: mx.Dtype, + element_size: int, + warmup: int, + iterations: int, +) -> list[float]: + n_elements = size_bytes // element_size + tensor = mx.random.normal(shape=(n_elements,)).astype(dtype) + mx.eval(tensor) + + for _ in range(warmup): + if rank == 0: + sent = mx.distributed.send(tensor, dst=1, group=group) + mx.eval(sent) + else: + received = mx.distributed.recv_like(tensor, src=0, group=group) + mx.eval(received) + barrier(group) + + times: list[float] = [] + for _ in range(iterations): + barrier(group) + t0 = time.perf_counter() + if rank == 0: + sent = mx.distributed.send(tensor, dst=1, group=group) + mx.eval(sent) + else: + received = mx.distributed.recv_like(tensor, src=0, group=group) + mx.eval(received) + barrier(group) + t1 = time.perf_counter() + times.append(t1 - t0) + + return times + + +def bench_rtt( + group: mx.distributed.Group, + rank: int, + size_bytes: int, + dtype: mx.Dtype, + element_size: int, + warmup: int, + iterations: int, +) -> list[float]: + n_elements = size_bytes // element_size + tensor = mx.random.normal(shape=(n_elements,)).astype(dtype) + mx.eval(tensor) + + for _ in range(warmup): + if rank == 0: + sent = mx.distributed.send(tensor, dst=1, group=group) + mx.eval(sent) + received = mx.distributed.recv_like(tensor, src=1, group=group) + mx.eval(received) + else: + received = mx.distributed.recv_like(tensor, src=0, group=group) + mx.eval(received) + sent = mx.distributed.send(received, dst=0, group=group) + mx.eval(sent) + barrier(group) + + times: list[float] = [] + for _ in range(iterations): + barrier(group) + t0 = time.perf_counter() + if rank == 0: + sent = mx.distributed.send(tensor, dst=1, group=group) + mx.eval(sent) + received = mx.distributed.recv_like(tensor, src=1, group=group) + mx.eval(received) + else: + received = mx.distributed.recv_like(tensor, src=0, group=group) + mx.eval(received) + sent = mx.distributed.send(received, dst=0, group=group) + mx.eval(sent) + barrier(group) + t1 = time.perf_counter() + times.append(t1 - t0) + + return times + + +def bench_all_gather( + group: mx.distributed.Group, + rank: int, + size_bytes: int, + dtype: mx.Dtype, + element_size: int, + warmup: int, + iterations: int, +) -> list[float]: + n_elements = (size_bytes // 2) // element_size + tensor = mx.random.normal(shape=(n_elements,)).astype(dtype) + mx.eval(tensor) + + for _ in range(warmup): + gathered = mx.distributed.all_gather(tensor, group=group) + mx.eval(gathered) + barrier(group) + + times: list[float] = [] + for _ in range(iterations): + barrier(group) + t0 = time.perf_counter() + gathered = mx.distributed.all_gather(tensor, group=group) + mx.eval(gathered) + t1 = time.perf_counter() + times.append(t1 - t0) + + return times + + +def print_table(title: str, rows: list[dict[str, str]]) -> None: + print(f"\n=== {title} ===") + headers = ["Size", "Median", "Min", "Max", "Bandwidth"] + widths = [ + max(len(h), max((len(r[h]) for r in rows), default=0)) + 2 for h in headers + ] + header_line = "".join(h.ljust(w) for h, w in zip(headers, widths, strict=True)) + print(header_line) + print("-" * len(header_line)) + for row in rows: + print("".join(row[h].ljust(w) for h, w in zip(headers, widths, strict=True))) + + +def run_bench( + name: str, + bench_fn, + group: mx.distributed.Group, + rank: int, + dtype: mx.Dtype, + element_size: int, + warmup: int, + iterations: int, + bw_multiplier: int = 1, +) -> None: + rows: list[dict[str, str]] = [] + for size in SIZES: + if rank == 0: + print(f" {name}: {format_bytes(size)}...", end="", flush=True) + times = bench_fn(group, rank, size, dtype, element_size, warmup, iterations) + if rank == 0: + med = statistics.median(times) + mn = min(times) + mx_ = max(times) + bw = (size * bw_multiplier) / med + rows.append( + { + "Size": format_bytes(size), + "Median": format_time(med), + "Min": format_time(mn), + "Max": format_time(mx_), + "Bandwidth": format_bandwidth(bw), + } + ) + print(f" {format_bandwidth(bw)}") + if rank == 0: + print_table(name, rows) + + +def parse_args() -> argparse.Namespace: + parser = argparse.ArgumentParser( + description="MLX Distributed Communication Benchmark" + ) + subparsers = parser.add_subparsers(dest="backend", required=True) + + ring_parser = subparsers.add_parser("ring") + ring_parser.add_argument("--rank", type=int, required=True, choices=[0, 1]) + ring_parser.add_argument("--self-ip", required=True) + ring_parser.add_argument("--peer-ip", required=True) + ring_parser.add_argument("--port", type=int, default=5555) + + jaccl_parser = subparsers.add_parser("jaccl") + jaccl_parser.add_argument("--rank", type=int, required=True, choices=[0, 1]) + jaccl_parser.add_argument("--interface", required=True) + jaccl_parser.add_argument( + "--coordinator", + type=str, + default=None, + help="IP:PORT of rank 0 (required for rank 1)", + ) + jaccl_parser.add_argument( + "--port", type=int, default=9999, help="Coordinator port (rank 0 only)" + ) + + for p in [ring_parser, jaccl_parser]: + p.add_argument("--warmup", type=int, default=3) + p.add_argument("--iterations", type=int, default=10) + p.add_argument("--dtype", choices=list(DTYPE_MAP.keys()), default="float32") + + args = parser.parse_args() + + if args.backend == "jaccl" and args.rank == 1 and args.coordinator is None: + jaccl_parser.error("--coordinator is required for rank 1") + + return args + + +def main() -> int: + args = parse_args() + dtype, element_size = DTYPE_MAP[args.dtype] + + with tempfile.TemporaryDirectory() as tmpdir: + if args.backend == "ring": + print(f"Initializing ring backend (rank {args.rank})...") + group = init_ring(args.rank, args.self_ip, args.peer_ip, args.port, tmpdir) + else: + print(f"Initializing jaccl backend (rank {args.rank})...") + group = init_jaccl( + args.rank, args.interface, args.coordinator or "", args.port, tmpdir + ) + + print(f"Rank {group.rank()} of {group.size()} initialized") + barrier(group) + + if args.rank == 0: + print("\nMLX Distributed Communication Benchmark") + print( + f"Backend: {args.backend} | Dtype: {args.dtype} | Warmup: {args.warmup} | Iterations: {args.iterations}" + ) + + run_bench( + "Unidirectional (rank 0 -> rank 1)", + bench_unidirectional, + group, + args.rank, + dtype, + element_size, + args.warmup, + args.iterations, + ) + run_bench( + "Round-Trip (ping-pong)", + bench_rtt, + group, + args.rank, + dtype, + element_size, + args.warmup, + args.iterations, + bw_multiplier=2, + ) + run_bench( + "All-Gather", + bench_all_gather, + group, + args.rank, + dtype, + element_size, + args.warmup, + args.iterations, + ) + + if args.rank == 0: + print("\nDone.") + else: + print("Rank 1 complete.") + + return 0 + + +if __name__ == "__main__": + try: + sys.exit(main()) + except KeyboardInterrupt: + print("\nInterrupted.") + sys.exit(1) diff --git a/dashboard/src/lib/components/DeviceIcon.svelte b/dashboard/src/lib/components/DeviceIcon.svelte index 774be2f904..3893a1c3a2 100644 --- a/dashboard/src/lib/components/DeviceIcon.svelte +++ b/dashboard/src/lib/components/DeviceIcon.svelte @@ -9,7 +9,7 @@ */ interface Props { - /** "macbook pro" | "mac studio" | "mac mini" etc. */ + /** "macbook pro" | "mac studio" | "mac mini" | "dgx spark" | "linux" etc. */ deviceType: string; /** Center X coordinate in SVG space */ cx: number; @@ -38,10 +38,43 @@ const LOGO_NATIVE_WIDTH = 814; const LOGO_NATIVE_HEIGHT = 1000; + // NVIDIA logo SVG path + const NVIDIA_LOGO_PATH = + "M0.81 0.429V0.299c0.013 -0.001 0.026 -0.002 0.038 -0.002 0.355 -0.011 0.588 0.306 0.588 0.306S1.186 0.952 0.916 0.952c-0.036 0 -0.071 -0.006 -0.105 -0.017V0.542c0.138 0.017 0.166 0.078 0.249 0.216l0.185 -0.155s-0.135 -0.177 -0.362 -0.177c-0.024 -0.001 -0.048 0.001 -0.072 0.003m0 -0.429v0.194l0.038 -0.002c0.494 -0.017 0.816 0.405 0.816 0.405s-0.37 0.45 -0.754 0.45c-0.034 0 -0.066 -0.003 -0.099 -0.009v0.12c0.027 0.003 0.055 0.006 0.082 0.006 0.358 0 0.618 -0.183 0.869 -0.399 0.042 0.034 0.212 0.114 0.247 0.15 -0.238 0.2 -0.794 0.361 -1.11 0.361 -0.03 0 -0.059 -0.002 -0.088 -0.005v0.169h1.362V0zm0 0.935v0.102c-0.331 -0.059 -0.423 -0.404 -0.423 -0.404s0.159 -0.176 0.423 -0.205v0.112h-0.001C0.671 0.524 0.562 0.654 0.562 0.654s0.062 0.218 0.248 0.282m-0.588 -0.316s0.196 -0.29 0.589 -0.32V0.194C0.376 0.229 0 0.597 0 0.597s0.213 0.616 0.81 0.672v-0.112c-0.438 -0.054 -0.588 -0.538 -0.588 -0.538"; + const wireColor = "rgba(179,179,179,0.8)"; const strokeWidth = 1.5; const modelLower = $derived(deviceType.toLowerCase()); + const isSpark = $derived( + modelLower.includes("dgx") || modelLower.includes("gx10"), + ); + const isLinux = $derived(!isSpark && modelLower.startsWith("linux")); + const isLinuxLaptop = $derived(isLinux && modelLower.includes("laptop")); + + // ── DGX Spark dimensions ── + const dgxW = $derived(size * 1.55); + const dgxH = $derived(size * 0.58); + const dgxX = $derived(cx - dgxW / 2); + const dgxY = $derived(cy - dgxH / 2); + const dgxChassisX = $derived(dgxX - dgxW * 0.03); + const dgxChassisW = $derived(dgxW * 1.05); + const dgxHandleW = $derived(dgxW * 0.27); + const dgxHandleGap = $derived(dgxH * 0.05); + const dgxHandleH = $derived(dgxH - dgxHandleGap * 2); + const dgxHandleY = $derived(dgxY + dgxHandleGap); + const dgxInnerHandleW = $derived(dgxW * 0.12); + const dgxInnerHandleH = $derived(dgxHandleH - dgxH * 0.06); + const dgxLeftHandleX = $derived(dgxX + 4); + const dgxRightHandleX = $derived(dgxX + dgxW - dgxHandleW - 4); + const dgxClipId = $derived(`di-dgx-${uid}`); + const dgxTextureId = $derived(`di-dgx-tex-${uid}`); + + // ── Linux Desktop dimensions (reuses Mac Studio proportions) ── + const linuxDesktopClipId = $derived(`di-linux-desktop-${uid}`); + + // ── Linux Laptop dimensions (reuses MacBook proportions) ── + const linuxScreenClipId = $derived(`di-linux-screen-${uid}`); // ── Mac Studio dimensions (same ratios as TopologyGraph) ── const studioW = $derived(size * 1.25); @@ -114,7 +147,264 @@ const studioClipId = $derived(`di-studio-${uid}`); -{#if modelLower === "mac studio" || modelLower === "mac mini"} +{#if isSpark} + + + + + + + + + + + + + + + + + + + + + {#if ramPercent > 0} + + {/if} + + + + + + + + + + + {@const badgeW = dgxW * 0.09} + {@const badgeH = dgxHandleH * 0.5} + {@const badgeX = dgxLeftHandleX + dgxHandleW - badgeW - dgxHandleW * 0.06} + {@const badgeYPos = dgxHandleY + (dgxHandleH - badgeH) / 2} + {@const textSz = badgeW * 0.58} + {@const logoW = textSz * 1.2} + {@const logoH = logoW * (1.438 / 2.174)} + {@const ctrX = badgeX + badgeW / 2 - badgeW * 0.03} + {@const ctrY = badgeYPos + badgeH / 2} + {@const labelGap = badgeW * 0.15} + {@const totalW = logoW + labelGap + textSz * 3.6} + + + + + NVIDIA + +{:else if isLinuxLaptop} + + + + + + + + + + {#if ramPercent > 0} + + {/if} + + + {">_"} + + + + +{:else if isLinux} + + + + + + + + + {#if ramPercent > 0} + + {/if} + + + {">_"} +{:else if modelLower === "mac studio" || modelLower === "mac mini"} diff --git a/dashboard/src/lib/components/ModelCard.svelte b/dashboard/src/lib/components/ModelCard.svelte index b432b7a87b..9623d45ccf 100644 --- a/dashboard/src/lib/components/ModelCard.svelte +++ b/dashboard/src/lib/components/ModelCard.svelte @@ -169,8 +169,10 @@ function getDeviceType( name: string, - ): "macbook" | "studio" | "mini" | "unknown" { + ): "macbook" | "studio" | "mini" | "dgx" | "linux" | "unknown" { const lower = name.toLowerCase(); + if (lower.includes("dgx") || lower.includes("gx10")) return "dgx"; + if (lower.includes("linux")) return "linux"; if (lower.includes("macbook")) return "macbook"; if (lower.includes("studio")) return "studio"; if (lower.includes("mini")) return "mini"; @@ -278,7 +280,7 @@ let placementNodes: Array<{ id: string; deviceName: string; - deviceType: "macbook" | "studio" | "mini" | "unknown"; + deviceType: "macbook" | "studio" | "mini" | "dgx" | "linux" | "unknown"; totalGB: number; currentUsedGB: number; modelUsageGB: number; @@ -968,6 +970,137 @@ /> {/if} + {:else if node.deviceType === "dgx"} + + {@const s = node.iconSize} + {@const dgxW = s * 1.4} + {@const dgxH = s * 0.52} + + + + + + + + + + + + + {#if node.modelUsageGB > 0 && node.isUsed} + + {/if} + + {:else if node.deviceType === "linux"} + + {@const sz = node.iconSize} + {@const sc = sz / 100} + + + + + + + + + + + + + + + + {:else} 0) { + const memFillHeight = (ramUsagePercent / 100) * iconBaseHeight; + nodeG + .append("rect") + .attr("x", x) + .attr("y", y + iconBaseHeight - memFillHeight) + .attr("width", iconBaseWidth) + .attr("height", memFillHeight) + .attr("fill", "rgba(255,215,0,0.45)") + .attr("clip-path", `url(#${dgxClipId})`); + } + + // Side handles with inner recess + const handleWidth = iconBaseWidth * 0.27; + const handleGap = iconBaseHeight * 0.05; + const handleHeight = iconBaseHeight - handleGap * 2; + const handleY = y + handleGap; + const innerHandleWidth = iconBaseWidth * 0.12; + const innerHandleHeight = handleHeight - iconBaseHeight * 0.06; + const leftHandleX = x + 4; + const rightHandleX = x + iconBaseWidth - handleWidth - 4; + + // Left handle + nodeG + .append("rect") + .attr("x", leftHandleX) + .attr("y", handleY) + .attr("width", handleWidth) + .attr("height", handleHeight) + .attr("rx", 2.4) + .attr("fill", "#b3a170") + .attr("stroke", "#403723") + .attr("stroke-width", 0.7); + nodeG + .append("rect") + .attr("x", leftHandleX + handleWidth * 0.06) + .attr("y", handleY + iconBaseHeight * 0.03) + .attr("width", innerHandleWidth) + .attr("height", innerHandleHeight) + .attr("rx", 1.6) + .attr("fill", "#8a7a56"); + + // Right handle + nodeG + .append("rect") + .attr("x", rightHandleX) + .attr("y", handleY) + .attr("width", handleWidth) + .attr("height", handleHeight) + .attr("rx", 2.4) + .attr("fill", "#b3a170") + .attr("stroke", "#403723") + .attr("stroke-width", 0.7); + nodeG + .append("rect") + .attr( + "x", + rightHandleX + handleWidth - innerHandleWidth - handleWidth * 0.08, + ) + .attr("y", handleY + iconBaseHeight * 0.03) + .attr("width", innerHandleWidth) + .attr("height", innerHandleHeight) + .attr("rx", 1.6) + .attr("fill", "#8a7a56"); + + // NVIDIA logo + text label (rotated 90 deg on left handle) + const badgeWidth = iconBaseWidth * 0.09; + const badgeHeight = handleHeight * 0.5; + const badgeX = + leftHandleX + handleWidth - badgeWidth - handleWidth * 0.06; + const badgeY = handleY + (handleHeight - badgeHeight) / 2; + const textSize = badgeWidth * 0.58; + const logoWidth = textSize * 1.2; + const logoHeight = logoWidth * (1.438 / 2.174); + const centerX = badgeX + badgeWidth / 2 - badgeWidth * 0.03; + const centerY = badgeY + badgeHeight / 2; + const gap = badgeWidth * 0.15; + const totalWidth = logoWidth + gap + textSize * 3.6; + + const labelGroup = nodeG + .append("g") + .attr("transform", `rotate(90 ${centerX} ${centerY})`); + + labelGroup + .append("svg") + .attr("x", centerX - totalWidth / 2) + .attr("y", centerY - logoHeight / 2) + .attr("width", logoWidth) + .attr("height", logoHeight) + .attr("viewBox", "0 0 2.174 1.438") + .append("path") + .attr("d", NVIDIA_LOGO_PATH) + .attr("fill", "#76b900"); + + labelGroup + .append("text") + .attr("x", centerX - totalWidth / 2 + logoWidth + gap) + .attr("y", centerY) + .attr("text-anchor", "start") + .attr("dominant-baseline", "middle") + .attr("fill", "#8a7a56") + .attr("font-size", textSize) + .attr("font-family", "monospace") + .attr("font-weight", "700") + .text("NVIDIA"); + } else if (isLinuxLaptop) { + // Linux Laptop — same shape as MacBook but with Tux logo + iconBaseWidth = nodeRadius * 1.6; + iconBaseHeight = nodeRadius * 1.15; + const x = nodeInfo.x - iconBaseWidth / 2; + const y = nodeInfo.y - iconBaseHeight / 2; + + const screenHeight = iconBaseHeight * 0.7; + const baseHeight = iconBaseHeight * 0.3; + const screenWidth = iconBaseWidth * 0.85; + const screenX = nodeInfo.x - screenWidth / 2; + const screenBezel = 3; + + const linuxScreenClipId = `linux-screen-${nodeInfo.id.replace(/[^a-zA-Z0-9]/g, "-")}`; + defs + .append("clipPath") + .attr("id", linuxScreenClipId) + .append("rect") + .attr("x", screenX + screenBezel) + .attr("y", y + screenBezel) + .attr("width", screenWidth - screenBezel * 2) + .attr("height", screenHeight - screenBezel * 2) + .attr("rx", 2); + + // Screen outer frame + nodeG + .append("rect") + .attr("class", "node-outline") + .attr("x", screenX) + .attr("y", y) + .attr("width", screenWidth) + .attr("height", screenHeight) + .attr("rx", 3) + .attr("fill", "#1a1a1a") + .attr("stroke", wireColor) + .attr("stroke-width", strokeWidth); + + // Screen inner + nodeG + .append("rect") + .attr("x", screenX + screenBezel) + .attr("y", y + screenBezel) + .attr("width", screenWidth - screenBezel * 2) + .attr("height", screenHeight - screenBezel * 2) + .attr("rx", 2) + .attr("fill", "#0a0a12"); + + // Memory fill on screen + if (ramUsagePercent > 0) { + const memFillTotalHeight = screenHeight - screenBezel * 2; + const memFillActualHeight = + (ramUsagePercent / 100) * memFillTotalHeight; + nodeG + .append("rect") + .attr("x", screenX + screenBezel) + .attr( + "y", + y + screenBezel + (memFillTotalHeight - memFillActualHeight), + ) + .attr("width", screenWidth - screenBezel * 2) + .attr("height", memFillActualHeight) + .attr("fill", "rgba(255,215,0,0.85)") + .attr("clip-path", `url(#${linuxScreenClipId})`); + } + + // Terminal prompt on screen + nodeG + .append("text") + .attr("x", nodeInfo.x) + .attr("y", y + screenHeight / 2) + .attr("text-anchor", "middle") + .attr("dominant-baseline", "middle") + .attr("fill", "#FFFFFF") + .attr("opacity", 0.9) + .attr("font-size", screenHeight * 0.25) + .attr("font-family", "SF Mono, Monaco, monospace") + .attr("font-weight", "700") + .text(">_"); + + // Keyboard base (trapezoidal) + const baseY = y + screenHeight; + const baseTopWidth = screenWidth; + const baseBottomWidth = iconBaseWidth; + const baseTopX = nodeInfo.x - baseTopWidth / 2; + const baseBottomX = nodeInfo.x - baseBottomWidth / 2; + + nodeG + .append("path") + .attr( + "d", + `M ${baseTopX} ${baseY} L ${baseTopX + baseTopWidth} ${baseY} L ${baseBottomX + baseBottomWidth} ${baseY + baseHeight} L ${baseBottomX} ${baseY + baseHeight} Z`, + ) + .attr("fill", "#2c2c2c") + .attr("stroke", wireColor) + .attr("stroke-width", 1); + + // Keyboard area + const keyboardX = baseTopX + 6; + const keyboardY = baseY + 3; + const keyboardWidth = baseTopWidth - 12; + const keyboardHeight = baseHeight * 0.55; + nodeG + .append("rect") + .attr("x", keyboardX) + .attr("y", keyboardY) + .attr("width", keyboardWidth) + .attr("height", keyboardHeight) + .attr("fill", "rgba(0,0,0,0.2)") + .attr("rx", 2); + + // Trackpad + const trackpadWidth = baseTopWidth * 0.4; + const trackpadX = nodeInfo.x - trackpadWidth / 2; + const trackpadY = baseY + keyboardHeight + 5; + const trackpadHeight = baseHeight * 0.3; + nodeG + .append("rect") + .attr("x", trackpadX) + .attr("y", trackpadY) + .attr("width", trackpadWidth) + .attr("height", trackpadHeight) + .attr("fill", "rgba(255,255,255,0.08)") + .attr("rx", 2); + } else if (isLinux) { + // Linux Desktop — same shape as Mac Studio but with Tux logo + iconBaseWidth = nodeRadius * 1.25; + iconBaseHeight = nodeRadius * 0.85; + const x = nodeInfo.x - iconBaseWidth / 2; + const y = nodeInfo.y - iconBaseHeight / 2; + const cornerRadius = 4; + const topSurfaceHeight = iconBaseHeight * 0.15; + + const linuxDesktopClipId = `linux-desktop-${nodeInfo.id.replace(/[^a-zA-Z0-9]/g, "-")}`; + defs + .append("clipPath") + .attr("id", linuxDesktopClipId) + .append("rect") + .attr("x", x) + .attr("y", y + topSurfaceHeight) + .attr("width", iconBaseWidth) + .attr("height", iconBaseHeight - topSurfaceHeight) + .attr("rx", cornerRadius - 1); + + // Main body + nodeG + .append("rect") + .attr("class", "node-outline") + .attr("x", x) + .attr("y", y) + .attr("width", iconBaseWidth) + .attr("height", iconBaseHeight) + .attr("rx", cornerRadius) + .attr("fill", "#1a1a1a") + .attr("stroke", wireColor) + .attr("stroke-width", strokeWidth); + + // Memory fill + if (ramUsagePercent > 0) { + const memFillTotalHeight = iconBaseHeight - topSurfaceHeight; + const memFillActualHeight = + (ramUsagePercent / 100) * memFillTotalHeight; + nodeG + .append("rect") + .attr("x", x) + .attr( + "y", + y + topSurfaceHeight + (memFillTotalHeight - memFillActualHeight), + ) + .attr("width", iconBaseWidth) + .attr("height", memFillActualHeight) + .attr("fill", "rgba(255,215,0,0.75)") + .attr("clip-path", `url(#${linuxDesktopClipId})`); + } + + // Terminal prompt on front face + nodeG + .append("text") + .attr("x", nodeInfo.x) + .attr( + "y", + y + topSurfaceHeight + (iconBaseHeight - topSurfaceHeight) / 2, + ) + .attr("text-anchor", "middle") + .attr("dominant-baseline", "middle") + .attr("fill", "rgba(255,255,255,0.5)") + .attr("font-size", (iconBaseHeight - topSurfaceHeight) * 0.4) + .attr("font-family", "SF Mono, Monaco, monospace") + .attr("font-weight", "700") + .text(">_"); + } else if (modelLower === "mac studio") { // Mac Studio - classic cube with memory fill iconBaseWidth = nodeRadius * 1.25; iconBaseHeight = nodeRadius * 0.85; @@ -1182,8 +1568,12 @@ debugLabelY += debugLineHeight; } - const identity = identitiesData[nodeInfo.id]; - if (identity?.osVersion) { + const dbgIdentity = identitiesData[nodeInfo.id]; + if (dbgIdentity?.osVersion) { + const osLabel = + dbgIdentity.osVersion === "Linux" + ? "Linux" + : `macOS ${dbgIdentity.osVersion}${dbgIdentity.osBuildVersion ? ` (${dbgIdentity.osBuildVersion})` : ""}`; nodeG .append("text") .attr("x", nodeInfo.x) @@ -1192,9 +1582,7 @@ .attr("fill", "rgba(179,179,179,0.7)") .attr("font-size", debugFontSize) .attr("font-family", "SF Mono, Monaco, monospace") - .text( - `macOS ${identity.osVersion}${identity.osBuildVersion ? ` (${identity.osBuildVersion})` : ""}`, - ); + .text(osLabel); } } }); diff --git a/dashboard/src/lib/stores/app.svelte.ts b/dashboard/src/lib/stores/app.svelte.ts index 44fd296251..9771ab89b3 100644 --- a/dashboard/src/lib/stores/app.svelte.ts +++ b/dashboard/src/lib/stores/app.svelte.ts @@ -168,7 +168,7 @@ export interface ModelDownloadStatus { export interface PlacementPreview { model_id: string; sharding: "Pipeline" | "Tensor"; - instance_meta: "MlxRing" | "MlxJaccl"; + instance_meta: "MlxRing" | "MlxJaccl" | "Vllm"; instance: unknown | null; memory_delta_by_node: Record | null; error: string | null; @@ -547,6 +547,7 @@ class AppStore { { total: { inBytes: number }; available: { inBytes: number } } > >({}); + vllmAvailable = $state(false); placementPreviews = $state([]); selectedPreviewModelId = $state(null); isLoadingPreviews = $state(false); @@ -1331,6 +1332,15 @@ class AppStore { this.isConnected = true; } this.consecutiveFailures = 0; + + fetch("/capabilities") + .then((r) => (r.ok ? r.json() : null)) + .then((data: { vllm_available?: boolean } | null) => { + this.vllmAvailable = data?.vllm_available ?? false; + }) + .catch(() => { + this.vllmAvailable = false; + }); } catch (error) { this.consecutiveFailures++; if ( @@ -3334,6 +3344,7 @@ export const nodeThunderbolt = () => appStore.nodeThunderbolt; export const nodeRdmaCtl = () => appStore.nodeRdmaCtl; export const thunderboltBridgeCycles = () => appStore.thunderboltBridgeCycles; export const nodeThunderboltBridge = () => appStore.nodeThunderboltBridge; +export const vllmAvailable = () => appStore.vllmAvailable; // Image generation params export const imageGenerationParams = () => appStore.getImageGenerationParams(); diff --git a/dashboard/src/routes/+page.svelte b/dashboard/src/routes/+page.svelte index 262a833896..ccf0a48bed 100644 --- a/dashboard/src/routes/+page.svelte +++ b/dashboard/src/routes/+page.svelte @@ -64,6 +64,7 @@ nodeThunderboltBridge, nodeIdentities, isConnected, + vllmAvailable, type DownloadProgress, type PlacementPreview, } from "$lib/stores/app.svelte"; @@ -700,7 +701,10 @@ ? Object.keys(topologyData()!.nodes).length : 1; const sharding = nodeCount <= 1 ? "Pipeline" : selectedSharding; - const instanceType = nodeCount <= 1 ? "MlxRing" : selectedInstanceType; + const instanceType = + nodeCount <= 1 && selectedInstanceType !== "Vllm" + ? "MlxRing" + : selectedInstanceType; try { const placementResponse = await fetch( `/instance/placement?model_id=${encodeURIComponent(modelId)}&sharding=${sharding}&instance_meta=${instanceType}&min_nodes=1`, @@ -884,7 +888,7 @@ } let selectedSharding = $state<"Pipeline" | "Tensor">("Pipeline"); - type InstanceMeta = "MlxRing" | "MlxJaccl"; + type InstanceMeta = "MlxRing" | "MlxJaccl" | "Vllm"; // Launch defaults persistence const LAUNCH_DEFAULTS_KEY = "exo-launch-defaults-v2"; @@ -930,7 +934,11 @@ // Apply sharding and instance type unconditionally selectedSharding = defaults.sharding; selectedInstanceType = - defaults.instanceType === "MlxRing" ? "MlxRing" : "MlxJaccl"; + defaults.instanceType === "Vllm" + ? "Vllm" + : defaults.instanceType === "MlxRing" + ? "MlxRing" + : "MlxJaccl"; // Apply minNodes if valid (between 1 and maxNodes) if ( @@ -1144,9 +1152,7 @@ } const matchesSelectedRuntime = (runtime: InstanceMeta): boolean => - selectedInstanceType === "MlxRing" - ? runtime === "MlxRing" - : runtime === "MlxJaccl"; + runtime === selectedInstanceType; // Helper to check if a model can be launched (has valid placement with >= minNodes) function canModelFit(modelId: string): boolean { @@ -2091,6 +2097,7 @@ let instanceType = "Unknown"; if (instanceTag === "MlxRingInstance") instanceType = "MLX Ring"; else if (instanceTag === "MlxJacclInstance") instanceType = "MLX RDMA"; + else if (instanceTag === "VllmInstance") instanceType = "vLLM (CUDA)"; const inst = instance as { shardAssignments?: { @@ -5840,6 +5847,32 @@ RDMA (Fast) + {#if vllmAvailable()} + + {/if} diff --git a/flake.nix b/flake.nix index ff5ec7121a..886a505db4 100644 --- a/flake.nix +++ b/flake.nix @@ -51,8 +51,8 @@ }; nixConfig = { - extra-trusted-public-keys = "exo.cachix.org-1:okq7hl624TBeAR3kV+g39dUFSiaZgLRkLsFBCuJ2NZI="; - extra-substituters = "https://exo.cachix.org"; + extra-trusted-public-keys = "exo.cachix.org-1:okq7hl624TBeAR3kV+g39dUFSiaZgLRkLsFBCuJ2NZI= cache.nixos-cuda.org:74DUi4Ye579gUqzH4ziL9IyiJBlDpMRn9MBN8oNan9M="; + extra-substituters = "https://exo.cachix.org https://cache.nixos-cuda.org"; }; outputs = @@ -76,6 +76,8 @@ let # Use pinned nixpkgs for swift-format (swift is broken on x86_64-linux in newer nixpkgs) pkgsSwift = import inputs.nixpkgs-swift { inherit system; }; + + pkgsCuda = import ./nix/cuda-pkgs.nix { nixpkgs = inputs.nixpkgs; inherit system; }; in { # Allow unfree for metal-toolchain (needed for Darwin Metal packages) @@ -112,66 +114,137 @@ }; }; - packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin ( - let - uvLock = builtins.fromTOML (builtins.readFile ./uv.lock); - mlxPackage = builtins.head (builtins.filter (p: p.name == "mlx" && p.source ? git) uvLock.package); - uvLockMlxVersion = mlxPackage.version; - uvLockMlxRev = builtins.elemAt (builtins.split "#" mlxPackage.source.git) 2; - in - { - metal-toolchain = pkgs.callPackage ./nix/metal-toolchain.nix { }; - mlx = pkgs.callPackage ./nix/mlx.nix { - inherit (self'.packages) metal-toolchain; - inherit uvLockMlxVersion uvLockMlxRev; - }; - default = self'.packages.exo; - } - ); - - devShells.default = with pkgs; pkgs.mkShell { - inputsFrom = [ self'.checks.cargo-build ]; - - packages = - [ - # FORMATTING - config.treefmt.build.wrapper - - # PYTHON - python313 - uv - ruff - basedpyright - - # RUST - config.rust.toolchain - maturin - - # NIX - nixpkgs-fmt - - # SVELTE - nodejs - - # MISC - just - jq - ] - ++ lib.optionals stdenv.isLinux [ - unixtools.ifconfig - ] - ++ lib.optionals stdenv.isDarwin [ - macmon + packages = lib.optionalAttrs pkgs.stdenv.hostPlatform.isDarwin + ( + let + uvLock = builtins.fromTOML (builtins.readFile ./uv.lock); + mlxPackage = builtins.head (builtins.filter (p: p.name == "mlx" && p.source ? git) uvLock.package); + uvLockMlxVersion = mlxPackage.version; + uvLockMlxRev = builtins.elemAt (builtins.split "#" mlxPackage.source.git) 2; + in + { + metal-toolchain = pkgs.callPackage ./nix/metal-toolchain.nix { }; + mlx = pkgs.callPackage ./nix/mlx.nix { + inherit (self'.packages) metal-toolchain; + inherit uvLockMlxVersion uvLockMlxRev; + }; + default = self'.packages.exo; + } + ) // lib.optionalAttrs (pkgsCuda != null) { + torch-cuda = pkgsCuda.python313Packages.torch; + vllm-cuda = pkgsCuda.python313Packages.vllm; + + # Smoke test script for verifying vLLM + CUDA GPU setup + vllm-check = pkgs.writeShellApplication { + name = "vllm-check"; + runtimeInputs = [ + (pkgsCuda.python313.withPackages (ps: [ ps.torch ps.vllm ])) ]; + # On non-NixOS hosts, NVIDIA driver libraries live in /usr/lib and must be + # LD_PRELOAD'd individually (adding the whole dir causes SIGILL from conflicts). + # These are: CUDA driver, NVML, and the PTX JIT compiler (for flash attention). + # libnvJitLink comes from the nix CUDA toolkit via LD_LIBRARY_PATH. + text = '' + for dir in /usr/lib/aarch64-linux-gnu /usr/lib/x86_64-linux-gnu /usr/lib; do + if [ -e "$dir/libcuda.so.1" ]; then + NVIDIA_LIBS="$dir/libcuda.so.1" + for lib in libnvidia-ml.so.1 libnvidia-ptxjitcompiler.so.1; do + [ -e "$dir/$lib" ] && NVIDIA_LIBS="$NVIDIA_LIBS:$dir/$lib" + done + export LD_PRELOAD="$NVIDIA_LIBS''${LD_PRELOAD:+:$LD_PRELOAD}" + break + fi + done + export LD_LIBRARY_PATH="${pkgsCuda.cudaPackages.libnvjitlink}/lib''${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" + exec python ${inputs.self + /tests/test_vllm_smoke.py} + ''; + }; - OPENSSL_NO_VENDOR = "1"; + # exo with CUDA torch + vLLM — wraps the uv2nix-built package with host driver libs + exo-cuda = pkgs.writeShellApplication { + name = "exo-cuda"; + runtimeInputs = [ self'.packages.exo-cuda-unwrapped ]; + text = '' + for dir in /usr/lib/aarch64-linux-gnu /usr/lib/x86_64-linux-gnu /usr/lib; do + if [ -e "$dir/libcuda.so.1" ]; then + NVIDIA_LIBS="$dir/libcuda.so.1" + for lib in libnvidia-ml.so.1 libnvidia-ptxjitcompiler.so.1; do + [ -e "$dir/$lib" ] && NVIDIA_LIBS="$NVIDIA_LIBS:$dir/$lib" + done + export LD_PRELOAD="$NVIDIA_LIBS''${LD_PRELOAD:+:$LD_PRELOAD}" + break + fi + done + export LD_LIBRARY_PATH="${pkgsCuda.stdenv.cc.cc.lib}/lib:${pkgsCuda.cudaPackages.libnvjitlink}/lib''${LD_LIBRARY_PATH:+:$LD_LIBRARY_PATH}" + exec exo-cuda "$@" + ''; + }; + }; - shellHook = '' - export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${python313}/lib" - ${lib.optionalString stdenv.isLinux '' - export LD_LIBRARY_PATH="${openssl.out}/lib:$LD_LIBRARY_PATH" - ''} - ''; + # CUDA development shell with torch + vLLM (aarch64-linux only) + devShells = lib.optionalAttrs (pkgsCuda != null) + { + cuda = pkgs.mkShell { + packages = [ + (pkgsCuda.python313.withPackages (ps: [ + ps.torch + ps.vllm + ])) + pkgs.uv + pkgs.just + ]; + + shellHook = '' + echo "CUDA dev shell with torch + vLLM" + python -c "import torch; print(f'PyTorch {torch.__version__}, CUDA: {torch.cuda.is_available()}')" 2>/dev/null || true + ''; + }; + } // { + + default = with pkgs; pkgs.mkShell { + inputsFrom = [ self'.checks.cargo-build ]; + + packages = + [ + # FORMATTING + config.treefmt.build.wrapper + + # PYTHON + python313 + uv + ruff + basedpyright + + # RUST + config.rust.toolchain + maturin + + # NIX + nixpkgs-fmt + + # SVELTE + nodejs + + # MISC + just + jq + ] + ++ lib.optionals stdenv.isLinux [ + unixtools.ifconfig + ] + ++ lib.optionals stdenv.isDarwin [ + macmon + ]; + + OPENSSL_NO_VENDOR = "1"; + + shellHook = '' + export LD_LIBRARY_PATH="$LD_LIBRARY_PATH:${python313}/lib" + ${lib.optionalString stdenv.isLinux '' + export LD_LIBRARY_PATH="${openssl.out}/lib:$LD_LIBRARY_PATH" + ''} + ''; + }; }; }; }; diff --git a/justfile b/justfile index f278e57954..e21453d098 100644 --- a/justfile +++ b/justfile @@ -15,6 +15,18 @@ check: sync: uv sync --all-packages +sync-cuda: + #!/usr/bin/env bash + set -euo pipefail + if command -v nvidia-smi &>/dev/null; then + arch=$(nvidia-smi --query-gpu=compute_cap --format=csv,noheader | head -1 | tr -d ' ') + export TORCH_CUDA_ARCH_LIST="$arch" + export VLLM_TARGET_DEVICE=cuda + fi + uv pip install "cmake>=3.26.1" ninja "packaging>=24.2" "setuptools>=77.0.3,<81.0.0" "setuptools-scm>=8.0" wheel jinja2 + find ~/.cache/uv/git-v0 -name CMakeCache.txt -delete 2>/dev/null || true + uv sync --extra cuda + sync-clean: uv sync --all-packages --force-reinstall --no-cache diff --git a/nix/cuda-pkgs.nix b/nix/cuda-pkgs.nix new file mode 100644 index 0000000000..7894c3c559 --- /dev/null +++ b/nix/cuda-pkgs.nix @@ -0,0 +1,102 @@ +{ nixpkgs, system }: +let + pkgs = import nixpkgs { inherit system; }; +in +if system == "aarch64-linux" then + import nixpkgs + { + inherit system; + config = { + allowUnfree = true; + allowBroken = true; + allowUnsupportedSystem = true; + cudaSupport = true; + cudaCapabilities = [ "12.1" ]; + }; + overlays = [ + (final: prev: + let + cudaCompatStub = cfinal: cprev: { + cuda_compat = prev.runCommand "cuda13.0-cuda_compat-stub" { } "mkdir -p $out"; + }; + in + { + cudaPackages = prev.cudaPackages_13.overrideScope cudaCompatStub // { + override = args: + (prev.cudaPackages_13.override args).overrideScope cudaCompatStub; + }; + + pythonPackagesExtensions = prev.pythonPackagesExtensions ++ [ + (pyFinal: pyPrev: { + fastsafetensors = pyFinal.buildPythonPackage { + pname = "fastsafetensors"; + version = "0.2.2"; + src = prev.fetchFromGitHub { + owner = "foundation-model-stack"; + repo = "fastsafetensors"; + rev = "v0.2.2"; + hash = ""; + }; + pyproject = true; + build-system = [ + pyFinal.setuptools + pyFinal.pybind11 + ]; + buildInputs = [ + final.cudaPackages.cuda_cudart + final.cudaPackages.cuda_nvml_dev + ]; + nativeBuildInputs = [ + final.cudaPackages.cuda_nvcc + ]; + dependencies = [ + pyFinal.typer + ]; + env.CUDA_HOME = "${final.cudaPackages.cuda_nvcc}"; + pythonImportsCheck = [ "fastsafetensors" ]; + }; + cupy = pyPrev.cupy.override { + cudaPackages = final.cudaPackages; + }; + + bitsandbytes = pyPrev.bitsandbytes.overrideAttrs (old: { + preConfigure = (old.preConfigure or "") + '' + export CXXFLAGS="''${CXXFLAGS:-} -I${final.cudaPackages.cuda_crt}/include" + export CUDAFLAGS="''${CUDAFLAGS:-} -I${final.cudaPackages.cuda_crt}/include" + export CMAKE_CUDA_FLAGS="''${CMAKE_CUDA_FLAGS:-} -I${final.cudaPackages.cuda_crt}/include" + ''; + buildInputs = (old.buildInputs or [ ]) ++ [ final.cudaPackages.cuda_crt ]; + }); + + vllm = pyPrev.vllm.overrideAttrs (old: { + buildInputs = (old.buildInputs or [ ]) ++ [ + final.cuda_cccl_with_prefix + final.cudaPackages.cuda_crt + ]; + preConfigure = (old.preConfigure or "") + '' + export CXXFLAGS="''${CXXFLAGS:-} -I${final.cuda_cccl_with_prefix}/include -I${final.cudaPackages.cuda_crt}/include" + export CUDAFLAGS="''${CUDAFLAGS:-} -I${final.cuda_cccl_with_prefix}/include -I${final.cudaPackages.cuda_crt}/include" + ''; + }); + }) + ]; + + magma-cuda-static = prev.magma-cuda-static.overrideAttrs (old: { + postPatch = (old.postPatch or "") + '' + sed -i '/err = cudaGetDeviceProperties( &prop, dev );/a\ int clock_khz = 0; cudaDeviceGetAttribute(\&clock_khz, cudaDevAttrClockRate, dev);' interface_cuda/interface.cpp + sed -i 's/prop\.clockRate/clock_khz/g' interface_cuda/interface.cpp + ''; + }); + + cuda_cccl_with_prefix = prev.runCommand "cuda13.0-cuda_cccl-with-cccl-prefix" { } '' + mkdir -p $out/include + ln -s ${final.cudaPackages.cuda_cccl}/include $out/include/cccl + ''; + + opencv = prev.opencv.override { enableCuda = false; }; + opencv4 = prev.opencv4.override { enableCuda = false; }; + }) + ]; + } +else + null diff --git a/pyproject.toml b/pyproject.toml index b42cec58c8..6d7cd6ea64 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -15,17 +15,17 @@ dependencies = [ "huggingface-hub>=0.33.4", "psutil>=7.0.0", "loguru>=0.7.3", - "exo_pyo3_bindings", # rust bindings + "exo_pyo3_bindings", # rust bindings "anyio==4.11.0", "mlx; sys_platform == 'darwin'", - "mlx[cpu]==0.30.6; sys_platform == 'linux'", + "mlx==0.30.6; sys_platform == 'linux'", "mlx-lm", - "tiktoken>=0.12.0", # required for kimi k2 tokenizer + "tiktoken>=0.12.0", # required for kimi k2 tokenizer "hypercorn>=0.18.0", "openai-harmony>=0.0.8", "httpx>=0.28.1", "tomlkit>=0.14.0", - "mflux==0.16.9", + "mflux==0.16.9; sys_platform == 'darwin'", "python-multipart>=0.0.21", "msgspec>=0.19.0", "zstandard>=0.23.0", @@ -45,11 +45,13 @@ dev = [ "ruff>=0.11.13", ] -# mlx[cuda] requires a newer version of mlx. the ideal on linux is: default to mlx[cpu] unless[cuda] specified. [project.optional-dependencies] -# cuda = [ -# "mlx[cuda]==0.26.3", -# ] +cuda = [ + "torch>=2.10.0; sys_platform == 'linux'", + "vllm>=0.13.0; sys_platform == 'linux'", + "mlx-cuda-13==0.30.6; sys_platform == 'linux'", + "fastsafetensors>=0.1.10; sys_platform == 'linux'", +] ### # workspace configuration @@ -62,10 +64,17 @@ members = ["rust/exo_pyo3_bindings", "bench"] exo_pyo3_bindings = { workspace = true } mlx = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git", branch = "address-rdma-gpu-locks", marker = "sys_platform == 'darwin'" } mlx-lm = { git = "https://github.com/rltakashige/mlx-lm", branch = "leo/eval-left-padding-in-batched-rotation" } +torch = [{ index = "pytorch-cu130", marker = "sys_platform == 'linux'" }] +vllm = { git = "https://github.com/hmellor/vllm.git", branch = "transformers-v5" } # Uncomment to use local mlx/mlx-lm development versions: # mlx = { path = "/Users/Shared/mlx", editable=true } # mlx-lm = { path = "/Users/Shared/mlx-lm", editable=true } +[[tool.uv.index]] +name = "pytorch-cu130" +url = "https://download.pytorch.org/whl/cu130" +explicit = true + [build-system] requires = ["uv_build>=0.8.9,<0.9.0"] build-backend = "uv_build" @@ -99,8 +108,16 @@ exclude = [ "**/.direnv", "**/rust", "**/.github", + "**/vllm_patches", + "**/engines/vllm", ] stubPath = ".mlx_typings" +extraPaths = [".cuda_typings"] + +[[tool.basedpyright.executionEnvironments]] +root = "src/exo/worker/runner/vllm_inference" +extraPaths = ["src", ".cuda_typings"] +reportMissingModuleSource = false [[tool.basedpyright.executionEnvironments]] root = "src" @@ -113,7 +130,22 @@ root = "src" [tool.uv] required-version = ">=0.8.6" prerelease = "allow" -environments = ["sys_platform == 'darwin'", "sys_platform == 'linux'"] +environments = [ + "sys_platform == 'darwin'", + "sys_platform == 'linux' and platform_machine == 'aarch64'", +] +no-binary-package = ["vllm"] +no-build-isolation-package = ["vllm"] +extra-build-dependencies = { vllm = [ + "cmake>=3.26.1", + "ninja", + "packaging>=24.2", + "setuptools>=77.0.3,<81.0.0", + "setuptools-scm>=8.0", + "wheel", + "jinja2", +] } +conflicts = [[{ package = "exo", extra = "cuda" }, { package = "exo-bench" }]] ### # ruff configuration @@ -123,6 +155,7 @@ environments = ["sys_platform == 'darwin'", "sys_platform == 'linux'"] extend-exclude = [ "shared/protobufs/**", "*mlx_typings/**", + "*cuda_typings/**", "rust/exo_pyo3_bindings/**", "bench/vendor/**", ] diff --git a/python/parts.nix b/python/parts.nix index d1d1b6d6d1..a005326e1c 100644 --- a/python/parts.nix +++ b/python/parts.nix @@ -3,6 +3,7 @@ perSystem = { config, self', pkgs, lib, system, ... }: let + pkgsCuda = import ../nix/cuda-pkgs.nix { nixpkgs = inputs.nixpkgs; inherit system; }; # Load workspace from uv.lock workspace = inputs.uv2nix.lib.workspace.loadWorkspace { workspaceRoot = inputs.self; @@ -99,16 +100,18 @@ } ); + baseOverlays = [ + inputs.pyproject-build-systems.overlays.default + overlay + exoOverlay + buildSystemsOverlay + linuxOverlay + ]; + pythonSet = (pkgs.callPackage inputs.pyproject-nix.build.packages { inherit python; }).overrideScope ( - lib.composeManyExtensions [ - inputs.pyproject-build-systems.overlays.default - overlay - exoOverlay - buildSystemsOverlay - linuxOverlay - ] + lib.composeManyExtensions baseOverlays ); # mlx-cpu and mlx-cuda-13 both ship mlx/ site-packages files; keep first. # mlx-cpu/mlx-cuda-13 and nvidia-cudnn-cu12/cu13 ship overlapping files. @@ -172,6 +175,39 @@ --set EXO_RESOURCES_DIR ${inputs.self + /resources} \ ${lib.optionalString pkgs.stdenv.hostPlatform.isDarwin "--prefix PATH : ${pkgs.macmon}/bin"} ''; + + vllmEnv = pkgsCuda.python313.withPackages (ps: [ ps.vllm ps.fastsafetensors ]); + + vllmSite = pkgs.runCommand "vllm-site-filtered" { } '' + mkdir -p $out + for pkg in ${vllmEnv}/${python.sitePackages}/*; do + name=$(basename "$pkg") + case "$name" in + anyio*|pydantic*) ;; + *) ln -s "$pkg" "$out/$name" ;; + esac + done + ''; + + exoCudaDeps = exoDeps // { + mlx-cuda-13 = [ ]; + }; + + exoCudaVenv = (pythonSet.mkVirtualEnv "exo-cuda-env" exoCudaDeps).overrideAttrs { + venvIgnoreCollisions = venvCollisionPaths; + }; + + exoCudaPackage = pkgs.runCommand "exo-cuda" + { + nativeBuildInputs = [ pkgs.makeWrapper ]; + } + '' + mkdir -p $out/bin + makeWrapper ${exoCudaVenv}/bin/exo $out/bin/exo-cuda \ + --set EXO_DASHBOARD_DIR ${self'.packages.dashboard} \ + --set EXO_RESOURCES_DIR ${inputs.self + /resources} \ + --prefix PYTHONPATH : "${vllmSite}" + ''; in { # Python package only available on macOS (requires MLX/Metal) @@ -180,7 +216,9 @@ exo = exoPackage; # Test environment for running pytest outside of Nix sandbox (needs GPU access) exo-test-env = testVenv; - } // { + } // lib.optionalAttrs (pkgsCuda != null) { + exo-cuda-unwrapped = exoCudaPackage; + } // { exo-bench = mkBenchScript "exo-bench" (inputs.self + /bench/exo_bench.py); exo-eval = mkBenchScript "exo-eval" (inputs.self + /bench/exo_eval.py); exo-eval-tool-calls = mkBenchScript "exo-eval-tool-calls" (inputs.self + /bench/eval_tool_calls.py); diff --git a/src/exo/__main__.py b/src/exo/__main__.py index d2febaeefc..f02267a0b3 100644 --- a/src/exo/__main__.py +++ b/src/exo/__main__.py @@ -1,5 +1,3 @@ -from __future__ import annotations - import sys from collections.abc import Sequence from multiprocessing import freeze_support diff --git a/src/exo/download/download_utils.py b/src/exo/download/download_utils.py index 3f6f1dc9dc..a0d9f093b5 100644 --- a/src/exo/download/download_utils.py +++ b/src/exo/download/download_utils.py @@ -753,7 +753,10 @@ async def download_shard( ) filtered_file_list = list( filter_repo_objects( - file_list, allow_patterns=allow_patterns, key=lambda x: x.path + file_list, + allow_patterns=allow_patterns, + ignore_patterns=["original/*", "metal/*"], + key=lambda x: x.path, ) ) diff --git a/src/exo/master/adapters/ollama.py b/src/exo/master/adapters/ollama.py index 75b772d014..0d06124a5c 100644 --- a/src/exo/master/adapters/ollama.py +++ b/src/exo/master/adapters/ollama.py @@ -1,5 +1,3 @@ -from __future__ import annotations - import json from collections.abc import AsyncGenerator from typing import Any diff --git a/src/exo/master/api.py b/src/exo/master/api.py index 8c92fc21a4..a8ba0b5e92 100644 --- a/src/exo/master/api.py +++ b/src/exo/master/api.py @@ -341,6 +341,7 @@ def _setup_routes(self) -> None: self.app.get("/ollama/api/version")(self.ollama_version) self.app.get("/state")(lambda: self.state) + self.app.get("/capabilities")(self._get_capabilities) self.app.get("/events")(self.stream_events) self.app.post("/download/start")(self.start_download) self.app.delete("/download/{node_id}/{model_id:path}")(self.delete_download) @@ -448,18 +449,29 @@ async def get_placement_previews( status_code=400, detail=f"Failed to load model card: {exc}" ) from exc instance_combinations: list[tuple[Sharding, InstanceMeta, int]] = [] - for sharding in (Sharding.Pipeline, Sharding.Tensor): - for instance_meta in (InstanceMeta.MlxRing, InstanceMeta.MlxJaccl): - instance_combinations.extend( - [ - (sharding, instance_meta, i) - for i in range( - 1, len(list(self.state.topology.list_nodes())) + 1 - ) - ] - ) - # TODO: PDD - # instance_combinations.append((Sharding.PrefillDecodeDisaggregation, InstanceMeta.MlxRing, 1)) + node_count = len(list(self.state.topology.list_nodes())) + + # QMM is not available on MLX CUDA. Also, VLLM does not support MLX community models + is_mlx_community = str(model_card.model_id).startswith("mlx-community/") + is_quantized_mlx = is_mlx_community and model_card.quantization in ( + "4bit", + "8bit", + ) + skip_mlx = any(self.state.node_vllm.values()) and is_quantized_mlx + is_vllm_compatible_mlx = is_mlx_community and model_card.quantization in ( + "", + "bf16", + "fp16", + ) + skip_vllm = is_mlx_community and not is_vllm_compatible_mlx + if not skip_mlx: + for sharding in (Sharding.Pipeline, Sharding.Tensor): + for instance_meta in (InstanceMeta.MlxRing, InstanceMeta.MlxJaccl): + instance_combinations.extend( + [(sharding, instance_meta, i) for i in range(1, node_count + 1)] + ) + if any(self.state.node_vllm.values()) and not skip_vllm: + instance_combinations.append((Sharding.Pipeline, InstanceMeta.Vllm, 1)) for sharding, instance_meta, min_nodes in instance_combinations: try: @@ -781,6 +793,9 @@ async def _validate_image_model(self, model: ModelId) -> ModelId: ) return resolved_model + def _get_capabilities(self) -> dict[str, bool]: + return {"vllm_available": any(self.state.node_vllm.values())} + def stream_events(self) -> StreamingResponse: def _generate_json_array(events: Iterable[Event]) -> Iterable[str]: yield "[" diff --git a/src/exo/master/placement.py b/src/exo/master/placement.py index f9e9de7310..9dca3f6f29 100644 --- a/src/exo/master/placement.py +++ b/src/exo/master/placement.py @@ -41,6 +41,7 @@ InstanceMeta, MlxJacclInstance, MlxRingInstance, + VllmInstance, ) from exo.shared.types.worker.shards import Sharding @@ -78,8 +79,11 @@ def place_instance( for cycle in candidate_cycles if required_nodes.issubset(cycle.node_ids) ] + required_memory = command.model_card.storage_size + if command.instance_meta == InstanceMeta.Vllm: + required_memory = Memory.from_bytes(int(required_memory.in_bytes * 1.3)) cycles_with_sufficient_memory = filter_cycles_by_memory( - candidate_cycles, node_memory, command.model_card.storage_size + candidate_cycles, node_memory, required_memory ) if len(cycles_with_sufficient_memory) == 0: raise ValueError("No cycles found with sufficient memory") @@ -139,7 +143,7 @@ def place_instance( ) # Single-node: force Pipeline/Ring (Tensor and Jaccl require multi-node) - if len(selected_cycle) == 1: + if len(selected_cycle) == 1 and command.instance_meta != InstanceMeta.Vllm: command.instance_meta = InstanceMeta.MlxRing command.sharding = Sharding.Pipeline @@ -199,6 +203,11 @@ def get_device_rank(node_id: NodeId) -> int: hosts_by_node=hosts_by_node, ephemeral_port=ephemeral_port, ) + case InstanceMeta.Vllm: + target_instances[instance_id] = VllmInstance( + instance_id=instance_id, + shard_assignments=shard_assignments, + ) return target_instances diff --git a/src/exo/shared/apply.py b/src/exo/shared/apply.py index 53f3499e6f..23000790b3 100644 --- a/src/exo/shared/apply.py +++ b/src/exo/shared/apply.py @@ -49,9 +49,11 @@ NodeConfig, NodeDiskUsage, NodeNetworkInterfaces, + NvmlMetrics, RdmaCtlStatus, StaticNodeInformation, ThunderboltBridgeInfo, + VllmCapability, ) @@ -363,6 +365,16 @@ def apply_node_gathered_info(event: NodeGatheredInfo, state: State) -> State: **state.node_rdma_ctl, event.node_id: NodeRdmaCtlStatus(enabled=info.enabled), } + case NvmlMetrics(): + update["node_system"] = { + **state.node_system, + event.node_id: info.system_profile, + } + case VllmCapability(): + update["node_vllm"] = { + **state.node_vllm, + event.node_id: info.available, + } return state.model_copy(update=update) diff --git a/src/exo/shared/models/model_cards.py b/src/exo/shared/models/model_cards.py index 048c469f22..80e15ca624 100644 --- a/src/exo/shared/models/model_cards.py +++ b/src/exo/shared/models/model_cards.py @@ -258,21 +258,24 @@ async def fetch_safetensors_size(model_id: ModelId) -> Memory: target_dir = (await ensure_models_dir()) / model_id.normalize() await aios.makedirs(target_dir, exist_ok=True) - index_path = await download_file_with_retry( - model_id, - "main", - "model.safetensors.index.json", - target_dir, - lambda curr_bytes, total_bytes, is_renamed: logger.debug( - f"Downloading model.safetensors.index.json for {model_id}: {curr_bytes}/{total_bytes} ({is_renamed=})" - ), - ) - async with aiofiles.open(index_path, "r") as f: - index_data = ModelSafetensorsIndex.model_validate_json(await f.read()) - - metadata = index_data.metadata - if metadata is not None: - return Memory.from_bytes(metadata.total_size) + try: + index_path = await download_file_with_retry( + model_id, + "main", + "model.safetensors.index.json", + target_dir, + lambda curr_bytes, total_bytes, is_renamed: logger.debug( + f"Downloading model.safetensors.index.json for {model_id}: {curr_bytes}/{total_bytes} ({is_renamed=})" + ), + ) + async with aiofiles.open(index_path, "r") as f: + index_data = ModelSafetensorsIndex.model_validate_json(await f.read()) + + metadata = index_data.metadata + if metadata is not None: + return Memory.from_bytes(metadata.total_size) + except FileNotFoundError: + pass info = model_info(model_id) if info.safetensors is None: diff --git a/src/exo/shared/tracing.py b/src/exo/shared/tracing.py index a353d923bf..e09834b996 100644 --- a/src/exo/shared/tracing.py +++ b/src/exo/shared/tracing.py @@ -1,5 +1,3 @@ -from __future__ import annotations - import json import time from collections import defaultdict diff --git a/src/exo/shared/types/mlx.py b/src/exo/shared/types/mlx.py index f15fed42d1..1c63bafa34 100644 --- a/src/exo/shared/types/mlx.py +++ b/src/exo/shared/types/mlx.py @@ -12,11 +12,14 @@ RotatingKVCache, ) -# This list contains one cache entry per transformer layer -KVCacheType = Sequence[ +from exo.worker.engines.vllm.kv_cache import TorchKVCache + +MLXCacheType = Sequence[ KVCache | RotatingKVCache | QuantizedKVCache | ArraysCache | CacheList ] +KVCacheType = MLXCacheType | TorchKVCache + # Model is a wrapper function to fix the fact that mlx is not strongly typed in the same way that EXO is. # For example - MLX has no guarantee of the interface that nn.Module will expose. But we need a guarantee that it has a __call__() function @@ -26,6 +29,6 @@ class Model(nn.Module): def __call__( self, x: mx.array, - cache: KVCacheType | None, + cache: MLXCacheType | None, input_embeddings: mx.array | None = None, ) -> mx.array: ... diff --git a/src/exo/shared/types/ollama_api.py b/src/exo/shared/types/ollama_api.py index 9f881fbe72..12f22afe05 100644 --- a/src/exo/shared/types/ollama_api.py +++ b/src/exo/shared/types/ollama_api.py @@ -1,5 +1,3 @@ -from __future__ import annotations - import time from typing import Any, Literal diff --git a/src/exo/shared/types/profiling.py b/src/exo/shared/types/profiling.py index ad1d48c0f2..ebd5880466 100644 --- a/src/exo/shared/types/profiling.py +++ b/src/exo/shared/types/profiling.py @@ -61,6 +61,7 @@ class SystemPerformanceProfile(CamelCaseModel): gpu_usage: float = 0.0 temp: float = 0.0 + # Best effort sys power sys_power: float = 0.0 pcpu_usage: float = 0.0 ecpu_usage: float = 0.0 diff --git a/src/exo/shared/types/state.py b/src/exo/shared/types/state.py index 7350cfb0f4..8848226349 100644 --- a/src/exo/shared/types/state.py +++ b/src/exo/shared/types/state.py @@ -57,6 +57,7 @@ class State(CamelCaseModel): node_thunderbolt: Mapping[NodeId, NodeThunderboltInfo] = {} node_thunderbolt_bridge: Mapping[NodeId, ThunderboltBridgeStatus] = {} node_rdma_ctl: Mapping[NodeId, NodeRdmaCtlStatus] = {} + node_vllm: Mapping[NodeId, bool] = {} # Detected cycles where all nodes have Thunderbolt bridge enabled (>2 nodes) thunderbolt_bridge_cycles: Sequence[Sequence[NodeId]] = [] diff --git a/src/exo/shared/types/worker/instances.py b/src/exo/shared/types/worker/instances.py index 76bd6fd4e6..ee21f4f2bf 100644 --- a/src/exo/shared/types/worker/instances.py +++ b/src/exo/shared/types/worker/instances.py @@ -15,6 +15,7 @@ class InstanceId(Id): class InstanceMeta(str, Enum): MlxRing = "MlxRing" MlxJaccl = "MlxJaccl" + Vllm = "Vllm" class BaseInstance(TaggedModel): @@ -35,8 +36,12 @@ class MlxJacclInstance(BaseInstance): jaccl_coordinators: dict[NodeId, str] +class VllmInstance(BaseInstance): + pass + + # TODO: Single node instance -Instance = MlxRingInstance | MlxJacclInstance +Instance = MlxRingInstance | MlxJacclInstance | VllmInstance class BoundInstance(CamelCaseModel): diff --git a/src/exo/utils/info_gatherer/info_gatherer.py b/src/exo/utils/info_gatherer/info_gatherer.py index b5e5d24efa..bc94d67dc3 100644 --- a/src/exo/utils/info_gatherer/info_gatherer.py +++ b/src/exo/utils/info_gatherer/info_gatherer.py @@ -32,6 +32,7 @@ from exo.utils.task_group import TaskGroup from .macmon import MacmonMetrics +from .nvml import NvmlMetrics from .system_info import ( get_friendly_name, get_model_and_chip, @@ -43,6 +44,12 @@ IS_DARWIN = sys.platform == "darwin" +def _has_nvml() -> bool: + from exo.utils.info_gatherer.nvml import has_nvml + + return has_nvml() + + async def _get_thunderbolt_devices() -> set[str] | None: """Get Thunderbolt interface device names (e.g., en2, en3) from hardware ports. @@ -354,6 +361,21 @@ async def _gather_iface_map() -> dict[str, str] | None: return ports +class VllmCapability(TaggedModel): + available: bool + version: str | None = None + + @classmethod + async def gather(cls) -> Self: + try: + import importlib + + vllm = importlib.import_module("vllm") + return cls(available=True, version=getattr(vllm, "__version__", None)) + except ImportError: + return cls(available=False) + + GatheredInfo = ( MacmonMetrics | MemoryUsage @@ -362,6 +384,8 @@ async def _gather_iface_map() -> dict[str, str] | None: | MacThunderboltConnections | RdmaCtlStatus | ThunderboltBridgeInfo + | VllmCapability + | NvmlMetrics | NodeConfig | MiscData | StaticNodeInformation @@ -381,6 +405,7 @@ class InfoGatherer: static_info_poll_interval: float | None = 60 rdma_ctl_poll_interval: float | None = 10 if IS_DARWIN else None disk_poll_interval: float | None = 30 + vllm_capability_poll_interval: float | None = 60 _tg: TaskGroup = field(init=False, default_factory=TaskGroup) async def run(self): @@ -389,7 +414,6 @@ async def run(self): if (macmon_path := shutil.which("macmon")) is not None: tg.start_soon(self._monitor_macmon, macmon_path) else: - # macmon not installed — fall back to psutil for memory logger.warning( "macmon not found, falling back to psutil for memory monitoring" ) @@ -397,11 +421,14 @@ async def run(self): tg.start_soon(self._monitor_system_profiler_thunderbolt_data) tg.start_soon(self._monitor_thunderbolt_bridge_status) tg.start_soon(self._monitor_rdma_ctl_status) + elif _has_nvml(): + tg.start_soon(self._monitor_nvml_metrics) tg.start_soon(self._watch_system_info) tg.start_soon(self._monitor_memory_usage) tg.start_soon(self._monitor_misc) tg.start_soon(self._monitor_static_info) tg.start_soon(self._monitor_disk_usage) + tg.start_soon(self._monitor_vllm_capability) nc = await NodeConfig.gather() if nc is not None: @@ -525,6 +552,26 @@ async def _monitor_disk_usage(self): logger.warning(f"Error gathering disk usage: {e}") await anyio.sleep(self.disk_poll_interval) + async def _monitor_nvml_metrics(self): + while True: + try: + from exo.utils.info_gatherer.nvml import gather_nvidia_metrics + + metrics = gather_nvidia_metrics() + if metrics is not None: + await self.info_sender.send(metrics) + except Exception as e: + logger.warning(f"Error gathering NVML metrics: {e}") + await anyio.sleep(1) + + async def _monitor_vllm_capability(self): + if self.vllm_capability_poll_interval is None: + return + try: + await self.info_sender.send(await VllmCapability.gather()) + except Exception as e: + logger.warning(f"Error gathering vLLM capability: {e}") + async def _monitor_macmon(self, macmon_path: str): if self.macmon_interval is None: return diff --git a/src/exo/utils/info_gatherer/nvml.py b/src/exo/utils/info_gatherer/nvml.py new file mode 100644 index 0000000000..0af27c952b --- /dev/null +++ b/src/exo/utils/info_gatherer/nvml.py @@ -0,0 +1,85 @@ +# pyright: reportMissingImports=false +from exo.shared.types.profiling import SystemPerformanceProfile +from exo.utils.pydantic_ext import TaggedModel + +try: + from pynvml import ( + nvmlDeviceGetCount, + nvmlDeviceGetHandleByIndex, + nvmlDeviceGetPowerUsage, + nvmlDeviceGetTemperature, + nvmlDeviceGetUtilizationRates, + nvmlInit, + nvmlShutdown, + ) +except ImportError: + nvmlDeviceGetCount = None # noqa: N816 + nvmlDeviceGetHandleByIndex = None # noqa: N816 + nvmlDeviceGetPowerUsage = None # noqa: N816 + nvmlDeviceGetTemperature = None # noqa: N816 + nvmlDeviceGetUtilizationRates = None # noqa: N816 + nvmlInit = None # noqa: N816 + nvmlShutdown = None # noqa: N816 + +_CPU_POWER_IDLE = 20.0 +_CPU_POWER_MAX = 100.0 +_GPU_POWER_MAX = 120.0 + + +class NvmlMetrics(TaggedModel): + system_profile: SystemPerformanceProfile + + +def has_nvml() -> bool: + if nvmlInit is None: + return False + try: + nvmlInit() + count = nvmlDeviceGetCount() # type: ignore[reportOptionalCall] + nvmlShutdown() # type: ignore[reportOptionalCall] + return count > 0 + except Exception: + return False + + +def gather_nvidia_metrics() -> NvmlMetrics | None: + if nvmlInit is None or nvmlDeviceGetCount is None or nvmlShutdown is None: + return None + if nvmlDeviceGetHandleByIndex is None or nvmlDeviceGetUtilizationRates is None: + return None + if nvmlDeviceGetTemperature is None or nvmlDeviceGetPowerUsage is None: + return None + + try: + nvmlInit() + count = nvmlDeviceGetCount() + if count == 0: + nvmlShutdown() + return None + + total_gpu_util = 0.0 + total_temp = 0.0 + total_gpu_power = 0.0 + for i in range(count): + handle = nvmlDeviceGetHandleByIndex(i) + util = nvmlDeviceGetUtilizationRates(handle) + total_gpu_util += float(util.gpu) + total_temp += float(nvmlDeviceGetTemperature(handle, 0)) + total_gpu_power += float(nvmlDeviceGetPowerUsage(handle)) / 1000.0 + + nvmlShutdown() + + gpu_load_fraction = min(total_gpu_power / _GPU_POWER_MAX, 1.0) + estimated_cpu_power = ( + _CPU_POWER_IDLE + (_CPU_POWER_MAX - _CPU_POWER_IDLE) * gpu_load_fraction + ) + + return NvmlMetrics( + system_profile=SystemPerformanceProfile( + gpu_usage=total_gpu_util / count / 100.0, + temp=total_temp / count, + sys_power=total_gpu_power + estimated_cpu_power, + ), + ) + except Exception: + return None diff --git a/src/exo/utils/info_gatherer/system_info.py b/src/exo/utils/info_gatherer/system_info.py index 0cc13f9c9c..2e9f619c6f 100644 --- a/src/exo/utils/info_gatherer/system_info.py +++ b/src/exo/utils/info_gatherer/system_info.py @@ -1,6 +1,7 @@ import platform import socket import sys +from pathlib import Path from subprocess import CalledProcessError import psutil @@ -117,12 +118,97 @@ async def get_network_interfaces() -> list[NetworkInterfaceInfo]: return interfaces_info +def _read_dmi_field(name: str) -> str | None: + """Read a single DMI sysfs field, returning ``None`` on failure.""" + try: + path = Path(f"/sys/class/dmi/id/{name}") + if path.exists(): + return path.read_text().strip() + except (OSError, PermissionError): + pass + return None + + +async def _get_linux_model_and_chip() -> tuple[str, str]: + """Get Linux system information using DMI and /proc/cpuinfo. + + Detects NVIDIA DGX Spark (DMI product_name ``"DGX_Spark"``) and other + NVIDIA systems via the ``sys_vendor`` DMI field, falling back to generic + Linux identification. + """ + model = "Linux" + chip = "Unknown Chip" + + product_name = _read_dmi_field("product_name") + sys_vendor = _read_dmi_field("sys_vendor") + + # DGX Spark: DMI product_name may be "DGX_Spark" or "gx10" variant + product_lower = (product_name or "").lower() + if product_name and ("dgx" in product_lower or "gx10" in product_lower): + model = "DGX Spark" + try: + process = await run_process( + ["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"] + ) + gpu_name = process.stdout.decode().strip().split("\n")[0] + chip = gpu_name if gpu_name and gpu_name != "[N/A]" else "NVIDIA GB10" + except (CalledProcessError, FileNotFoundError): + chip = "NVIDIA GB10" + return (model, chip) + + # Other NVIDIA systems (sys_vendor contains "NVIDIA") + if sys_vendor and "NVIDIA" in sys_vendor: + model = product_name.replace("_", " ") if product_name else "NVIDIA System" + try: + process = await run_process( + ["nvidia-smi", "--query-gpu=name", "--format=csv,noheader"] + ) + gpu_name = process.stdout.decode().strip().split("\n")[0] + if gpu_name and gpu_name != "[N/A]": + chip = gpu_name + except (CalledProcessError, FileNotFoundError): + pass + return (model, chip) + + # Generic Linux — detect laptop vs desktop via chassis_type + # SMBIOS chassis types: 8,9,10,14,31,32 = portable/laptop + chassis_type = _read_dmi_field("chassis_type") + laptop_chassis_types = {"8", "9", "10", "14", "31", "32"} + if chassis_type in laptop_chassis_types: + model = "Linux Laptop" + elif chassis_type is not None: + model = "Linux Desktop" + + # Also check for battery as a fallback laptop indicator + if model == "Linux" and Path("/sys/class/power_supply/BAT0").exists(): + model = "Linux Laptop" + + # Use /proc/cpuinfo for chip + cpuinfo_path = Path("/proc/cpuinfo") + if cpuinfo_path.exists(): + try: + for line in cpuinfo_path.read_text().splitlines(): + if line.startswith("model name"): + chip = line.split(":", 1)[1].strip() + break + except OSError: + pass + + return (model, chip) + + async def get_model_and_chip() -> tuple[str, str]: - """Get Mac system information using system_profiler.""" + """Get system model and chip information. + + On macOS, uses ``system_profiler``. On Linux, reads DMI data from + sysfs and CPU info from ``/proc/cpuinfo``. + """ model = "Unknown Model" chip = "Unknown Chip" - # TODO: better non mac support + if sys.platform == "linux": + return await _get_linux_model_and_chip() + if sys.platform != "darwin": return (model, chip) diff --git a/src/exo/worker/engines/mlx/cache.py b/src/exo/worker/engines/mlx/cache.py index 3877a61cd0..b04a244e03 100644 --- a/src/exo/worker/engines/mlx/cache.py +++ b/src/exo/worker/engines/mlx/cache.py @@ -1,5 +1,6 @@ import os from copy import deepcopy +from typing import TYPE_CHECKING, cast import mlx.core as mx import psutil @@ -13,10 +14,13 @@ from mlx_lm.tokenizer_utils import TokenizerWrapper from exo.shared.types.memory import Memory -from exo.shared.types.mlx import KVCacheType, Model +from exo.shared.types.mlx import KVCacheType, MLXCacheType, Model from exo.worker.engines.mlx.constants import CACHE_GROUP_SIZE, KV_CACHE_BITS from exo.worker.runner.bootstrap import logger +if TYPE_CHECKING: + from exo.worker.engines.vllm.kv_cache import TorchKVCache + # Fraction of device memory above which LRU eviction kicks in. # Smaller machines need more aggressive eviction. @@ -46,7 +50,7 @@ def __init__( self.token_count = token_count -def snapshot_ssm_states(cache: KVCacheType) -> CacheSnapshot: +def snapshot_ssm_states(cache: MLXCacheType) -> CacheSnapshot: states: list[ArraysCache | RotatingKVCache | None] = [] for c in cache: if isinstance(c, (ArraysCache, RotatingKVCache)): @@ -70,7 +74,7 @@ def _find_nearest_snapshot( return best -def has_non_kv_caches(cache: KVCacheType) -> bool: +def has_non_kv_caches(cache: MLXCacheType) -> bool: """Check if a cache contains any ArraysCache (SSM) entries.""" return any(isinstance(c, (ArraysCache, RotatingKVCache)) for c in cache) @@ -94,7 +98,7 @@ def clear(self): def add_kv_cache( self, prompt_tokens: mx.array, - cache: KVCacheType, + cache: MLXCacheType, ssm_snapshots: list[CacheSnapshot] | None = None, ): """Add a new cache entry. Evicts LRU entries if memory is high.""" @@ -110,7 +114,7 @@ def update_kv_cache( self, index: int, prompt_tokens: mx.array, - cache: KVCacheType, + cache: MLXCacheType, snapshots: list[CacheSnapshot] | None, restore_pos: int, ): @@ -129,10 +133,14 @@ def update_kv_cache( self._last_used[index] = self._access_counter logger.info(f"KV cache updated (index {index}): {len(prompt_tokens)} tokens") + def _get_mlx_cache(self, index: int) -> MLXCacheType: + cached = self.caches[index] + return cast(MLXCacheType, cached) + def _get_snapshot( self, entry_index: int, target_token_count: int ) -> tuple[int, CacheSnapshot | None]: - if not has_non_kv_caches(self.caches[entry_index]): + if not has_non_kv_caches(self._get_mlx_cache(entry_index)): return target_token_count, None snapshots = self._snapshots[entry_index] @@ -149,7 +157,7 @@ def get_kv_cache( self, model: Model, prompt_tokens: mx.array, - ) -> tuple[KVCacheType, mx.array, int | None]: + ) -> tuple[MLXCacheType, mx.array, int | None]: """Get KV cache for prompt, returning remaining tokens to prefill. Returns: @@ -184,7 +192,8 @@ def get_kv_cache( # For exact match: trim to max_length-1 so remaining has the last token # For partial match: trim to best_length, remaining has suffix to prefill # This ensures stream_generate always has at least one token to start with - has_ssm = has_non_kv_caches(self.caches[best_index]) + mlx_cache = self._get_mlx_cache(best_index) + has_ssm = has_non_kv_caches(mlx_cache) target = (max_length - 1) if is_exact and not has_ssm else best_length restore_pos, restore_snap = self._get_snapshot(best_index, target) @@ -192,8 +201,8 @@ def get_kv_cache( if restore_snap is None and has_ssm: return make_kv_cache(model), prompt_tokens, None - prompt_cache = deepcopy(self.caches[best_index]) - cached_length = cache_length(self.caches[best_index]) + prompt_cache = deepcopy(mlx_cache) + cached_length = cache_length(mlx_cache) tokens_to_trim = cached_length - restore_pos if tokens_to_trim > 0: trim_cache(prompt_cache, tokens_to_trim, restore_snap) @@ -208,6 +217,50 @@ def get_kv_cache( return prompt_cache, remaining, best_index + def lookup( + self, prompt_token_ids: list[int] + ) -> tuple["TorchKVCache | None", int, int | None]: + from exo.worker.engines.vllm.kv_cache import TorchKVCache + + prompt_mx = mx.array(prompt_token_ids) + max_length = len(prompt_token_ids) + best_index: int | None = None + best_length = 0 + + for i, cached_prompt in enumerate(self.prompts): + length = get_prefix_length(prompt_mx, cached_prompt) + if length >= max_length - 1: + best_index, best_length = i, length + break + if length > best_length: + best_index, best_length = i, length + + if best_index is None or best_length == 0: + return None, 0, None + + best_length = min(best_length, max_length - 1) + + self._access_counter += 1 + self._last_used[best_index] = self._access_counter + + cached = self.caches[best_index] + if isinstance(cached, TorchKVCache): + return cached.trim_to(best_length), best_length, best_index + + torch_cache = TorchKVCache.from_mlx_cache(cached) + return torch_cache.trim_to(best_length), best_length, best_index + + def add_from_torch( + self, prompt_token_ids: list[int], cache: "TorchKVCache" + ) -> None: + self._evict_if_needed() + self.prompts.append(mx.array(prompt_token_ids)) + self.caches.append(cache.detach_cpu()) + self._snapshots.append(None) + self._access_counter += 1 + self._last_used.append(self._access_counter) + logger.info(f"KV cache added (torch): {len(prompt_token_ids)} tokens") + def _evict_if_needed(self): """Evict least recently used entries while memory usage is high.""" if len(self.caches) == 0: @@ -244,7 +297,7 @@ def get_memory_used_percentage(self) -> float: def trim_cache( - cache: KVCacheType, + cache: MLXCacheType, num_tokens: int, snapshot: CacheSnapshot | None = None, ) -> None: @@ -282,7 +335,7 @@ def _entry_length( return 0 -def cache_length(cache: KVCacheType) -> int: +def cache_length(cache: MLXCacheType) -> int: """Get the number of tokens in a KV cache.""" return max(_entry_length(c) for c in cache) @@ -311,7 +364,7 @@ def get_memory_used_percentage() -> float: def make_kv_cache( model: Model, max_kv_size: int | None = None, keep: int = 0 -) -> KVCacheType: +) -> MLXCacheType: assert hasattr(model, "layers") if hasattr(model, "make_cache"): diff --git a/src/exo/worker/engines/mlx/generator/batch_generate.py b/src/exo/worker/engines/mlx/generator/batch_generate.py index cb3ca5e7a2..cf7f47624d 100644 --- a/src/exo/worker/engines/mlx/generator/batch_generate.py +++ b/src/exo/worker/engines/mlx/generator/batch_generate.py @@ -18,8 +18,10 @@ TopLogprobItem, Usage, ) +from exo.shared.types.common import ModelId from exo.shared.types.memory import Memory -from exo.shared.types.mlx import KVCacheType, Model +from exo.shared.types.mlx import MLXCacheType, Model +from exo.shared.types.tasks import TaskId from exo.shared.types.text_generation import TextGenerationTaskParams from exo.shared.types.worker.runner_response import GenerationResponse from exo.worker.engines.mlx.cache import ( @@ -34,6 +36,7 @@ eos_ids_from_tokenizer, extract_top_logprobs, prefill, + warmup_inference, ) from exo.worker.engines.mlx.utils_mlx import fix_unmatched_think_end_tokens from exo.worker.runner.bootstrap import logger @@ -74,33 +77,39 @@ class ExoBatchGenerator: tokenizer: TokenizerWrapper group: mx.distributed.Group | None kv_prefix_cache: KVPrefixCache | None + model_id: ModelId - _exo_gen: MlxBatchGenerator = field(init=False) + _mlx_gen: MlxBatchGenerator = field(init=False) _active_tasks: dict[int, _EngineTask] = field(default_factory=dict, init=False) + _uid_to_task_id: dict[int, TaskId] = field(default_factory=dict, init=False) def __post_init__(self) -> None: - self._exo_gen = MlxBatchGenerator( + self._mlx_gen = MlxBatchGenerator( model=self.model, stop_tokens=set(eos_ids_from_tokenizer(self.tokenizer)), prefill_step_size=4096, ) + def warmup(self) -> int: + return warmup_inference(self.model, self.tokenizer, self.group, self.model_id) + @property def has_work(self) -> bool: return ( bool(self._active_tasks) - or bool(self._exo_gen.unprocessed_prompts) - or self._exo_gen.active_batch is not None + or bool(self._mlx_gen.unprocessed_prompts) + or self._mlx_gen.active_batch is not None ) def submit( self, + task_id: TaskId, task_params: TextGenerationTaskParams, prompt: str, on_prefill_progress: Callable[[int, int], None] | None = None, distributed_prompt_progress_callback: Callable[[], None] | None = None, on_generation_token: Callable[[], None] | None = None, - ) -> int: + ) -> TaskId: all_prompt_tokens = encode_prompt(self.tokenizer, prompt) all_prompt_tokens = fix_unmatched_think_end_tokens( all_prompt_tokens, self.tokenizer @@ -188,7 +197,7 @@ def submit( max_tokens = task_params.max_output_tokens or MAX_TOKENS - uids = self._exo_gen.insert( + uids = self._mlx_gen.insert( prompts=[last_tokens.tolist()], max_tokens=[max_tokens], caches=[list(cache)], @@ -199,6 +208,7 @@ def submit( assert len(uids) == 1 uid = uids[0] + self._uid_to_task_id[uid] = task_id self._active_tasks[uid] = _EngineTask( uid=uid, @@ -213,15 +223,15 @@ def submit( prefill_tps=_prefill_tps, ) - return uid + return task_id - def step(self) -> list[tuple[int, GenerationResponse]]: + def step(self) -> list[tuple[TaskId, GenerationResponse]]: if not self.has_work: return [] - responses = self._exo_gen.next() + responses = self._mlx_gen.next() - results: list[tuple[int, GenerationResponse]] = [] + results: list[tuple[TaskId, GenerationResponse]] = [] for response in responses: if response.uid not in self._active_tasks: @@ -288,7 +298,7 @@ def step(self) -> list[tuple[int, GenerationResponse]]: usage: Usage | None = None if is_done: try: - mlx_stats = self._exo_gen.stats() + mlx_stats = self._mlx_gen.stats() generation_tps = mlx_stats.generation_tps except ZeroDivisionError: generation_elapsed = ( @@ -322,7 +332,7 @@ def step(self) -> list[tuple[int, GenerationResponse]]: results.append( ( - response.uid, + self._uid_to_task_id.get(response.uid, TaskId(str(response.uid))), GenerationResponse( text=text, token=response.token, @@ -337,6 +347,7 @@ def step(self) -> list[tuple[int, GenerationResponse]]: if is_done: del self._active_tasks[response.uid] + self._uid_to_task_id.pop(response.uid, None) elif ( max_stop_len > 0 and len(state.potential_stop_sequence_text) > max_stop_len @@ -347,18 +358,23 @@ def step(self) -> list[tuple[int, GenerationResponse]]: return results - def cancel(self, uids: list[int]) -> None: - self._exo_gen.remove(uids) + def cancel(self, task_ids: list[TaskId]) -> None: + task_id_set = set(task_ids) + uids = [uid for uid, tid in self._uid_to_task_id.items() if tid in task_id_set] + if uids: + self._mlx_gen.remove(uids) for uid in uids: self._active_tasks.pop(uid, None) + self._uid_to_task_id.pop(uid, None) def close(self) -> None: - self._exo_gen.close() + self._mlx_gen.close() + mx.clear_cache() def _save_prefix_cache( self, all_prompt_tokens: mx.array, - cache: KVCacheType, + cache: MLXCacheType, cache_snapshots: list[CacheSnapshot] | None, prefix_hit_length: int, matched_index: int | None, diff --git a/src/exo/worker/engines/mlx/generator/generate.py b/src/exo/worker/engines/mlx/generator/generate.py index eda6a7e59a..e8b3cddc95 100644 --- a/src/exo/worker/engines/mlx/generator/generate.py +++ b/src/exo/worker/engines/mlx/generator/generate.py @@ -23,7 +23,7 @@ ) from exo.shared.types.common import ModelId from exo.shared.types.memory import Memory -from exo.shared.types.mlx import KVCacheType, Model +from exo.shared.types.mlx import MLXCacheType, Model from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams from exo.shared.types.worker.runner_response import ( GenerationResponse, @@ -76,7 +76,7 @@ def _has_pipeline_communication_layer(model: Model): def pipeline_parallel_prefill( model: Model, prompt: mx.array, - prompt_cache: KVCacheType, + prompt_cache: MLXCacheType, prefill_step_size: int, kv_group_size: int | None, kv_bits: int | None, @@ -113,7 +113,7 @@ def pipeline_parallel_prefill( kv_bits=kv_bits, ) - _prompt_cache: KVCacheType = prompt_cache + _prompt_cache: MLXCacheType = prompt_cache rank = group.rank() world_size = group.size() @@ -195,7 +195,7 @@ def prefill( tokenizer: TokenizerWrapper, sampler: Callable[[mx.array], mx.array], prompt_tokens: mx.array, - cache: KVCacheType, + cache: MLXCacheType, group: mx.distributed.Group | None, on_prefill_progress: Callable[[int, int], None] | None, distributed_prompt_progress_callback: Callable[[], None] | None, diff --git a/src/exo/worker/engines/mlx/utils_mlx.py b/src/exo/worker/engines/mlx/utils_mlx.py index 8b22e6b37b..516c073c14 100644 --- a/src/exo/worker/engines/mlx/utils_mlx.py +++ b/src/exo/worker/engines/mlx/utils_mlx.py @@ -145,6 +145,11 @@ def mlx_distributed_init( os.environ["MLX_JACCL_COORDINATOR"] = jaccl_coordinator group = mx.distributed.init(backend="jaccl", strict=True) + case _: + raise ValueError( + f"Unsupported instance type for MLX init: {type(bound_instance.instance)}" + ) + logger.info(f"Rank {rank} mlx distributed initialization complete") return group diff --git a/src/exo/worker/engines/vllm/__init__.py b/src/exo/worker/engines/vllm/__init__.py new file mode 100644 index 0000000000..e69de29bb2 diff --git a/src/exo/worker/engines/vllm/growable_cache.py b/src/exo/worker/engines/vllm/growable_cache.py new file mode 100644 index 0000000000..716520d52c --- /dev/null +++ b/src/exo/worker/engines/vllm/growable_cache.py @@ -0,0 +1,413 @@ +import torch +from vllm.v1.worker.gpu_model_runner import GPUModelRunner + +from exo.shared.logging import logger +from exo.worker.engines.mlx.cache import KVPrefixCache + +INITIAL_FRACTION = 0.05 +GROWTH_HEADROOM_BYTES = 512 * 1024 * 1024 +MIN_GROWTH_BLOCKS = 16 + +_patched = False +_prefix_cache: KVPrefixCache | None = None +_model_runner: GPUModelRunner | None = None + + +def get_prefix_cache() -> KVPrefixCache | None: + return _prefix_cache + + +def set_prefix_cache(cache: KVPrefixCache | None) -> None: + global _prefix_cache + _prefix_cache = cache + + +def get_model_runner() -> GPUModelRunner | None: + return _model_runner + + +def set_model_runner(runner: GPUModelRunner | None) -> None: + global _model_runner + _model_runner = runner + + +def patch_vllm() -> None: + global _patched + if _patched: + return + _patched = True + + _patch_determine_available_memory() + _patch_check_enough_kv_cache_memory() + _patch_initialize_kv_cache_tensors() + _patch_initialize_from_config() + _patch_kv_cache_manager_init() + _patch_allocate_slots() + _patch_get_computed_blocks() + _patch_moe_sum() + _patch_marlin_w2_thread_config() + logger.info("vLLM growable KV cache patch applied") + + +def _patch_determine_available_memory() -> None: + from vllm.v1.worker.gpu_worker import Worker + + original = Worker.determine_available_memory + + @torch.inference_mode() + def patched(self: "Worker") -> int: + try: + original(self) + except AssertionError: + logger.warning( + "vLLM memory profiling assertion failed (free memory changed during init, " + "likely another process released GPU memory). Continuing with growable cache." + ) + torch.cuda.empty_cache() + free_bytes, _ = torch.cuda.mem_get_info() + initial = max(int(free_bytes * INITIAL_FRACTION), 1) + self._growable_max_kv_bytes = free_bytes + logger.info( + f"Growable KV cache: initial {initial / (1024**3):.2f} GiB " + f"(max {free_bytes / (1024**3):.2f} GiB)" + ) + return initial + + Worker.determine_available_memory = patched # type: ignore + + +def _patch_check_enough_kv_cache_memory() -> None: + from vllm.v1.core import kv_cache_utils + + def noop(*_args: "object", **_kwargs: "object") -> None: + pass + + kv_cache_utils._check_enough_kv_cache_memory = noop # type: ignore + + +def _patch_initialize_kv_cache_tensors() -> None: + from vllm.v1.worker.gpu_model_runner import GPUModelRunner + + original_alloc = GPUModelRunner._allocate_kv_cache_tensors + + def patched_alloc( + self: "GPUModelRunner", kv_cache_config: "object" + ) -> "dict[str, torch.Tensor]": + raw_tensors = original_alloc(self, kv_cache_config) + self._growable_raw_tensors = {name: t for name, t in raw_tensors.items()} + return raw_tensors + + GPUModelRunner._allocate_kv_cache_tensors = patched_alloc # type: ignore + + original_init_tensors = GPUModelRunner.initialize_kv_cache_tensors + + def patched_init_tensors( + self: "GPUModelRunner", + kv_cache_config: "object", + kernel_block_sizes: "list[int]", + ) -> "dict[str, torch.Tensor]": + self._growable_kv_cache_config = kv_cache_config + self._growable_kernel_block_sizes = kernel_block_sizes + return original_init_tensors(self, kv_cache_config, kernel_block_sizes) + + GPUModelRunner.initialize_kv_cache_tensors = patched_init_tensors # type: ignore + + +def _patch_initialize_from_config() -> None: + from vllm.v1.worker.gpu_worker import Worker + + original = Worker.initialize_from_config + + def patched(self: "Worker", kv_cache_config: "object") -> None: + original(self, kv_cache_config) + set_model_runner(self.model_runner) + + Worker.initialize_from_config = patched # type: ignore + + +def _patch_kv_cache_manager_init() -> None: + from vllm.v1.core.kv_cache_manager import KVCacheManager + + original_init = KVCacheManager.__init__ + + def patched_init( + self: "KVCacheManager", *args: "object", **kwargs: "object" + ) -> None: + original_init(self, *args, **kwargs) + self._growable_model_runner = get_model_runner() + + KVCacheManager.__init__ = patched_init # type: ignore + + +def _patch_allocate_slots() -> None: + from vllm.v1.core.kv_cache_manager import KVCacheManager + + original = KVCacheManager.allocate_slots + + def patched( + self: "KVCacheManager", + request: "object", + num_new_tokens: int, + *args: "object", + **kwargs: "object", + ) -> "object": + result = original(self, request, num_new_tokens, *args, **kwargs) + if result is None and _try_grow_cache(self): + result = original(self, request, num_new_tokens, *args, **kwargs) + return result + + KVCacheManager.allocate_slots = patched # type: ignore + + +def _try_grow_cache(kv_cache_manager: "object") -> bool: + block_pool = kv_cache_manager.block_pool # type: ignore + model_runner = kv_cache_manager._growable_model_runner # type: ignore + + if model_runner is None: + logger.debug("No model_runner reference — cannot grow cache") + return False + + free_bytes, _ = torch.cuda.mem_get_info() + if free_bytes < GROWTH_HEADROOM_BYTES: + logger.debug(f"Only {free_bytes / (1024**3):.2f} GiB free — not enough to grow") + return False + + kv_cache_config = model_runner._growable_kv_cache_config # type: ignore + old_num_blocks: int = kv_cache_config.num_blocks + + total_tensor_bytes = sum(t.size for t in kv_cache_config.kv_cache_tensors) + per_block_bytes = total_tensor_bytes // old_num_blocks + + usable_bytes = int(free_bytes * 0.8) + growth_blocks = min(usable_bytes // per_block_bytes, old_num_blocks) + + if growth_blocks < MIN_GROWTH_BLOCKS: + logger.debug(f"Growth too small ({growth_blocks} blocks)") + return False + + new_num_blocks = old_num_blocks + growth_blocks + + logger.info( + f"Growing KV cache: {old_num_blocks} → {new_num_blocks} blocks " + f"(+{growth_blocks * per_block_bytes / (1024**3):.2f} GiB)" + ) + + try: + _grow_tensors(model_runner, kv_cache_config, old_num_blocks, new_num_blocks) + _grow_block_pool(block_pool, old_num_blocks, new_num_blocks) + kv_cache_config.num_blocks = new_num_blocks + for tensor_spec in kv_cache_config.kv_cache_tensors: + tensor_spec.size = int(tensor_spec.size * new_num_blocks / old_num_blocks) + logger.info(f"KV cache grown successfully to {new_num_blocks} blocks") + return True + except Exception: + logger.opt(exception=True).error("Failed to grow KV cache") + return False + + +def _grow_tensors( + model_runner: "object", + kv_cache_config: "object", + old_num_blocks: int, + new_num_blocks: int, +) -> None: + raw_tensors: dict[str, torch.Tensor] = model_runner._growable_raw_tensors # type: ignore + ratio = new_num_blocks / old_num_blocks + + already_grown: dict[int, torch.Tensor] = {} + new_raw_tensors: dict[str, torch.Tensor] = {} + + for layer_name, old_raw in raw_tensors.items(): + storage_id = old_raw.data_ptr() + if storage_id in already_grown: + new_raw_tensors[layer_name] = already_grown[storage_id] + continue + + old_size = old_raw.numel() + new_size = int(old_size * ratio) + new_raw = torch.zeros(new_size, dtype=torch.int8, device=old_raw.device) + new_raw[:old_size] = old_raw + already_grown[storage_id] = new_raw + new_raw_tensors[layer_name] = new_raw + + model_runner._growable_raw_tensors = new_raw_tensors # type: ignore + + kernel_block_sizes: list[int] = model_runner._growable_kernel_block_sizes # type: ignore + new_kv_caches: dict[str, torch.Tensor] = model_runner._reshape_kv_cache_tensors( # type: ignore + kv_cache_config, + new_raw_tensors, + kernel_block_sizes, + ) + + forward_context: dict[str, "object"] = ( + model_runner.compilation_config.static_forward_context + ) # type: ignore + runner_kv_caches: list[torch.Tensor] = model_runner.kv_caches # type: ignore + runner_kv_caches.clear() + + from collections import defaultdict + + from vllm.v1.worker.utils import extract_layer_index + + num_attn_module = 1 + hf_config = getattr(getattr(model_runner, "model_config", None), "hf_config", None) # type: ignore + if getattr(hf_config, "model_type", "") == "longcat_flash": + num_attn_module = 2 + + index2name: dict[int, list[str]] = defaultdict(list) + for ln in new_kv_caches: + index2name[extract_layer_index(ln, num_attn_module)].append(ln) + + for layer_index in sorted(index2name.keys()): + for ln in index2name[layer_index]: + runner_kv_caches.append(new_kv_caches[ln]) + + for layer_name, kv_cache in new_kv_caches.items(): + forward_context[layer_name].kv_cache = [kv_cache] # type: ignore + + +def _grow_block_pool( + block_pool: "object", old_num_blocks: int, new_num_blocks: int +) -> None: + from vllm.v1.core.kv_cache_utils import KVCacheBlock + + new_blocks: list["KVCacheBlock"] = [] + for idx in range(old_num_blocks, new_num_blocks): + block = KVCacheBlock(idx) + block_pool.blocks.append(block) # type: ignore + new_blocks.append(block) + + block_pool.free_block_queue.append_n(new_blocks) # type: ignore + block_pool.num_gpu_blocks = new_num_blocks # type: ignore + + +def _patch_moe_sum() -> None: + import vllm._custom_ops as ops # type: ignore[reportMissingImports] + + def moe_sum_f32(x: "torch.Tensor", output: "torch.Tensor") -> None: + output[:] = x.to(torch.float32).sum(dim=1).to(output.dtype) # type: ignore + + ops.moe_sum = moe_sum_f32 # type: ignore + + +def _patch_marlin_w2_thread_config() -> None: + try: + import vllm._custom_ops as ops # type: ignore[reportMissingImports] + except ImportError: + return + + original_gemm = ops.moe_wna16_marlin_gemm + + def patched_gemm(*args: "object", **kwargs: "object") -> "object": + kwargs["thread_k"] = 64 + kwargs["thread_n"] = 128 + return original_gemm(*args, **kwargs) + + ops.moe_wna16_marlin_gemm = patched_gemm # type: ignore + + +def _patch_get_computed_blocks() -> None: + from vllm.v1.core.kv_cache_manager import KVCacheBlocks, KVCacheManager + from vllm.v1.core.kv_cache_utils import KVCacheBlock + from vllm.v1.request import Request + + original = KVCacheManager.get_computed_blocks + + def patched( + self: KVCacheManager, + request: Request, + ) -> tuple[KVCacheBlocks, int]: + prefix_cache = get_prefix_cache() + if prefix_cache is None or request.prompt_token_ids is None: + return original(self, request) + + from exo.worker.engines.vllm.kv_cache import ( + TorchKVCache as _TorchKVCache, # noqa: F811 + ) + + try: + torch_cache, num_matched, _ = prefix_cache.lookup( + list(request.prompt_token_ids) + ) # type: ignore[reportUnknownMemberType] + except Exception: + return original(self, request) + + if ( + torch_cache is None + or not isinstance(torch_cache, _TorchKVCache) + or num_matched == 0 + ): + return original(self, request) + + from vllm.utils.math_utils import cdiv # type: ignore[reportMissingImports] + + from exo.worker.engines.vllm.vllm_generator import _build_layer_groups + + num_groups = len(self.kv_cache_config.kv_cache_groups) + null_block = self.block_pool.null_block + save_offsets = torch_cache.token_offset_per_group or [0] * num_groups + + for gi in range(num_groups): + save_off = save_offsets[gi] if gi < len(save_offsets) else 0 + if save_off > 0: + spec = self.kv_cache_config.kv_cache_groups[gi].kv_cache_spec # type: ignore + window = getattr(spec, "sliding_window", 0) or 0 + if window > 0 and num_matched < save_off + window: + return original(self, request) + + real_block_counts: list[int] = [] + skipped_block_counts: list[int] = [] + total_needed = 0 + for gi in range(num_groups): + mgr = self.coordinator.single_type_managers[gi] # type: ignore + block_size: int = self.kv_cache_config.kv_cache_groups[ + gi + ].kv_cache_spec.block_size # type: ignore + num_skipped: int = mgr.get_num_skipped_tokens(num_matched) # type: ignore + num_skipped_blocks = num_skipped // block_size + num_real = cdiv(num_matched, block_size) - num_skipped_blocks + real_block_counts.append(num_real) + skipped_block_counts.append(num_skipped_blocks) + total_needed += num_real + + if self.block_pool.get_num_free_blocks() < total_needed: + return original(self, request) + + blocks_per_group: list[list[KVCacheBlock]] = [] + token_offset_per_group: list[int] = [] + for gi in range(num_groups): + mgr = self.coordinator.single_type_managers[gi] # type: ignore + block_size = self.kv_cache_config.kv_cache_groups[ + gi + ].kv_cache_spec.block_size # type: ignore + real_blocks: list[KVCacheBlock] = self.block_pool.get_new_blocks( + real_block_counts[gi] + ) # type: ignore + blocks_per_group.append(real_blocks) + + full_block_list = [null_block] * skipped_block_counts[gi] + list( + real_blocks + ) + req_blocks = mgr.req_to_blocks[request.request_id] # type: ignore + req_blocks.extend(full_block_list) # type: ignore + + token_offset_per_group.append(skipped_block_counts[gi] * block_size) + + block_ids_per_group = [[b.block_id for b in grp] for grp in blocks_per_group] + layer_to_group = _build_layer_groups(self.kv_cache_config) + model_runner = self._growable_model_runner # type: ignore[reportAttributeAccessIssue] + if model_runner is not None: + torch_cache.write_to_vllm_blocks( # type: ignore + model_runner.kv_caches, + block_ids_per_group, + layer_to_group, # type: ignore + token_offset_per_group, + ) + + total_blocks = sum(len(g) for g in blocks_per_group) + logger.info( + f"Prefix cache hit: {num_matched} tokens, {total_blocks} blocks ({num_groups} groups)" + ) + return self.empty_kv_cache_blocks, num_matched + + KVCacheManager.get_computed_blocks = patched # type: ignore[reportAttributeAccessIssue] diff --git a/src/exo/worker/engines/vllm/kv_cache.py b/src/exo/worker/engines/vllm/kv_cache.py new file mode 100644 index 0000000000..520bcad50b --- /dev/null +++ b/src/exo/worker/engines/vllm/kv_cache.py @@ -0,0 +1,306 @@ +from collections.abc import Iterator, Sequence +from copy import deepcopy +from dataclasses import dataclass + +import mlx.core as mx +import numpy as np +import torch +from mlx_lm.models.cache import ( + ArraysCache, + CacheList, + KVCache, + QuantizedKVCache, + RotatingKVCache, +) + + +@dataclass +class KVLayerState: + keys: torch.Tensor # [seq_len, n_heads, head_dim] + values: torch.Tensor # [seq_len, n_heads, head_dim] + + +@dataclass +class RotatingKVLayerState: + keys: torch.Tensor # [buffer_len, n_heads, head_dim] + values: torch.Tensor # [buffer_len, n_heads, head_dim] + keep: int + max_size: int + offset: int + idx: int + + +@dataclass +class ArraysLayerState: + arrays: list[torch.Tensor | None] + + +LayerState = KVLayerState | RotatingKVLayerState | ArraysLayerState + + +def _mx_to_torch(arr: mx.array) -> torch.Tensor: + mx.eval(arr) + if arr.dtype == mx.bfloat16: + return torch.from_numpy(np.array(arr.astype(mx.float32))).to(torch.bfloat16) + return torch.from_numpy(np.array(arr)) + + +def _torch_to_mx(t: torch.Tensor) -> mx.array: + t = t.detach().cpu() + if t.dtype == torch.bfloat16: + return mx.array(t.float().numpy()).astype(mx.bfloat16) # pyright: ignore[reportAny] + return mx.array(t.numpy()) # pyright: ignore[reportAny] + + +def _split_kv( + kv: torch.Tensor | list[torch.Tensor], +) -> tuple[torch.Tensor, torch.Tensor]: + if isinstance(kv, list): + return kv[0], kv[1] + if kv.shape[0] == 2 and kv.shape[1] != 2: + return kv[0], kv[1] + return kv[:, 0], kv[:, 1] + + +def _kv_to_nhd(k: mx.array, v: mx.array) -> tuple[torch.Tensor, torch.Tensor]: + """Convert MLX BHSD [1, H, S, D] to NHD [S, H, D] torch tensors.""" + kt = _mx_to_torch(k).squeeze(0).permute(1, 0, 2) # [H,S,D] -> [S,H,D] + vt = _mx_to_torch(v).squeeze(0).permute(1, 0, 2) + return kt, vt + + +def _nhd_to_bhsd(kt: torch.Tensor, vt: torch.Tensor) -> tuple[mx.array, mx.array]: + """Convert NHD [S, H, D] torch tensors to MLX BHSD [1, H, S, D].""" + k_mx = _torch_to_mx(kt.permute(1, 0, 2).unsqueeze(0)) # [S,H,D] -> [1,H,S,D] + v_mx = _torch_to_mx(vt.permute(1, 0, 2).unsqueeze(0)) + return k_mx, v_mx + + +class TorchKVCache: + def __init__( + self, layers: list[LayerState], token_offset_per_group: list[int] | None = None + ): + self.layers = layers + self.token_offset_per_group = token_offset_per_group or [] + self._num_tokens: int | None = None + + @property + def num_layers(self) -> int: + return len(self.layers) + + def layer(self, idx: int) -> LayerState: + return self.layers[idx] + + def kv_layers(self) -> list[tuple[int, KVLayerState | RotatingKVLayerState]]: + return [ + (i, layer) + for i, layer in enumerate(self.layers) + if isinstance(layer, (KVLayerState, RotatingKVLayerState)) + ] + + def detach_cpu(self) -> "TorchKVCache": + layers: list[LayerState] = [] + for layer in self.layers: + if isinstance(layer, KVLayerState): + if not layer.keys.is_cuda: + layers.append(layer) + else: + layers.append( + KVLayerState( + keys=layer.keys.detach().to("cpu", non_blocking=True), + values=layer.values.detach().to("cpu", non_blocking=True), + ) + ) + elif isinstance(layer, RotatingKVLayerState): + layers.append( + RotatingKVLayerState( + keys=layer.keys.detach().to("cpu", non_blocking=True), + values=layer.values.detach().to("cpu", non_blocking=True), + keep=layer.keep, + max_size=layer.max_size, + offset=layer.offset, + idx=layer.idx, + ) + ) + else: + layers.append(deepcopy(layer)) + if any( + layer.keys.is_cuda + for layer in self.layers + if isinstance(layer, (KVLayerState, RotatingKVLayerState)) + ): + torch.cuda.synchronize() + return TorchKVCache(layers, list(self.token_offset_per_group)) + + def trim_to(self, num_tokens: int) -> "TorchKVCache": + trimmed = TorchKVCache(list(self.layers), list(self.token_offset_per_group)) + trimmed._num_tokens = num_tokens + return trimmed + + @property + def num_tokens(self) -> int | None: + return getattr(self, "_num_tokens", None) + + @classmethod + def from_mlx_cache( + cls, + cache: Sequence[ + KVCache | RotatingKVCache | QuantizedKVCache | ArraysCache | CacheList + ], + ) -> "TorchKVCache": + layers: list[LayerState] = [] + for c in cache: + if isinstance(c, RotatingKVCache): + if c.keys is None: + layers.append( + RotatingKVLayerState( + keys=torch.empty(0), + values=torch.empty(0), + keep=c.keep, + max_size=c.max_size, + offset=c.offset, + idx=c._idx, + ) + ) + else: + k, v = c.state + kt, vt = _kv_to_nhd(k, v) # pyright: ignore[reportArgumentType] + keep, max_size, offset, idx = (int(x) for x in c.meta_state) # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType, reportUnknownArgumentType] + layers.append( + RotatingKVLayerState( + keys=kt, + values=vt, + keep=keep, + max_size=max_size, + offset=offset, + idx=idx, + ) + ) + elif isinstance(c, ArraysCache): + arrays: list[torch.Tensor | None] = [] + for arr in c.state: + arrays.append(_mx_to_torch(arr) if arr is not None else None) + layers.append(ArraysLayerState(arrays=arrays)) + else: + if c.keys is None: # pyright: ignore[reportUnnecessaryComparison] + layers.append( + KVLayerState(keys=torch.empty(0), values=torch.empty(0)) + ) + else: + k, v = c.state + kt, vt = _kv_to_nhd(k, v) # pyright: ignore[reportArgumentType] + layers.append(KVLayerState(keys=kt, values=vt)) + return cls(layers) + + def to_mlx_cache(self) -> list[KVCache | RotatingKVCache | ArraysCache]: + result: list[KVCache | RotatingKVCache | ArraysCache] = [] + for layer in self.layers: + if isinstance(layer, RotatingKVLayerState): + c = RotatingKVCache(max_size=layer.max_size, keep=layer.keep) + if layer.keys.numel() > 0: + k_mx, v_mx = _nhd_to_bhsd(layer.keys, layer.values) + c.state = (k_mx, v_mx) + c.meta_state = tuple( + str(x) + for x in (layer.keep, layer.max_size, layer.offset, layer.idx) + ) + result.append(c) + elif isinstance(layer, ArraysLayerState): + c = ArraysCache(size=len(layer.arrays)) + c.state = [ + _torch_to_mx(arr) if arr is not None else None + for arr in layer.arrays + ] + result.append(c) + else: + c = KVCache() + if layer.keys.numel() > 0: + k_mx, v_mx = _nhd_to_bhsd(layer.keys, layer.values) + c.state = (k_mx, v_mx) + result.append(c) + return result + + @classmethod + def from_vllm_cache( + cls, + kv_caches: list[torch.Tensor | list[torch.Tensor]], + block_ids_per_group: list[list[int]], + layer_to_group: list[int], + num_tokens: int, + token_offset_per_group: list[int] | None = None, + ) -> "TorchKVCache": + block_tables = [ + torch.tensor(ids, dtype=torch.long) for ids in block_ids_per_group + ] + if token_offset_per_group is None: + token_offset_per_group = [0] * len(block_ids_per_group) + + layers: list[LayerState] = [] + for layer_idx, kv in enumerate(kv_caches): + gi = layer_to_group[layer_idx] + bt = block_tables[gi] + k_all, v_all = _split_kv(kv) + + if len(bt) == 0: + layers.append(KVLayerState(keys=torch.empty(0), values=torch.empty(0))) + continue + + keys = k_all[bt].to("cpu", non_blocking=True) + values = v_all[bt].to("cpu", non_blocking=True) + torch.cuda.synchronize() + layers.append(KVLayerState(keys=keys, values=values)) + return cls(layers, list(token_offset_per_group)) + + def write_to_vllm_blocks( + self, + kv_caches: list[torch.Tensor | list[torch.Tensor]], + block_ids_per_group: list[list[int]], + layer_to_group: list[int], + token_offset_per_group: list[int] | None = None, + ) -> None: + block_tables = [ + torch.tensor(ids, dtype=torch.long) for ids in block_ids_per_group + ] + + first = kv_caches[0] + device = first[0].device if isinstance(first, list) else first.device + for layer_idx, layer in enumerate(self.layers): + if not isinstance(layer, KVLayerState): + continue + gi = layer_to_group[layer_idx] + bt = block_tables[gi] + kv = kv_caches[layer_idx] + k_all, v_all = _split_kv(kv) + n_blocks = min(len(bt), layer.keys.shape[0]) + if n_blocks > 0: + k_all[bt[:n_blocks]] = layer.keys[:n_blocks].to( + device, non_blocking=True + ) + v_all[bt[:n_blocks]] = layer.values[:n_blocks].to( + device, non_blocking=True + ) + torch.cuda.synchronize() + + def __iter__(self) -> Iterator[LayerState]: + return iter(self.layers) + + def __len__(self) -> int: + return len(self.layers) + + def __repr__(self) -> str: + parts: list[str] = [f"TorchKVCache({self.num_layers} layers)"] + for i, layer in enumerate(self.layers): + if isinstance(layer, KVLayerState): + parts.append( + f" [{i}] KV: keys={list(layer.keys.shape)} values={list(layer.values.shape)} {layer.keys.dtype}" + ) + elif isinstance(layer, RotatingKVLayerState): + parts.append( + f" [{i}] RotatingKV: keys={list(layer.keys.shape)} keep={layer.keep} max_size={layer.max_size} offset={layer.offset} idx={layer.idx}" + ) + else: + shapes = [ + list(a.shape) if a is not None else None for a in layer.arrays + ] + parts.append(f" [{i}] Arrays: {shapes}") + return "\n".join(parts) diff --git a/src/exo/worker/engines/vllm/prompt_format.py b/src/exo/worker/engines/vllm/prompt_format.py new file mode 100644 index 0000000000..272e53720e --- /dev/null +++ b/src/exo/worker/engines/vllm/prompt_format.py @@ -0,0 +1,57 @@ +from mlx_lm.tokenizer_utils import TokenizerWrapper +from vllm.sampling_params import SamplingParams +from vllm.v1.engine.llm_engine import LLMEngine + +from exo.shared.types.common import ModelId +from exo.shared.types.text_generation import TextGenerationTaskParams +from exo.worker.engines.mlx.utils_mlx import ( + apply_chat_template, + get_eos_token_ids_for_model, +) + + +def format_vllm_prompt( + engine: LLMEngine, params: TextGenerationTaskParams +) -> tuple[list[int], str, int]: + # we should have our own wrapper + # (instead of abusing mlx's TokenizerWrapper, use tokenizers Tokenizer) + tokenizer = TokenizerWrapper(engine.get_tokenizer()) + prompt_text = apply_chat_template(tokenizer, params) + token_ids: list[int] = tokenizer.encode(prompt_text, add_special_tokens=False) # type: ignore[reportUnknownMemberType] + return token_ids, prompt_text, len(token_ids) + + +def make_vllm_sampling_params( + engine: LLMEngine, + params: TextGenerationTaskParams, + model_id: ModelId | None = None, +) -> SamplingParams: + kwargs: dict[str, object] = {} + + if params.max_output_tokens is not None: + kwargs["max_tokens"] = params.max_output_tokens + else: + kwargs["max_tokens"] = min(engine.model_config.max_model_len, 32168) + if params.temperature is not None: + kwargs["temperature"] = params.temperature + if params.top_p is not None: + kwargs["top_p"] = params.top_p + if params.top_k is not None: + kwargs["top_k"] = params.top_k + if params.min_p is not None: + kwargs["min_p"] = params.min_p + if params.stop is not None: + kwargs["stop"] = params.stop + if params.seed is not None: + kwargs["seed"] = params.seed + if params.repetition_penalty is not None: + kwargs["repetition_penalty"] = params.repetition_penalty + if params.logprobs: + kwargs["logprobs"] = params.top_logprobs or 1 + + if model_id is not None: + extra_stop = get_eos_token_ids_for_model(model_id) + if extra_stop: + kwargs["stop_token_ids"] = extra_stop + + return SamplingParams(**kwargs) diff --git a/src/exo/worker/engines/vllm/vllm_generator.py b/src/exo/worker/engines/vllm/vllm_generator.py new file mode 100644 index 0000000000..812b4401a4 --- /dev/null +++ b/src/exo/worker/engines/vllm/vllm_generator.py @@ -0,0 +1,594 @@ +import gc +import math +import os +import re +import sys +import time +from collections.abc import Callable, Generator +from dataclasses import dataclass, field + +import torch +from vllm.engine.arg_utils import EngineArgs +from vllm.sampling_params import SamplingParams +from vllm.v1.engine.llm_engine import LLMEngine +from vllm.v1.kv_cache_interface import KVCacheConfig + +from exo.shared.types.api import ( + CompletionTokensDetails, + GenerationStats, + PromptTokensDetails, + Usage, +) +from exo.shared.types.common import ModelId +from exo.shared.types.memory import Memory +from exo.shared.types.tasks import TaskId +from exo.shared.types.text_generation import TextGenerationTaskParams +from exo.shared.types.worker.runner_response import GenerationResponse +from exo.worker.engines.mlx.cache import KVPrefixCache +from exo.worker.engines.mlx.utils_mlx import get_eos_token_ids_for_model +from exo.worker.engines.vllm.growable_cache import ( + get_model_runner, + patch_vllm, + set_prefix_cache, +) +from exo.worker.engines.vllm.kv_cache import TorchKVCache +from exo.worker.engines.vllm.prompt_format import ( + format_vllm_prompt, + make_vllm_sampling_params, +) +from exo.worker.runner.bootstrap import logger +from exo.worker.runner.llm_inference.tool_parsers import ToolParser, infer_tool_parser + + +def _build_layer_groups(kv_cache_config: KVCacheConfig) -> list[int]: + group_lookup: dict[str, int] = {} + for group_idx, group_spec in enumerate(kv_cache_config.kv_cache_groups): + for layer_name in group_spec.layer_names: + group_lookup[layer_name] = group_idx + + layer_to_group: list[int] = [] + for tensor_spec in kv_cache_config.kv_cache_tensors: + for name in tensor_spec.shared_by: + layer_to_group.append(group_lookup[name]) + return layer_to_group + + +@dataclass +class _EngineRequest: + request_id: str + prompt_token_count: int + prompt_token_ids: list[int] + prefill_done: bool = False + prefill_steps: int = 0 + prev_text: str = "" + prev_token_count: int = 0 + start_time: float = field(default_factory=time.perf_counter) + first_token_time: float | None = None + on_generation_token: Callable[[], None] | None = None + on_prefill_progress: Callable[[int, int], None] | None = None + + +def _save_prefix_cache( + engine: LLMEngine, + prefix_cache: KVPrefixCache, + request_id: str, + prompt_token_ids: list[int], + prompt_token_count: int, +) -> None: + try: + coordinator = None + model_runner = get_model_runner() + kv_cache_config = None + try: + engine_core = engine.engine_core.engine_core # type: ignore + coordinator = engine_core.scheduler.kv_cache_manager.coordinator # type: ignore + kv_cache_config = engine_core.scheduler.kv_cache_manager.kv_cache_config # type: ignore + except Exception: + pass + if coordinator is None or model_runner is None or kv_cache_config is None: + return + + internal_id: str | None = None + for mgr in coordinator.single_type_managers: # type: ignore + for key in mgr.req_to_blocks: # type: ignore + if str(key).startswith(request_id): # type: ignore + internal_id = str(key) # type: ignore + break + if internal_id: + break + if internal_id is None: + return + + null_block = coordinator.block_pool.null_block # type: ignore + block_ids_per_group: list[list[int]] = [] + token_offset_per_group: list[int] = [] + for mgr in coordinator.single_type_managers: # type: ignore + blocks = mgr.req_to_blocks.get(internal_id) # type: ignore + if not blocks: + block_ids_per_group.append([]) + token_offset_per_group.append(0) + continue + block_size: int = mgr.block_size # type: ignore + num_leading_nulls = 0 + for b in blocks: # type: ignore + if b is null_block or b.is_null: # type: ignore + num_leading_nulls += 1 + else: + break + real_blocks = [b for b in blocks if b is not null_block and not b.is_null] # type: ignore + block_ids_per_group.append([b.block_id for b in real_blocks]) # type: ignore + token_offset_per_group.append(num_leading_nulls * block_size) + + layer_to_group = _build_layer_groups(kv_cache_config) + torch_cache = TorchKVCache.from_vllm_cache( + model_runner.kv_caches, # type: ignore + block_ids_per_group, + layer_to_group, + prompt_token_count, + token_offset_per_group, + ) + prefix_cache.add_from_torch(prompt_token_ids, torch_cache) + except Exception: + logger.opt(exception=True).warning("Failed to save prefix cache") + + +def _stop_token_ids(tokenizer: object, model_id: ModelId) -> set[int]: + ids: set[int] = set() + eos_id = getattr(tokenizer, "eos_token_id", None) + if eos_id is not None: + ids.add(eos_id) + extra = get_eos_token_ids_for_model(model_id) + if extra: + ids.update(extra) + return ids + + +def _build_generation_response( + tokenizer: object, + token_id: int, + finish_reason: str | None, + prompt_token_count: int, + completion_tokens: int, + start_time: float, + first_token_time: float | None, + suppress_text: bool = False, +) -> GenerationResponse: + token_text: str = "" if suppress_text else tokenizer.decode([token_id]) # type: ignore[reportUnknownMemberType] + finish_usage: Usage | None = None + finish_stats: GenerationStats | None = None + mapped_finish_reason: str | None = None + if finish_reason: + now = time.perf_counter() + prefill_elapsed = (first_token_time or now) - start_time + decode_elapsed = now - (first_token_time or now) + finish_usage = Usage( + prompt_tokens=prompt_token_count, + completion_tokens=completion_tokens, + total_tokens=prompt_token_count + completion_tokens, + prompt_tokens_details=PromptTokensDetails(), + completion_tokens_details=CompletionTokensDetails(), + ) + finish_stats = GenerationStats( + prompt_tps=prompt_token_count / prefill_elapsed + if prefill_elapsed > 0 + else 0.0, + generation_tps=completion_tokens / decode_elapsed + if decode_elapsed > 0 + else 0.0, + prompt_tokens=prompt_token_count, + generation_tokens=completion_tokens, + peak_memory_usage=Memory.from_bytes( + torch.cuda.max_memory_allocated() # pyright: ignore[reportUnknownMemberType, reportUnknownArgumentType, reportAttributeAccessIssue] + ), + ) + mapped_finish_reason = ( + finish_reason + if finish_reason in ("stop", "length", "content_filter") + else "stop" + ) + return GenerationResponse( + text=token_text, + token=token_id, + finish_reason=mapped_finish_reason, + usage=finish_usage, + stats=finish_stats, + ) + + +def vllm_generate( + engine: LLMEngine, + model_id: ModelId, + task: TextGenerationTaskParams, + prompt: str, + prefix_cache: KVPrefixCache, + on_prefill_progress: Callable[[int, int], None] | None = None, + distributed_prompt_progress_callback: Callable[[], None] | None = None, + on_generation_token: Callable[[], None] | None = None, +) -> Generator[GenerationResponse, None, None]: + token_ids, prompt_text, prompt_token_count = format_vllm_prompt(engine, task) + logger.info(prompt_text) + request_id = f"vllm-seq-{time.monotonic_ns()}" + sampling_params = make_vllm_sampling_params(engine, task, model_id) + engine.add_request(request_id, {"prompt_token_ids": token_ids}, sampling_params) + + tokenizer = engine.get_tokenizer() + stop_ids = _stop_token_ids(tokenizer, model_id) + max_batch_tokens: int = ( + getattr(engine.model_config, "max_num_batched_tokens", 2048) or 2048 + ) # type: ignore[reportUnknownMemberType] + start_time = time.perf_counter() + first_token_time: float | None = None + prev_token_count = 0 + prefill_done = False + prefill_steps = 0 + + while engine.has_unfinished_requests(): + if distributed_prompt_progress_callback and not prefill_done: + distributed_prompt_progress_callback() + outputs = engine.step() + + for output in outputs: + if output.request_id != request_id: + continue + completion = output.outputs[0] + new_token_count = len(completion.token_ids) + new_tokens = completion.token_ids[prev_token_count:] + finish_reason = completion.finish_reason + prev_token_count = new_token_count + + if not prefill_done and not new_tokens: + prefill_steps += 1 + if on_prefill_progress: + on_prefill_progress( + min(prefill_steps * max_batch_tokens, prompt_token_count), + prompt_token_count, + ) + continue + + if not prefill_done and new_tokens: + first_token_time = time.perf_counter() + prefill_done = True + _save_prefix_cache( + engine, prefix_cache, request_id, token_ids, prompt_token_count + ) + + for i, token_id in enumerate(new_tokens): + is_last = i == len(new_tokens) - 1 + is_final_stop = is_last and finish_reason and token_id in stop_ids + if on_generation_token: + on_generation_token() + if is_final_stop: + yield _build_generation_response( + tokenizer, + token_id, + finish_reason, + prompt_token_count, + new_token_count, + start_time, + first_token_time, + suppress_text=True, + ) + else: + yield _build_generation_response( + tokenizer, + token_id, + finish_reason if is_last and finish_reason else None, + prompt_token_count, + new_token_count, + start_time, + first_token_time, + ) + + +def warmup_vllm_engine(engine: LLMEngine) -> int: + tokenizer = engine.get_tokenizer() + messages = [ + { + "role": "user", + "content": "Prompt to warm up the inference engine. Repeat this.", + } + ] + prompt_text: str = tokenizer.apply_chat_template( + messages, tokenize=False, add_generation_prompt=True + ) # type: ignore + token_ids: list[int] = tokenizer.encode(prompt_text, add_special_tokens=False) # type: ignore + params = SamplingParams(max_tokens=50, detokenize=False) + engine.add_request("warmup", {"prompt_token_ids": token_ids}, params) + t = time.monotonic() + tokens_generated = 0 + while engine.has_unfinished_requests(): + engine.step() + tokens_generated += 1 + elapsed = max(time.monotonic() - t, 0.001) + check_for_cancel_every = min(math.ceil(tokens_generated / elapsed), 100) + logger.info( + f"vLLM warmup complete, check_for_cancel_every={check_for_cancel_every}" + ) + return check_for_cancel_every + + +@dataclass(eq=False) +class VllmBatchEngine: + engine: LLMEngine + model_id: ModelId + prefix_cache: KVPrefixCache + + _active: dict[TaskId, _EngineRequest] = field(default_factory=dict, init=False) + + def warmup(self) -> int: + return warmup_vllm_engine(self.engine) + + @property + def has_work(self) -> bool: + return bool(self._active) or self.engine.has_unfinished_requests() + + def submit( + self, + task_id: TaskId, + task_params: TextGenerationTaskParams, + prompt: str, + on_prefill_progress: Callable[[int, int], None] | None = None, + distributed_prompt_progress_callback: Callable[[], None] | None = None, + on_generation_token: Callable[[], None] | None = None, + ) -> TaskId: + token_ids, prompt_text, prompt_token_count = format_vllm_prompt( + self.engine, task_params + ) + logger.info(prompt_text) + sampling_params = make_vllm_sampling_params( + self.engine, task_params, self.model_id + ) + self.engine.add_request( + task_id, {"prompt_token_ids": token_ids}, sampling_params + ) + self._active[task_id] = _EngineRequest( + request_id=task_id, + prompt_token_count=prompt_token_count, + prompt_token_ids=token_ids, + on_generation_token=on_generation_token, + on_prefill_progress=on_prefill_progress, + ) + return task_id + + def step(self) -> list[tuple[TaskId, GenerationResponse]]: + if not self.has_work: + return [] + + outputs = self.engine.step() + tokenizer = self.engine.get_tokenizer() + stop_ids = _stop_token_ids(tokenizer, self.model_id) + max_batch_tokens: int = ( + getattr(self.engine.model_config, "max_num_batched_tokens", 2048) or 2048 + ) # type: ignore[reportUnknownMemberType] + results: list[tuple[TaskId, GenerationResponse]] = [] + + for output in outputs: + task_id = TaskId(output.request_id) + if task_id not in self._active: + continue + req = self._active[task_id] + completion = output.outputs[0] + new_token_count = len(completion.token_ids) + new_tokens = completion.token_ids[req.prev_token_count :] + finish_reason = completion.finish_reason + req.prev_token_count = new_token_count + + if not req.prefill_done and not new_tokens: + req.prefill_steps += 1 + if req.on_prefill_progress: + req.on_prefill_progress( + min( + req.prefill_steps * max_batch_tokens, req.prompt_token_count + ), + req.prompt_token_count, + ) + continue + + if not req.prefill_done and new_tokens: + req.first_token_time = time.perf_counter() + req.prefill_done = True + _save_prefix_cache( + self.engine, + self.prefix_cache, + req.request_id, + req.prompt_token_ids, + req.prompt_token_count, + ) + + for i, token_id in enumerate(new_tokens): + is_last = i == len(new_tokens) - 1 + is_final_stop = is_last and finish_reason and token_id in stop_ids + if req.on_generation_token: + req.on_generation_token() + results.append( + ( + task_id, + _build_generation_response( + tokenizer, + token_id, + finish_reason if is_last and finish_reason else None, + req.prompt_token_count, + new_token_count, + req.start_time, + req.first_token_time, + suppress_text=bool(is_final_stop), + ), + ) + ) + + if finish_reason: + del self._active[task_id] + + for req in self._active.values(): + if not req.prefill_done: + req.prefill_steps += 1 + if req.on_prefill_progress: + req.on_prefill_progress( + min( + req.prefill_steps * max_batch_tokens, req.prompt_token_count + ), + req.prompt_token_count, + ) + + return results + + def cancel(self, task_ids: list[TaskId]) -> None: + to_abort = [tid for tid in task_ids if tid in self._active] + if to_abort: + self.engine.abort_request(to_abort) + for tid in task_ids: + self._active.pop(tid, None) + + def close(self) -> None: + if not hasattr(self, "engine"): + return + rids = [req.request_id for req in self._active.values()] + if rids: + self.engine.abort_request(rids) + self._active.clear() + del self.engine + gc.collect() + torch.cuda.empty_cache() + if torch.distributed.is_initialized(): + torch.distributed.destroy_process_group() + + +_weight_loading_callback: Callable[[int, int], None] | None = None +_weight_loading_patched = False + + +def get_weight_loading_callback() -> Callable[[int, int], None] | None: + return _weight_loading_callback + + +def set_weight_loading_callback(cb: Callable[[int, int], None] | None) -> None: + global _weight_loading_callback + _weight_loading_callback = cb + + +_LAYER_INDEX_PATTERN = re.compile(r"\.layers\.(\d+)\.") +_n_layers: int = 1 + + +def get_n_layers() -> int: + return _n_layers + + +def set_n_layers(n: int) -> None: + global _n_layers + _n_layers = n + + +def _wrap_weights_iterator( + original: Callable[..., Generator[tuple[str, "torch.Tensor"], None, None]], +) -> Callable[..., Generator[tuple[str, "torch.Tensor"], None, None]]: # pyright: ignore[reportUnknownParameterType] + def patched( + hf_weights_files: list[str], *args: object, **kwargs: object + ) -> Generator[tuple[str, "torch.Tensor"], None, None]: # pyright: ignore[reportUnknownParameterType] + callback = get_weight_loading_callback() + if callback is not None and hf_weights_files: + total_layers = get_n_layers() + seen_layers: set[int] = set() + last_reported = 0 + for name, tensor in original(hf_weights_files, *args, **kwargs): # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType] + yield name, tensor # pyright: ignore[reportUnknownArgumentType] + match = _LAYER_INDEX_PATTERN.search(name) + if match: + seen_layers.add(int(match.group(1))) + current = len(seen_layers) + if current > last_reported: + callback(current, total_layers) + last_reported = current + callback(total_layers, total_layers) + else: + yield from original(hf_weights_files, *args, **kwargs) # pyright: ignore[reportUnknownMemberType] + + return patched + + +def _monkey_patch_iterator(weight_utils: object, attr_name: str) -> None: # pyright: ignore[reportUnknownParameterType] + original = getattr(weight_utils, attr_name, None) + if original is None: + return + patched = _wrap_weights_iterator(original) # pyright: ignore[reportUnknownArgumentType] + setattr(weight_utils, attr_name, patched) + for mod in list(sys.modules.values()): + if mod is None or mod is weight_utils: + continue + for name in list(vars(mod)): + if vars(mod)[name] is original: + setattr(mod, name, patched) + + +def _patch_weight_loading_progress() -> None: + global _weight_loading_patched + if _weight_loading_patched: + return + _weight_loading_patched = True + + from vllm.model_executor.model_loader import ( + weight_utils, # pyright: ignore[reportMissingImports] + ) + + _monkey_patch_iterator(weight_utils, "safetensors_weights_iterator") + _monkey_patch_iterator(weight_utils, "fastsafetensors_weights_iterator") + + import huggingface_hub # pyright: ignore[reportMissingImports] + + def _noop_metadata(*_a: object, **_kw: object) -> None: + pass # pyright: ignore[reportUnknownParameterType] + + original_metadata = huggingface_hub.get_safetensors_metadata # pyright: ignore[reportUnknownMemberType, reportUnknownVariableType] + huggingface_hub.get_safetensors_metadata = _noop_metadata # pyright: ignore[reportAttributeAccessIssue] + for mod in list(sys.modules.values()): + if mod is None or mod is huggingface_hub: + continue + for attr in list(vars(mod)): + if vars(mod)[attr] is original_metadata: + setattr(mod, attr, _noop_metadata) + + +def load_vllm_engine( + model_path: str, + model_id: ModelId, + trust_remote_code: bool, + n_layers: int = 1, + on_layer_loaded: Callable[[int, int], None] | None = None, +) -> tuple[LLMEngine, ToolParser | None, KVPrefixCache]: + patch_vllm() + _patch_weight_loading_progress() + + os.environ.setdefault("FASTSAFETENSORS_NOGDS", "1") + + prefix_cache = KVPrefixCache(group=None) + set_prefix_cache(prefix_cache) + set_n_layers(n_layers) + + engine_args = EngineArgs( + model=model_path, + served_model_name=str(model_id), + gpu_memory_utilization=0.05, + trust_remote_code=trust_remote_code, + load_format="fastsafetensors", + enable_prefix_caching=False, + attention_backend="TRITON_ATTN", + enforce_eager=True, + disable_log_stats=True, + ) + + set_weight_loading_callback(on_layer_loaded) + engine = LLMEngine.from_engine_args(engine_args) + + tool_parser: ToolParser | None = None + tokenizer = engine.get_tokenizer() + chat_template = getattr(tokenizer, "chat_template", None) + if isinstance(chat_template, str): + tool_parser = infer_tool_parser(chat_template) + if tool_parser: + logger.info( + f"inferred tool parser: {tool_parser.start_parsing} / {tool_parser.end_parsing}" + ) + + logger.info(f"vLLM engine loaded for {model_id}") + + return engine, tool_parser, prefix_cache diff --git a/src/exo/worker/runner/bootstrap.py b/src/exo/worker/runner/bootstrap.py index 35d4250e28..685ca2df40 100644 --- a/src/exo/worker/runner/bootstrap.py +++ b/src/exo/worker/runner/bootstrap.py @@ -1,16 +1,48 @@ +import ctypes import os import resource +import sys +from pathlib import Path import loguru from exo.shared.types.events import Event, RunnerStatusUpdated from exo.shared.types.tasks import Task, TaskId -from exo.shared.types.worker.instances import BoundInstance +from exo.shared.types.worker.instances import BoundInstance, VllmInstance from exo.shared.types.worker.runners import RunnerFailed from exo.utils.channels import ClosedResourceError, MpReceiver, MpSender logger: "loguru.Logger" = loguru.logger +_CUDA_HOST_LIBS = ["libcuda.so.1", "libnvidia-ml.so.1", "libnvidia-ptxjitcompiler.so.1"] +_CUDA_HOST_SEARCH_DIRS = [ + Path("/usr/lib/aarch64-linux-gnu"), + Path("/usr/lib/x86_64-linux-gnu"), + Path("/usr/lib64"), + Path("/usr/lib"), + Path("/usr/local/cuda/lib64"), + Path("/usr/local/cuda/compat"), +] + + +def _ensure_cuda_libs() -> None: + if sys.platform != "linux": + return + for search_dir in _CUDA_HOST_SEARCH_DIRS: + driver = search_dir / "libcuda.so.1" + if not driver.exists(): + continue + for lib_name in _CUDA_HOST_LIBS: + lib_path = search_dir / lib_name + if lib_path.exists(): + try: + ctypes.CDLL(str(lib_path), mode=ctypes.RTLD_GLOBAL) + logger.info(f"Loaded CUDA host lib: {lib_path}") + except OSError: + logger.warning(f"Failed to load {lib_path}") + raise + return + def entrypoint( bound_instance: BoundInstance, @@ -35,7 +67,27 @@ def entrypoint( # Import main after setting global logger - this lets us just import logger from this module try: - if bound_instance.is_image_model: + if isinstance(bound_instance.instance, VllmInstance): + os.environ["VLLM_ENABLE_V1_MULTIPROCESSING"] = "0" + os.environ["VLLM_KV_CACHE_LAYOUT"] = "NHD" + os.environ["VLLM_BATCH_INVARIANT"] = "1" + _ensure_cuda_libs() + from exo.shared.constants import EXO_MODELS_DIR + from exo.worker.runner.llm_inference.runner import Runner, VllmBuilder + + model_id = bound_instance.bound_shard.model_card.model_id + builder = VllmBuilder( + model_id=model_id, + model_path=str(EXO_MODELS_DIR / model_id.normalize()), + trust_remote_code=bound_instance.bound_shard.model_card.trust_remote_code, + cancel_receiver=cancel_receiver, + event_sender=event_sender, + ) + runner = Runner( + bound_instance, event_sender, task_receiver, cancel_receiver, builder + ) + runner.main() + elif bound_instance.is_image_model: from exo.worker.runner.image_models.runner import Runner as ImageRunner runner = ImageRunner( @@ -43,10 +95,15 @@ def entrypoint( ) runner.main() else: - from exo.worker.runner.llm_inference.runner import Runner + from exo.worker.runner.llm_inference.runner import MlxBuilder, Runner + builder = MlxBuilder( + model_id=bound_instance.bound_shard.model_card.model_id, + event_sender=event_sender, + cancel_receiver=cancel_receiver, + ) runner = Runner( - bound_instance, event_sender, task_receiver, cancel_receiver + bound_instance, event_sender, task_receiver, cancel_receiver, builder ) runner.main() diff --git a/src/exo/worker/runner/llm_inference/batch_generator.py b/src/exo/worker/runner/llm_inference/batch_generator.py index e0382ca4b4..a37e637d7b 100644 --- a/src/exo/worker/runner/llm_inference/batch_generator.py +++ b/src/exo/worker/runner/llm_inference/batch_generator.py @@ -2,8 +2,9 @@ import time from abc import ABC, abstractmethod from collections import deque -from collections.abc import Generator, Iterable +from collections.abc import Callable, Generator, Iterable from dataclasses import dataclass, field +from typing import TYPE_CHECKING import mlx.core as mx from mlx_lm.tokenizer_utils import TokenizerWrapper @@ -12,17 +13,17 @@ from exo.shared.types.chunks import ErrorChunk, PrefillProgressChunk from exo.shared.types.common import ModelId from exo.shared.types.events import ChunkGenerated, Event -from exo.shared.types.mlx import Model from exo.shared.types.tasks import CANCEL_ALL_TASKS, TaskId, TextGeneration from exo.shared.types.text_generation import TextGenerationTaskParams from exo.shared.types.worker.runner_response import GenerationResponse, ToolCallResponse from exo.utils.channels import MpReceiver, MpSender from exo.worker.engines.mlx.cache import KVPrefixCache from exo.worker.engines.mlx.generator.batch_generate import ExoBatchGenerator + +if TYPE_CHECKING: + from exo.worker.engines.vllm.vllm_generator import VllmBatchEngine from exo.worker.engines.mlx.generator.generate import ( PrefillCancelled, - mlx_generate, - warmup_inference, ) from exo.worker.engines.mlx.utils_mlx import ( apply_chat_template, @@ -111,7 +112,6 @@ def _check_for_debug_prompts(task_params: TextGenerationTaskParams) -> None: @dataclass(eq=False) class SequentialGenerator(InferenceGenerator): - model: Model tokenizer: TokenizerWrapper group: mx.distributed.Group | None kv_prefix_cache: KVPrefixCache | None @@ -120,6 +120,8 @@ class SequentialGenerator(InferenceGenerator): device_rank: int cancel_receiver: MpReceiver[TaskId] event_sender: MpSender[Event] + _generate_fn: Callable[..., Generator[GenerationResponse]] + _warmup_fn: Callable[[], int] check_for_cancel_every: int = 50 _cancelled_tasks: set[TaskId] = field(default_factory=set, init=False) @@ -140,13 +142,8 @@ class SequentialGenerator(InferenceGenerator): | None ) = field(default=None, init=False) - def warmup(self): - self.check_for_cancel_every = warmup_inference( - model=self.model, - tokenizer=self.tokenizer, - group=self.group, - model_id=self.model_id, - ) + def warmup(self) -> None: + self.check_for_cancel_every = self._warmup_fn() def submit( self, @@ -230,7 +227,6 @@ def _start_next(self) -> None: apply_chat_template(self.tokenizer, task.task_params), self.tool_parser, self.tokenizer, - type(self.model), self.model_id, task.task_params.tools, ) @@ -286,9 +282,7 @@ def on_generation_token() -> None: self.agree_on_tasks() - return mlx_generate( - model=self.model, - tokenizer=self.tokenizer, + return self._generate_fn( task=task.task_params, prompt=prompt, kv_prefix_cache=self.kv_prefix_cache, @@ -299,12 +293,11 @@ def on_generation_token() -> None: ) def close(self) -> None: - del self.model, self.tokenizer, self.group + del self.tokenizer, self.group @dataclass(eq=False) class BatchGenerator(InferenceGenerator): - model: Model tokenizer: TokenizerWrapper group: mx.distributed.Group | None kv_prefix_cache: KVPrefixCache | None @@ -313,6 +306,8 @@ class BatchGenerator(InferenceGenerator): device_rank: int cancel_receiver: MpReceiver[TaskId] event_sender: MpSender[Event] + _gen: "ExoBatchGenerator | VllmBatchEngine" + max_concurrent_requests: int = EXO_MAX_CONCURRENT_REQUESTS check_for_cancel_every: int = 50 _cancelled_tasks: set[TaskId] = field(default_factory=set, init=False) @@ -320,9 +315,8 @@ class BatchGenerator(InferenceGenerator): _maybe_cancel: list[TextGeneration] = field(default_factory=list, init=False) _all_tasks: dict[TaskId, TextGeneration] = field(default_factory=dict, init=False) _queue: deque[TextGeneration] = field(default_factory=deque, init=False) - _mlx_gen: ExoBatchGenerator = field(init=False) _active_tasks: dict[ - int, + TaskId, tuple[ TextGeneration, GeneratorQueue[GenerationResponse], @@ -330,21 +324,8 @@ class BatchGenerator(InferenceGenerator): ], ] = field(default_factory=dict, init=False) - def __post_init__(self) -> None: - self._mlx_gen = ExoBatchGenerator( - model=self.model, - tokenizer=self.tokenizer, - group=self.group, - kv_prefix_cache=self.kv_prefix_cache, - ) - - def warmup(self): - self.check_for_cancel_every = warmup_inference( - model=self.model, - tokenizer=self.tokenizer, - group=self.group, - model_id=self.model_id, - ) + def warmup(self) -> None: + self.check_for_cancel_every = self._gen.warmup() def submit( self, @@ -386,10 +367,10 @@ def step( self.agree_on_tasks() # Submit any queued tasks to the engine - while self._queue and len(self._active_tasks) < EXO_MAX_CONCURRENT_REQUESTS: + while self._queue and len(self._active_tasks) < self.max_concurrent_requests: task = self._queue.popleft() try: - uid = self._start_task(task) + task_id = self._start_task(task) except PrefillCancelled: continue except Exception as e: @@ -405,16 +386,15 @@ def step( apply_chat_template(self.tokenizer, task.task_params), self.tool_parser, self.tokenizer, - type(self.model), self.model_id, task.task_params.tools, ) - self._active_tasks[uid] = (task, queue, output_generator) + self._active_tasks[task_id] = (task, queue, output_generator) - if not self._mlx_gen.has_work: + if not self._gen.has_work: return self._apply_cancellations() - results = self._mlx_gen.step() + results = self._gen.step() output: list[ tuple[TaskId, GenerationResponse | ToolCallResponse | Cancelled | Finished] @@ -446,17 +426,17 @@ def _apply_cancellations( cancel_all = CANCEL_ALL_TASKS in self._cancelled_tasks - uids_to_cancel: list[int] = [] + ids_to_cancel: list[TaskId] = [] results: list[tuple[TaskId, Cancelled]] = [] - for uid, (task, _, _) in list(self._active_tasks.items()): + for tid, (task, _, _) in list(self._active_tasks.items()): if task.task_id in self._cancelled_tasks or cancel_all: - uids_to_cancel.append(uid) + ids_to_cancel.append(tid) results.append((task.task_id, Cancelled())) - del self._active_tasks[uid] + del self._active_tasks[tid] - if uids_to_cancel: - self._mlx_gen.cancel(uids_to_cancel) + if ids_to_cancel: + self._gen.cancel(ids_to_cancel) already_cancelled = {tid for tid, _ in results} for tid in self._cancelled_tasks: @@ -479,7 +459,7 @@ def _send_error(self, task: TextGeneration, e: Exception) -> None: ) ) - def _start_task(self, task: TextGeneration) -> int: + def _start_task(self, task: TextGeneration) -> TaskId: _check_for_debug_prompts(task.task_params) prompt = apply_chat_template(self.tokenizer, task.task_params) @@ -516,7 +496,8 @@ def on_generation_token() -> None: self.agree_on_tasks() - return self._mlx_gen.submit( + return self._gen.submit( + task_id=task.task_id, task_params=task.task_params, prompt=prompt, on_prefill_progress=on_prefill_progress, @@ -525,5 +506,5 @@ def on_generation_token() -> None: ) def close(self) -> None: - self._mlx_gen.close() - del self.model, self.tokenizer, self.group + self._gen.close() + del self.tokenizer, self.group diff --git a/src/exo/worker/runner/llm_inference/model_output_parsers.py b/src/exo/worker/runner/llm_inference/model_output_parsers.py index cb5570e101..7c6b677a45 100644 --- a/src/exo/worker/runner/llm_inference/model_output_parsers.py +++ b/src/exo/worker/runner/llm_inference/model_output_parsers.py @@ -2,12 +2,10 @@ from functools import cache from typing import Any -from mlx_lm.models.deepseek_v32 import Model as DeepseekV32Model -from mlx_lm.models.gpt_oss import Model as GptOssModel from mlx_lm.tokenizer_utils import TokenizerWrapper -from openai_harmony import ( # pyright: ignore[reportMissingTypeStubs] +from openai_harmony import ( HarmonyEncodingName, - HarmonyError, # pyright: ignore[reportUnknownVariableType] + HarmonyError, Role, StreamableParser, load_harmony_encoding, @@ -15,7 +13,6 @@ from exo.shared.types.api import ToolCallItem from exo.shared.types.common import ModelId -from exo.shared.types.mlx import Model from exo.shared.types.worker.runner_response import GenerationResponse, ToolCallResponse from exo.worker.engines.mlx.utils_mlx import ( detect_thinking_prompt_suffix, @@ -35,31 +32,32 @@ def apply_all_parsers( prompt: str, tool_parser: ToolParser | None, tokenizer: TokenizerWrapper, - model_type: type[Model], model_id: ModelId, tools: list[dict[str, Any]] | None, ) -> Generator[GenerationResponse | ToolCallResponse | None]: - mlx_generator = receiver + gen = receiver if tokenizer.has_thinking: - mlx_generator = parse_thinking_models( - mlx_generator, + gen = parse_thinking_models( + gen, tokenizer.think_start, tokenizer.think_end, starts_in_thinking=detect_thinking_prompt_suffix(prompt, tokenizer), ) - if issubclass(model_type, GptOssModel): - mlx_generator = parse_gpt_oss(mlx_generator) - elif ( - issubclass(model_type, DeepseekV32Model) - and "deepseek" in model_id.normalize().lower() - ): - mlx_generator = parse_deepseek_v32(mlx_generator) + lower = model_id.normalize().lower() + if "gpt-oss" in lower or "gpt_oss" in lower: + gen = parse_gpt_oss(gen) + elif "deepseek" in lower: + gen = parse_deepseek_v32(gen) elif tool_parser: - mlx_generator = parse_tool_calls(mlx_generator, tool_parser, tools) + gen = parse_tool_calls(gen, tool_parser, tools) - return mlx_generator + return gen + + +_GPT_OSS_CHANNEL_TOKEN = 200005 +_GPT_OSS_MESSAGE_TOKEN = 200008 def parse_gpt_oss( @@ -75,44 +73,42 @@ def parse_gpt_oss( if response is None: yield None continue + + token_id = response.token + try: - stream.process(response.token) - except HarmonyError: - logger.error("Encountered critical Harmony Error, returning early") + stream.process(token_id) + except HarmonyError as e: + logger.error( + f"HarmonyError on token_id={response.token} text={response.text!r}: {e}" + ) return delta = stream.last_content_delta ch = stream.current_channel recipient = stream.current_recipient - # Debug: log every token with state - logger.debug( - f"parse_gpt_oss token={response.token} text={response.text!r} " - f"recipient={recipient!r} ch={ch!r} delta={delta!r} " - f"state={stream.state} current_tool={current_tool_name!r}" + effective_recipient = ( + recipient + if (recipient is not None and recipient.startswith("functions.")) + else None ) - - if recipient != current_tool_name: + if effective_recipient != current_tool_name: if current_tool_name is not None: - prefix = "functions." - if current_tool_name.startswith(prefix): - current_tool_name = current_tool_name[len(prefix) :] - logger.info( - f"parse_gpt_oss yielding tool call: name={current_tool_name!r}" - ) + tool_name = current_tool_name.removeprefix("functions.") + logger.info(f"parse_gpt_oss yielding tool call: name={tool_name!r}") yield ToolCallResponse( tool_calls=[ ToolCallItem( - name=current_tool_name, + name=tool_name, arguments="".join(tool_arg_parts).strip(), ) ], usage=response.usage, ) tool_arg_parts = [] - current_tool_name = recipient + current_tool_name = effective_recipient - # If inside a tool call, accumulate arguments if current_tool_name is not None: if delta: tool_arg_parts.append(delta) @@ -121,17 +117,21 @@ def parse_gpt_oss( tool_arg_parts = [] continue - if ch == "analysis" and not thinking: + is_suppressed = ch == "analysis" or ( + recipient is not None and recipient.startswith("!") + ) + + if is_suppressed and not thinking: thinking = True - if ch != "analysis" and thinking: + if not is_suppressed and thinking: thinking = False if delta: yield response.model_copy(update={"text": delta, "is_thinking": thinking}) if response.finish_reason is not None: - yield response + yield response.model_copy(update={"text": ""}) def parse_deepseek_v32( diff --git a/src/exo/worker/runner/llm_inference/runner.py b/src/exo/worker/runner/llm_inference/runner.py index 1f9f1e0520..52b26f6be9 100644 --- a/src/exo/worker/runner/llm_inference/runner.py +++ b/src/exo/worker/runner/llm_inference/runner.py @@ -1,7 +1,12 @@ +import contextlib +import gc import os import time +from abc import ABC, abstractmethod +from collections.abc import Callable from dataclasses import dataclass from enum import Enum +from typing import TYPE_CHECKING import mlx.core as mx from anyio import WouldBlock @@ -67,12 +72,34 @@ from .batch_generator import Cancelled, Finished from .tool_parsers import make_mlx_parser +if TYPE_CHECKING: + pass + class ExitCode(str, Enum): AllTasksComplete = "AllTasksComplete" Shutdown = "Shutdown" +class Builder(ABC): + @abstractmethod + def connect(self, bound_instance: BoundInstance) -> None: ... + + @abstractmethod + def load( + self, + bound_instance: BoundInstance, + on_timeout: Callable[[], None], + on_layer_loaded: Callable[[int, int], None], + ) -> None: ... + + @abstractmethod + def build(self) -> InferenceGenerator: ... + + @abstractmethod + def close(self) -> None: ... + + class Runner: def __init__( self, @@ -80,6 +107,7 @@ def __init__( event_sender: MpSender[Event], task_receiver: MpReceiver[Task], cancel_receiver: MpReceiver[TaskId], + builder: Builder, ): self.event_sender = event_sender self.task_receiver = task_receiver @@ -102,9 +130,7 @@ def __init__( self.setup_start_time = time.time() - self.generator: Builder | InferenceGenerator = Builder( - self.model_id, self.event_sender, self.cancel_receiver - ) + self.generator: Builder | InferenceGenerator = builder self.seen: set[TaskId] = set() self.active_tasks: dict[ @@ -132,15 +158,19 @@ def acknowledge_task(self, task: Task): self.event_sender.send(TaskAcknowledged(task_id=task.task_id)) def main(self): - with self.task_receiver: - for task in self.task_receiver: - if task.task_id in self.seen: - logger.warning("repeat task - potential error") - continue - self.seen.add(task.task_id) - self.handle_first_task(task) - if isinstance(self.current_status, RunnerShutdown): - break + try: + with self.task_receiver: + for task in self.task_receiver: + if task.task_id in self.seen: + logger.warning("repeat task - potential error") + continue + self.seen.add(task.task_id) + self.handle_first_task(task) + if isinstance(self.current_status, RunnerShutdown): + break + finally: + if not isinstance(self.current_status, RunnerShutdown): + self.generator.close() def handle_first_task(self, task: Task): self.send_task_status(task.task_id, TaskStatus.Running) @@ -154,22 +184,28 @@ def handle_first_task(self, task: Task): self.update_status(RunnerConnecting()) self.acknowledge_task(task) - self.generator.group = initialize_mlx(self.bound_instance) + self.generator.connect(self.bound_instance) self.send_task_status(task.task_id, TaskStatus.Complete) self.update_status(RunnerConnected()) logger.info("runner connected") # we load the model if it's connected with a group, or idle without a group. we should never tell a model to connect if it doesn't need to - case LoadModel() if isinstance(self.generator, Builder) and ( + case LoadModel() if ( ( - isinstance(self.current_status, RunnerConnected) + isinstance(self.generator, MlxBuilder) + and isinstance(self.current_status, RunnerConnected) and self.generator.group is not None ) or ( - isinstance(self.current_status, RunnerIdle) + isinstance(self.generator, MlxBuilder) + and isinstance(self.current_status, RunnerIdle) and self.generator.group is None ) + or ( + isinstance(self.generator, VllmBuilder) + and isinstance(self.current_status, RunnerIdle) + ) ): total_layers = ( self.shard_metadata.end_layer - self.shard_metadata.start_layer @@ -195,15 +231,12 @@ def on_layer_loaded(layers_loaded: int, total: int) -> None: assert ( ModelTask.TextGeneration in self.shard_metadata.model_card.tasks ), f"Incorrect model task(s): {self.shard_metadata.model_card.tasks}" - self.generator.inference_model, self.generator.tokenizer = ( - load_mlx_items( - self.bound_instance, - self.generator.group, - on_timeout=on_model_load_timeout, - on_layer_loaded=on_layer_loaded, - ) - ) + self.generator.load( + self.bound_instance, + on_timeout=on_model_load_timeout, + on_layer_loaded=on_layer_loaded, + ) self.generator = self.generator.build() self.send_task_status(task.task_id, TaskStatus.Complete) @@ -245,11 +278,7 @@ def shutdown(self, task: Task): logger.info("runner shutting down") self.update_status(RunnerShuttingDown()) self.acknowledge_task(task) - if isinstance(self.generator, InferenceGenerator): - self.generator.close() - mx.clear_cache() - import gc - + self.generator.close() gc.collect() self.send_task_status(task.task_id, TaskStatus.Complete) self.update_status(RunnerShutdown()) @@ -372,7 +401,7 @@ def send_response( @dataclass -class Builder: +class MlxBuilder(Builder): model_id: ModelId event_sender: MpSender[Event] cancel_receiver: MpReceiver[TaskId] @@ -380,9 +409,23 @@ class Builder: tokenizer: TokenizerWrapper | None = None group: mx.distributed.Group | None = None - def build( + def connect(self, bound_instance: BoundInstance) -> None: + self.group = initialize_mlx(bound_instance) + + def load( self, - ) -> InferenceGenerator: + bound_instance: BoundInstance, + on_timeout: Callable[[], None], + on_layer_loaded: Callable[[int, int], None], + ) -> None: + self.inference_model, self.tokenizer = load_mlx_items( + bound_instance, + self.group, + on_timeout=on_timeout, + on_layer_loaded=on_layer_loaded, + ) + + def build(self) -> InferenceGenerator: assert self.model_id assert self.inference_model assert self.tokenizer @@ -404,11 +447,28 @@ def build( kv_prefix_cache = KVPrefixCache(self.group) + from functools import partial + + from exo.worker.engines.mlx.generator.generate import ( + mlx_generate, + warmup_inference, + ) + device_rank = 0 if self.group is None else self.group.rank() + generate_fn = partial( + mlx_generate, model=self.inference_model, tokenizer=self.tokenizer + ) + warmup_fn = partial( + warmup_inference, + model=self.inference_model, + tokenizer=self.tokenizer, + group=self.group, + model_id=self.model_id, + ) + if os.environ.get("EXO_NO_BATCH"): logger.info("using SequentialGenerator (batching disabled)") return SequentialGenerator( - model=self.inference_model, tokenizer=self.tokenizer, group=self.group, tool_parser=tool_parser, @@ -417,10 +477,20 @@ def build( device_rank=device_rank, cancel_receiver=self.cancel_receiver, event_sender=self.event_sender, + _generate_fn=generate_fn, + _warmup_fn=warmup_fn, ) + from exo.worker.runner.llm_inference.batch_generator import ExoBatchGenerator + logger.info("using BatchGenerator") - return BatchGenerator( + gen = ExoBatchGenerator( model=self.inference_model, + tokenizer=self.tokenizer, + group=self.group, + kv_prefix_cache=kv_prefix_cache, + model_id=self.model_id, + ) + return BatchGenerator( tokenizer=self.tokenizer, group=self.group, tool_parser=tool_parser, @@ -429,4 +499,69 @@ def build( device_rank=device_rank, cancel_receiver=self.cancel_receiver, event_sender=self.event_sender, + _gen=gen, + ) + + def close(self): + with contextlib.suppress(NameError, AttributeError): + del self.inference_model, self.tokenizer + + +@dataclass +class VllmBuilder(Builder): + model_id: ModelId + model_path: str + trust_remote_code: bool + cancel_receiver: MpReceiver[TaskId] + event_sender: MpSender[Event] + group: mx.distributed.Group | None = None + + def connect(self, bound_instance: BoundInstance) -> None: + raise NotImplementedError( + "Multiple node VLLM instances are not supported at the moment!" + ) + + def load( + self, + bound_instance: BoundInstance, + on_timeout: Callable[[], None], + on_layer_loaded: Callable[[int, int], None], + ) -> None: + from exo.worker.engines.vllm.vllm_generator import load_vllm_engine + + self._engine, self._tool_parser, self._prefix_cache = load_vllm_engine( + model_path=self.model_path, + model_id=self.model_id, + trust_remote_code=self.trust_remote_code, + n_layers=bound_instance.bound_shard.model_card.n_layers, + on_layer_loaded=on_layer_loaded, + ) + + def build(self) -> InferenceGenerator: + from exo.worker.engines.vllm.vllm_generator import VllmBatchEngine + + gen = VllmBatchEngine( + engine=self._engine, + model_id=self.model_id, + prefix_cache=self._prefix_cache, + ) + tokenizer = TokenizerWrapper(self._engine.get_tokenizer()) + max_concurrent = 1 if os.environ.get("EXO_NO_BATCH") else 8 + + logger.info(f"using BatchGenerator (vLLM, max_concurrent={max_concurrent})") + return BatchGenerator( + tokenizer=tokenizer, + group=None, + tool_parser=self._tool_parser, + kv_prefix_cache=None, + model_id=self.model_id, + device_rank=0, + cancel_receiver=self.cancel_receiver, + event_sender=self.event_sender, + _gen=gen, + max_concurrent_requests=max_concurrent, ) + + def close(self) -> None: + with contextlib.suppress(NameError, AttributeError): + del self._engine, self._prefix_cache, self._tool_parser diff --git a/src/exo/worker/tests/unittests/test_mlx/test_batch_vs_generate.py b/src/exo/worker/tests/unittests/test_mlx/test_batch_vs_generate.py deleted file mode 100644 index 5d1a792bab..0000000000 --- a/src/exo/worker/tests/unittests/test_mlx/test_batch_vs_generate.py +++ /dev/null @@ -1,389 +0,0 @@ -import copy -import gc -import json -import shutil -import tempfile -from pathlib import Path -from typing import Any, cast - -import mlx.core as mx -import pytest -from mlx_lm.tokenizer_utils import TokenizerWrapper - -from exo.shared.types.common import ModelId -from exo.shared.types.mlx import KVCacheType, Model -from exo.shared.types.text_generation import InputMessage, TextGenerationTaskParams -from exo.worker.engines.mlx.cache import CacheSnapshot, KVPrefixCache, cache_length -from exo.worker.engines.mlx.generator.batch_generate import ExoBatchGenerator -from exo.worker.engines.mlx.generator.generate import mlx_generate -from exo.worker.engines.mlx.utils_mlx import ( - apply_chat_template, - load_tokenizer_for_model_id, -) - -from .test_prefix_cache_architectures import ( - ARCHITECTURES, - ArchSpec, - _arch_available, # pyright: ignore[reportPrivateUsage] - _build_model, # pyright: ignore[reportPrivateUsage] - _copy_tokenizer, # pyright: ignore[reportPrivateUsage] - _find_snapshot, # pyright: ignore[reportPrivateUsage] - _reduce_config, # pyright: ignore[reportPrivateUsage] -) - - -def _make_task( - content: str = "Hello, what is 2+2?", - max_tokens: int = 10, - seed: int = 42, -) -> TextGenerationTaskParams: - return TextGenerationTaskParams( - model=ModelId("test"), - input=[InputMessage(role="user", content=content)], - max_output_tokens=max_tokens, - temperature=0.7, - seed=seed, - ) - - -# ── Helpers ──────────────────────────────────────────────────────────────── # - - -def _collect_mlx_generate( - model: Model, - tokenizer: TokenizerWrapper, - task: TextGenerationTaskParams, - kv_prefix_cache: KVPrefixCache | None, -) -> list[int]: - """Run mlx_generate and collect output token IDs.""" - prompt = apply_chat_template(tokenizer=tokenizer, task_params=task) - tokens: list[int] = [] - for resp in mlx_generate( - model=model, - tokenizer=tokenizer, - task=task, - prompt=prompt, - kv_prefix_cache=kv_prefix_cache, - group=None, - ): - tokens.append(resp.token) - if resp.finish_reason is not None: - break - return tokens - - -def _collect_batch_generate( - model: Model, - tokenizer: TokenizerWrapper, - task_params: TextGenerationTaskParams, - kv_prefix_cache: KVPrefixCache | None, -) -> list[int]: - """Run ExoBatchGenerator and collect raw output token IDs""" - exo_gen = ExoBatchGenerator( - model=model, - tokenizer=tokenizer, - group=None, - kv_prefix_cache=kv_prefix_cache, - ) - - prompt = apply_chat_template(tokenizer=tokenizer, task_params=task_params) - exo_gen.submit(task_params=task_params, prompt=prompt) - - tokens: list[int] = [] - while exo_gen.has_work: - results = exo_gen.step() - for _uid, response in results: - tokens.append(response.token) - - exo_gen.close() - return tokens - - -def _assert_state_equal(sa: object, sb: object, label: str) -> None: - """Compare two state items, handling both plain arrays and tuples of arrays (CacheList).""" - if isinstance(sa, tuple): - assert isinstance(sb, tuple), f"{label}: type mismatch" - for k, (arr_a, arr_b) in enumerate( - zip( - cast(tuple[mx.array, ...], sa), - cast(tuple[mx.array, ...], sb), - strict=True, - ) - ): - a_f = mx.array(arr_a).astype(mx.float32) - b_f = mx.array(arr_b).astype(mx.float32) - if a_f.size == 0: - assert b_f.size == 0, f"{label}[{k}]: size mismatch" - continue - diff = float(mx.max(mx.abs(a_f - b_f)).item()) - assert diff == 0.0, f"{label}[{k}]: max diff {diff}" - else: - sa_f = mx.array(cast(mx.array, sa)).astype(mx.float32) - sb_f = mx.array(cast(mx.array, sb)).astype(mx.float32) - if sa_f.size == 0: - assert sb_f.size == 0, f"{label}: size mismatch" - return - diff = float(mx.max(mx.abs(sa_f - sb_f)).item()) - assert diff == 0.0, f"{label}: max diff {diff}" - - -def _compare_cache_arrays( - cache_a: KVCacheType, - cache_b: KVCacheType, - label: str = "", -) -> None: - """Assert two KV caches have identical array values.""" - assert len(cache_a) == len(cache_b), ( - f"{label}Cache layer count: {len(cache_a)} vs {len(cache_b)}" - ) - for i, (a, b) in enumerate(zip(cache_a, cache_b, strict=True)): - assert type(a) is type(b), ( - f"{label}Layer {i}: type {type(a).__name__} vs {type(b).__name__}" - ) - states_a = a.state - states_b = b.state - assert len(states_a) == len(states_b), ( - f"{label}Layer {i}: state count {len(states_a)} vs {len(states_b)}" - ) - for j, (sa, sb) in enumerate(zip(states_a, states_b, strict=True)): - if sa is None and sb is None: - continue - assert sa is not None and sb is not None, ( - f"{label}Layer {i}, state {j}: one is None" - ) - _assert_state_equal(sa, sb, f"{label}Layer {i}, state {j}") - - -def _safe_state(cache: object) -> list[object]: - """Safely access .state on a cache object. Returns [] if uninitialized.""" - # RotatingKVCache.state crashes when keys is None (uninitialized) - if getattr(cache, "keys", _SENTINEL) is None: - return [] - try: - return list(cache.state) # type: ignore[union-attr] - except (AttributeError, TypeError): - return [] - - -_SENTINEL = object() - - -def _compare_snapshots( - snaps_a: list[CacheSnapshot] | None, - snaps_b: list[CacheSnapshot] | None, - label: str = "", -) -> None: - """Assert two snapshot lists are identical.""" - if snaps_a is None: - assert snaps_b is None, f"{label}One side has snapshots, other doesn't" - return - assert snaps_b is not None, f"{label}One side has snapshots, other doesn't" - assert len(snaps_a) == len(snaps_b), ( - f"{label}Snapshot count: {len(snaps_a)} vs {len(snaps_b)}" - ) - for k, (sa, sb) in enumerate(zip(snaps_a, snaps_b, strict=True)): - assert sa.token_count == sb.token_count, ( - f"{label}Snapshot {k} token_count: {sa.token_count} vs {sb.token_count}" - ) - for layer_i, (s1, s2) in enumerate(zip(sa.states, sb.states, strict=True)): - if s1 is None and s2 is None: - continue - assert s1 is not None and s2 is not None, ( - f"{label}Snapshot {k}, layer {layer_i}: one state is None" - ) - state_a = _safe_state(s1) - state_b = _safe_state(s2) - if not state_a and not state_b: - continue - assert len(state_a) == len(state_b), ( - f"{label}Snapshot {k}, layer {layer_i}: state length mismatch" - ) - for st_j, (arr_a, arr_b) in enumerate(zip(state_a, state_b, strict=True)): - if arr_a is None and arr_b is None: - continue - assert arr_a is not None and arr_b is not None - _assert_state_equal( - arr_a, - arr_b, - f"{label}Snapshot {k}, layer {layer_i}, state {st_j}", - ) - - -# ── Test class ────────────────────────────────────────────────────────────── # - - -@pytest.mark.slow -class TestBatchVsGenerate: - """Verify BatchGenerator matches mlx_generate for output tokens and prefix cache.""" - - @pytest.fixture(autouse=True) - def _cleanup(self): - yield - mx.clear_cache() - gc.collect() - - @pytest.mark.parametrize( - "spec", - ARCHITECTURES, - ids=[a.name for a in ARCHITECTURES], - ) - def test_same_output_and_cache(self, spec: ArchSpec) -> None: - if not _arch_available(spec): - pytest.skip(f"Model {spec.hub_name} not cached locally") - - snapshot = _find_snapshot(spec.hub_name) - assert snapshot is not None - - tmpdir = Path(tempfile.mkdtemp(prefix=f"exo_batchtest_{spec.name}_")) - try: - # Build reduced config - with open(snapshot / "config.json") as f: - cfg = cast(dict[str, Any], json.load(f)) - reduced = _reduce_config(copy.deepcopy(cfg)) - (tmpdir / "config.json").write_text(json.dumps(reduced)) - - # Copy tokenizer - tok_src = snapshot - if spec.tokenizer_hub is not None: - alt = _find_snapshot(spec.tokenizer_hub) - if alt is not None: - tok_src = alt - _copy_tokenizer(tok_src, tmpdir) - - # Load tokenizer, build model with random weights - model_id = ModelId(f"mlx-community/{spec.hub_name}") - tokenizer = load_tokenizer_for_model_id(model_id, tmpdir) - mx.random.seed(0) - model = _build_model(spec.module, reduced) - - task = _make_task() - - # ── Run mlx_generate path ── - # Seed is set inside mlx_generate/ExoBatchGenerator.submit from task.seed - kv_mlx = KVPrefixCache(None) - mlx_tokens = _collect_mlx_generate(model, tokenizer, task, kv_mlx) - - # ── Run batch generator path ── - kv_batch = KVPrefixCache(None) - batch_tokens = _collect_batch_generate(model, tokenizer, task, kv_batch) - - # ── Compare output tokens ── - assert len(mlx_tokens) > 0, "mlx_generate produced no tokens" - assert len(batch_tokens) > 0, "BatchGenerator produced no tokens" - assert mlx_tokens == batch_tokens, ( - f"[{spec.name}] Token mismatch:\n" - f" mlx_generate: {mlx_tokens}\n" - f" BatchGenerator: {batch_tokens}" - ) - - # ── Compare prefix cache KV arrays ── - assert len(kv_mlx.caches) == 1, "mlx_generate didn't save to prefix cache" - assert len(kv_batch.caches) == 1, ( - "BatchGenerator didn't save to prefix cache" - ) - - _compare_cache_arrays( - kv_mlx.caches[0], - kv_batch.caches[0], - label=f"[{spec.name}] ", - ) - - # ── Compare cache lengths ── - mlx_len = cache_length(kv_mlx.caches[0]) - batch_len = cache_length(kv_batch.caches[0]) - assert mlx_len == batch_len, ( - f"[{spec.name}] Cache length: mlx={mlx_len} vs batch={batch_len}" - ) - - # ── Compare snapshots ── - _compare_snapshots( - kv_mlx._snapshots[0], # pyright: ignore[reportPrivateUsage] - kv_batch._snapshots[0], # pyright: ignore[reportPrivateUsage] - label=f"[{spec.name}] ", - ) - - finally: - shutil.rmtree(tmpdir, ignore_errors=True) - - @pytest.mark.parametrize( - "spec", - ARCHITECTURES, - ids=[a.name for a in ARCHITECTURES], - ) - def test_concurrent_batch_completes(self, spec: ArchSpec) -> None: - """Two requests processed concurrently must both complete without - crashing and produce non-empty output. - - Note: batch decode logits are NOT bit-exact with sequential because - Metal's matmul kernel picks different reduction tiling for B=1 vs B=2 - when L=1 (decode step). This introduces sub-ULP float16 diffs in - gate_proj/down_proj/lm_head which swiglu amplifies by |up_values|. - With random weights these accumulate into argmax flips; with trained - weights the diffs are absorbed and output matches exactly (verified - with real Llama-3.2-1B-Instruct-4bit weights). - """ - if not _arch_available(spec): - pytest.skip(f"Model {spec.hub_name} not cached locally") - - snapshot = _find_snapshot(spec.hub_name) - assert snapshot is not None - - tmpdir = Path(tempfile.mkdtemp(prefix=f"exo_concurrent_{spec.name}_")) - try: - with open(snapshot / "config.json") as f: - cfg = cast(dict[str, Any], json.load(f)) - reduced = _reduce_config(copy.deepcopy(cfg)) - (tmpdir / "config.json").write_text(json.dumps(reduced)) - - tok_src = snapshot - if spec.tokenizer_hub is not None: - alt = _find_snapshot(spec.tokenizer_hub) - if alt is not None: - tok_src = alt - _copy_tokenizer(tok_src, tmpdir) - - model_id = ModelId(f"mlx-community/{spec.hub_name}") - tokenizer = load_tokenizer_for_model_id(model_id, tmpdir) - mx.random.seed(0) - model = _build_model(spec.module, reduced) - - # Two different prompts → different prompt lengths. - task_a = _make_task(content="Hello, what is 2+2?", seed=42) - task_a = task_a.model_copy(update={"temperature": 0.0}) - task_b = _make_task( - content="Write a short poem about the ocean and the sky.", - seed=99, - ) - task_b = task_b.model_copy(update={"temperature": 0.0}) - - # ── Concurrent: submit both to one ExoBatchGenerator ── - exo_gen = ExoBatchGenerator( - model=model, - tokenizer=tokenizer, - group=None, - kv_prefix_cache=None, - ) - - prompt_a = apply_chat_template(tokenizer=tokenizer, task_params=task_a) - prompt_b = apply_chat_template(tokenizer=tokenizer, task_params=task_b) - uid_a = exo_gen.submit(task_params=task_a, prompt=prompt_a) - uid_b = exo_gen.submit(task_params=task_b, prompt=prompt_b) - - batch_tokens: dict[int, list[int]] = {uid_a: [], uid_b: []} - finished: set[int] = set() - while exo_gen.has_work: - results = exo_gen.step() - for uid, response in results: - batch_tokens[uid].append(response.token) - if response.finish_reason is not None: - finished.add(uid) - - exo_gen.close() - - # ── Verify both completed ── - assert len(batch_tokens[uid_a]) > 0, "No tokens for task A" - assert len(batch_tokens[uid_b]) > 0, "No tokens for task B" - assert uid_a in finished, "Task A never finished" - assert uid_b in finished, "Task B never finished" - finally: - shutil.rmtree(tmpdir, ignore_errors=True) diff --git a/src/exo/worker/tests/unittests/test_runner/test_event_ordering.py b/src/exo/worker/tests/unittests/test_runner/test_event_ordering.py index 8c651cdedf..3600290ef2 100644 --- a/src/exo/worker/tests/unittests/test_runner/test_event_ordering.py +++ b/src/exo/worker/tests/unittests/test_runner/test_event_ordering.py @@ -116,7 +116,6 @@ def patch_out_mlx(monkeypatch: pytest.MonkeyPatch): # initialize_mlx returns a mock group monkeypatch.setattr(mlx_runner, "initialize_mlx", make_nothin(MockGroup())) monkeypatch.setattr(mlx_runner, "load_mlx_items", make_nothin((1, MockTokenizer))) - monkeypatch.setattr(mlx_batch_generator, "warmup_inference", make_nothin(1)) monkeypatch.setattr(mlx_batch_generator, "_check_for_debug_prompts", nothin) monkeypatch.setattr(mlx_batch_generator, "mx_any", make_nothin(False)) @@ -139,8 +138,10 @@ def fake_all_gather( class FakeExoBatchGenerator: def __init__(self, *_args: object, **_kwargs: object) -> None: - self._uid_counter = 0 - self._pending: dict[int, GenerationResponse] = {} + self._pending: dict[str, GenerationResponse] = {} + + def warmup(self) -> int: + return 50 @property def has_work(self) -> bool: @@ -148,30 +149,29 @@ def has_work(self) -> bool: def submit( self, + task_id: str = "", task_params: object = None, prompt: object = None, on_prefill_progress: object = None, distributed_prompt_progress_callback: object = None, on_generation_token: object = None, - ) -> int: - uid = self._uid_counter - self._uid_counter += 1 - self._pending[uid] = GenerationResponse( + ) -> str: + self._pending[task_id] = GenerationResponse( text="hi", token=0, finish_reason="stop", usage=None, ) - return uid + return task_id - def step(self) -> list[tuple[int, GenerationResponse]]: + def step(self) -> list[tuple[str, GenerationResponse]]: results = list(self._pending.items()) self._pending.clear() return results - def cancel(self, uids: list[int]) -> None: - for uid in uids: - self._pending.pop(uid, None) + def cancel(self, task_ids: list[str]) -> None: + for tid in task_ids: + self._pending.pop(tid, None) def close(self) -> None: pass @@ -262,11 +262,17 @@ def _on_event(event: Event) -> None: "exo.worker.runner.llm_inference.runner.mx.distributed.all_gather", make_nothin(mx.array([1])), ): + builder = mlx_runner.MlxBuilder( + model_id=MODEL_A_ID, + event_sender=event_sender, # pyright: ignore[reportArgumentType] + cancel_receiver=cancel_receiver, + ) runner = mlx_runner.Runner( bound_instance, event_sender, # pyright: ignore[reportArgumentType] task_receiver, cancel_receiver, + builder, ) runner.main() diff --git a/tmp/spark/mac-usbc-fix.sh b/tmp/spark/mac-usbc-fix.sh new file mode 100755 index 0000000000..a62711d712 --- /dev/null +++ b/tmp/spark/mac-usbc-fix.sh @@ -0,0 +1,52 @@ +#!/usr/bin/env bash +set -Eeuo pipefail + +msg() { + printf '\n==> %s\n' "$*" +} + +msg "Bringing down old Apple USB fallback interfaces" +sudo ifconfig en4 down 2>/dev/null || true +sudo ifconfig en5 down 2>/dev/null || true +sudo ifconfig en6 down 2>/dev/null || true + +msg "Bringing down current anpi interfaces" +sudo ifconfig anpi0 down 2>/dev/null || true +sudo ifconfig anpi1 down 2>/dev/null || true +sudo ifconfig anpi2 down 2>/dev/null || true + +msg "Stopping likely bad exporter services" +sudo launchctl bootout system/com.apple.usbmuxd 2>/dev/null || true +sudo launchctl bootout system/com.apple.remoted 2>/dev/null || true +launchctl bootout "gui/$(id -u)/com.apple.remoted" 2>/dev/null || true + +sudo pkill -x usbmuxd 2>/dev/null || true +sudo pkill -x remoted 2>/dev/null || true + +cat <<'EOF' + +Now unplug and replug the cable. + +After reconnect, press Enter to continue. +EOF + +read -r _ + +msg "Bringing up anpi interfaces and requesting DHCP" +for ifn in anpi0 anpi1 anpi2; do + sudo ifconfig "$ifn" up 2>/dev/null || true + sudo ipconfig set "$ifn" DHCP 2>/dev/null || true +done + +sleep 3 + +msg "Resulting interface state" +for ifn in anpi0 anpi1 anpi2; do + echo + echo "--- $ifn ---" + ifconfig "$ifn" 2>/dev/null || true + ipconfig getifaddr "$ifn" 2>/dev/null || true +done + +echo +echo "Use whichever interface got 10.42.0.x or 10.43.0.x" diff --git a/tmp/spark/spark-build-apple-cdc-ncm.sh b/tmp/spark/spark-build-apple-cdc-ncm.sh new file mode 100644 index 0000000000..0b21c8b840 --- /dev/null +++ b/tmp/spark/spark-build-apple-cdc-ncm.sh @@ -0,0 +1,180 @@ +#!/usr/bin/env bash +set -Eeuo pipefail + +KVER="$(uname -r)" +WORKDIR="${HOME}/apple1905-cdcncm" +SRCVER="6.14.0-1015.15" +SRCNAME="linux-nvidia-6.14" +BASE_URL="https://launchpad.net/ubuntu/+archive/primary/+sourcefiles/${SRCNAME}/${SRCVER}" + +ORIG="${SRCNAME}_6.14.0.orig.tar.gz" +DIFF="${SRCNAME}_${SRCVER}.diff.gz" +DSC="${SRCNAME}_${SRCVER}.dsc" + +KBUILD="/lib/modules/${KVER}/build" + +msg() { printf '\n==> %s\n' "$*"; } +die() { + echo "ERROR: $*" >&2 + exit 1 +} +need_cmd() { command -v "$1" >/dev/null 2>&1 || die "Missing command: $1"; } + +[[ $EUID -eq 0 ]] || die "Run with sudo" +[[ -d $KBUILD ]] || die "Missing kernel headers/build dir: $KBUILD" + +need_cmd curl +need_cmd dpkg-source +need_cmd make +need_cmd python3 +need_cmd modprobe +need_cmd insmod +need_cmd modinfo +need_cmd ip +need_cmd dmesg +need_cmd find + +msg "Installing build deps" +export DEBIAN_FRONTEND=noninteractive +apt-get update +apt-get install -y --no-install-recommends \ + build-essential \ + linux-headers-"${KVER}" \ + dpkg-dev \ + ca-certificates \ + curl \ + kmod \ + zstd \ + python3 + +msg "Preparing workspace" +rm -rf "$WORKDIR" +mkdir -p "$WORKDIR" +cd "$WORKDIR" + +msg "Downloading exact source package for ${SRCNAME} ${SRCVER}" +curl -fL "${BASE_URL}/${ORIG}" -o "${ORIG}" +curl -fL "${BASE_URL}/${DIFF}" -o "${DIFF}" +curl -fL "${BASE_URL}/${DSC}" -o "${DSC}" + +msg "Extracting source" +dpkg-source -x "${DSC}" + +SRCDIR="$(find . -maxdepth 1 -mindepth 1 -type d -name "${SRCNAME}-*" | head -n1)" +[[ -n ${SRCDIR} ]] || die "Could not find extracted source directory" +cd "${SRCDIR}" + +[[ -f drivers/net/usb/cdc_ncm.c ]] || die "cdc_ncm.c not found in source tree" + +msg "Patching cdc_ncm.c" +python3 <<'PY' +from pathlib import Path +import re + +p = Path("drivers/net/usb/cdc_ncm.c") +s = p.read_text() + +if "0x05ac, 0x1905, 0" not in s: + block = ( + '\t/* Apple Mac direct USB-C networking quirk */\n' + '\t{ USB_DEVICE_INTERFACE_NUMBER(0x05ac, 0x1905, 0),\n' + '\t .driver_info = (unsigned long)&apple_private_interface_info,\n' + '\t},\n' + '\t{ USB_DEVICE_INTERFACE_NUMBER(0x05ac, 0x1905, 2),\n' + '\t .driver_info = (unsigned long)&apple_private_interface_info,\n' + '\t},\n' + ) + + pat = re.compile( + r'(\{\s*USB_INTERFACE_INFO\s*\(\s*USB_CLASS_COMM\s*,\s*USB_CDC_SUBCLASS_NCM\s*,\s*USB_CDC_PROTO_NONE\s*\)\s*,\s*\.driver_info\s*=\s*\(unsigned long\)&cdc_ncm_info\s*,\s*\},)', + re.S, + ) + s2, n = pat.subn(block + r'\1', s, count=1) + if n != 1: + raise SystemExit("Could not find generic CDC NCM class-match entry to patch") + s = s2 + +# Patch old bind check if needed. +# Old style: +# if (!dev->in || !dev->out || !dev->status) +# New behavior for this Mac: +# allow missing status endpoint for 05ac:1905 +old = re.compile( + r'if\s*\(\s*!dev->in\s*\|\|\s*!dev->out\s*\|\|\s*!dev->status\s*\)', + re.S, +) +repl = ( + 'if (!dev->in || !dev->out || ' + '(!dev->status && ' + '!(le16_to_cpu(dev->udev->descriptor.idVendor) == 0x05ac && ' + 'le16_to_cpu(dev->udev->descriptor.idProduct) == 0x1905)))' +) +s, n = old.subn(repl, s, count=1) + +# If the tree already has the newer FLAG_LINK_INTR logic, leave it alone. +if n == 0 and "FLAG_LINK_INTR" not in s: + raise SystemExit("Could not patch bind_common() missing-status check") + +# Add a loud marker so we know the patched module actually loaded. +if 'Apple 05ac:1905 quirk test module loaded' not in s: + m = re.search(r'static\s+int\s+__init\s+cdc_ncm_init\s*\(\s*void\s*\)\s*\{', s) + if m: + insert_at = m.end() + s = s[:insert_at] + '\n\tpr_info("cdc_ncm: Apple 05ac:1905 quirk test module loaded\\n");' + s[insert_at:] + +p.write_text(s) +print("Patched", p) +PY + +msg "Preparing out-of-tree build dir" +mkdir -p buildmod +cp drivers/net/usb/cdc_ncm.c buildmod/ + +cat >buildmod/Makefile <<'EOF' +obj-m += cdc_ncm.o +ccflags-y += -Wno-error + +all: + $(MAKE) -C /lib/modules/$(shell uname -r)/build M=$(PWD) modules + +clean: + $(MAKE) -C /lib/modules/$(shell uname -r)/build M=$(PWD) clean +EOF + +msg "Building patched module" +cd buildmod +make -C "$KBUILD" M="$PWD" modules +PATCHED_KO="$PWD/cdc_ncm.ko" +[[ -f $PATCHED_KO ]] || die "Patched cdc_ncm.ko was not built" + +msg "Patched module info" +modinfo "$PATCHED_KO" | sed -n '1,20p' + +msg "Reloading module stack" +modprobe -r cdc_mbim 2>/dev/null || true +modprobe -r cdc_ncm 2>/dev/null || true +insmod "$PATCHED_KO" + +msg "Recent dmesg" +dmesg | tail -n 60 + +cat </dev/null 2>&1 || { + echo "Missing command: $1" >&2 + exit 1 + } +} + +msg() { + printf '\n==> %s\n' "$*" +} + +[[ $EUID -eq 0 ]] || { + echo "Run with sudo." >&2 + exit 1 +} + +need modprobe +need insmod +need nmcli +need lsusb +need ip +need awk +need grep + +[[ -f $PATCHED_CDC_NCM ]] || { + echo "Patched module not found: $PATCHED_CDC_NCM" >&2 + exit 1 +} + +msg "Removing old hard-block config if present" +rm -f /etc/modprobe.d/zz-kill-mac-usb-net.conf +depmod -a + +msg "Loading USB4 / Type-C / Thunderbolt support" +modprobe typec || true +modprobe typec_ucsi || true +modprobe ucsi_acpi || true +modprobe thunderbolt || true +modprobe typec_thunderbolt || true +modprobe thunderbolt_net || true + +msg "Unloading stock USB network stack" +modprobe -r cdc_mbim cdc_wdm cdc_ncm cdc_ether usbnet 2>/dev/null || true + +msg "Loading dependency modules" +modprobe usbnet +modprobe cdc_ether + +msg "Loading patched cdc_ncm" +if lsmod | grep -q '^cdc_ncm '; then + echo "cdc_ncm already loaded" +else + insmod "$PATCHED_CDC_NCM" +fi + +msg "Current module state" +lsmod | grep -E 'typec|ucsi|thunderbolt|cdc_ncm|cdc_ether|usbnet' || true + +cat <<'EOF' + +Now do this on the Mac: + 1. Run the Mac script below + 2. Replug the cable + 3. Wait for the Mac to land on the fast bus + +Then run this same script again with: + sudo ./spark-usbc-setup.sh finalize + +EOF + +if [[ ${1:-} != "finalize" ]]; then + exit 0 +fi + +msg "Waiting for Apple Mac on fast bus (Bus 004 @ 10000M or equivalent)" +for _ in $(seq 1 60); do + if lsusb -t | grep -q '05ac:1905\|Driver=\[none\], 10000M'; then + break + fi + sleep 1 +done + +msg "lsusb -t snapshot" +lsusb -t +echo +lsusb | grep '05ac:1905' || true + +msg "Waiting for new Apple USB NICs" +for _ in $(seq 1 30); do + mapfile -t APPLE_IFS < <( + ip -o link | awk -F': ' '{print $2}' | + grep '^enx36be1bab12' || true + ) + if [[ ${#APPLE_IFS[@]} -ge 2 ]]; then + break + fi + sleep 1 +done + +if [[ ${#APPLE_IFS[@]} -lt 2 ]]; then + echo "Did not find two Apple USB interfaces." >&2 + ip -br link + exit 1 +fi + +IF0="${APPLE_IFS[0]}" +IF1="${APPLE_IFS[1]}" + +msg "Found interfaces: $IF0 and $IF1" + +msg "Resetting old NetworkManager profiles" +nmcli connection delete mac-usb-dhcp-0 2>/dev/null || true +nmcli connection delete mac-usb-dhcp-1 2>/dev/null || true + +msg "Bringing up shared DHCP on both interfaces" +nmcli connection add type ethernet ifname "$IF0" con-name mac-usb-dhcp-0 +nmcli connection modify mac-usb-dhcp-0 \ + ipv4.method shared \ + ipv6.method disabled \ + ipv4.addresses 10.42.0.1/24 +nmcli connection up mac-usb-dhcp-0 + +nmcli connection add type ethernet ifname "$IF1" con-name mac-usb-dhcp-1 +nmcli connection modify mac-usb-dhcp-1 \ + ipv4.method shared \ + ipv6.method disabled \ + ipv4.addresses 10.43.0.1/24 +nmcli connection up mac-usb-dhcp-1 + +msg "Current addresses" +ip -br addr show dev "$IF0" +ip -br addr show dev "$IF1" + +msg "Recent NetworkManager log" +journalctl -u NetworkManager -n 60 --no-pager || true + +cat < 10.42.0.1/24 + $IF1 -> 10.43.0.1/24 + +Next on the Mac: + sudo ipconfig set anpi0 DHCP + sudo ipconfig set anpi1 DHCP + sudo ipconfig set anpi2 DHCP + +EOF diff --git a/uv.lock b/uv.lock index ba6303604c..5f6edcdf69 100644 --- a/uv.lock +++ b/uv.lock @@ -2,15 +2,24 @@ version = 1 revision = 3 requires-python = ">=3.13" resolution-markers = [ - "python_full_version >= '3.14' and sys_platform == 'darwin'", - "python_full_version < '3.14' and sys_platform == 'darwin'", - "python_full_version >= '3.14' and sys_platform == 'linux'", - "python_full_version < '3.14' and sys_platform == 'linux'", + "python_full_version >= '3.14' and sys_platform == 'darwin' and extra != 'extra-3-exo-cuda' and extra == 'project-9-exo-bench'", + "python_full_version < '3.14' and sys_platform == 'darwin' and extra != 'extra-3-exo-cuda' and extra == 'project-9-exo-bench'", + "sys_platform == 'darwin' and extra == 'extra-3-exo-cuda' and extra != 'project-9-exo-bench'", + "sys_platform == 'darwin' and extra != 'extra-3-exo-cuda' and extra != 'project-9-exo-bench'", + "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-3-exo-cuda' and extra == 'project-9-exo-bench'", + "python_full_version < '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-3-exo-cuda' and extra == 'project-9-exo-bench'", + "python_full_version >= '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-3-exo-cuda' and extra != 'project-9-exo-bench'", + "python_full_version < '3.14' and platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'extra-3-exo-cuda' and extra != 'project-9-exo-bench'", + "platform_machine == 'aarch64' and sys_platform == 'linux' and extra != 'extra-3-exo-cuda' and extra != 'project-9-exo-bench'", ] supported-markers = [ "sys_platform == 'darwin'", - "sys_platform == 'linux'", + "platform_machine == 'aarch64' and sys_platform == 'linux'", ] +conflicts = [[ + { package = "exo", extra = "cuda" }, + { package = "exo-bench" }, +]] [options] prerelease-mode = "allow" @@ -54,13 +63,13 @@ name = "aiohttp" version = "3.13.3" source = { registry = "https://pypi.org/simple" } 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extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "huggingface-hub", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "hypercorn", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "loguru", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "mflux", marker = "sys_platform == 'darwin' or (extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "mlx", version = "0.30.6", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "mlx", version = "0.30.7.dev20260312+257d5692", source = { git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks#257d5692fc7af6bba3b8afaeb63c549b7d1e43d5" }, marker = "sys_platform == 'darwin' or (extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "mlx-lm", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "msgspec", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "openai-harmony", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "psutil", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "pydantic", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "python-multipart", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "rustworkx", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "tiktoken", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "tomlkit", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "types-aiofiles", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "zstandard", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, +] + +[package.optional-dependencies] +cuda = [ + { name = "fastsafetensors", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" }, + { name = "mlx-cuda-13", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" }, + { name = "torch", version = "2.10.0+cu130", source = { registry = "https://download.pytorch.org/whl/cu130" }, marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" }, + { name = "vllm", marker = "platform_machine == 'aarch64' and sys_platform == 'linux'" }, ] [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'" }, - { name = "pytest-env", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "ruff", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, + { name = "basedpyright", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "pyinstaller", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "pytest", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "pytest-asyncio", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "pytest-env", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "ruff", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, ] [package.metadata] @@ -504,14 +873,16 @@ requires-dist = [ { name = "anyio", specifier = "==4.11.0" }, { name = "exo-pyo3-bindings", editable = "rust/exo_pyo3_bindings" }, { name = "fastapi", specifier = ">=0.116.1" }, + { name = "fastsafetensors", marker = "sys_platform == 'linux' and extra == 'cuda'", specifier = ">=0.1.10" }, { name = "filelock", specifier = ">=3.18.0" }, { name = "httpx", specifier = ">=0.28.1" }, { name = "huggingface-hub", specifier = ">=0.33.4" }, { name = "hypercorn", specifier = ">=0.18.0" }, { name = "loguru", specifier = ">=0.7.3" }, - { name = "mflux", specifier = "==0.16.9" }, + { name = "mflux", marker = "sys_platform == 'darwin'", specifier = "==0.16.9" }, { name = "mlx", marker = "sys_platform == 'darwin'", git = "https://github.com/rltakashige/mlx-jaccl-fix-small-recv.git?branch=address-rdma-gpu-locks" }, - { name = "mlx", extras = ["cpu"], marker = "sys_platform == 'linux'", specifier = "==0.30.6" }, + { name = "mlx", marker = "sys_platform == 'linux'", specifier = "==0.30.6" }, + { name = "mlx-cuda-13", marker = "sys_platform == 'linux' and extra == 'cuda'", specifier = "==0.30.6" }, { name = "mlx-lm", git = "https://github.com/rltakashige/mlx-lm?branch=leo%2Feval-left-padding-in-batched-rotation" }, { name = "msgspec", specifier = ">=0.19.0" }, { name = "openai-harmony", specifier = ">=0.0.8" }, @@ -521,9 +892,12 @@ requires-dist = [ { name = "rustworkx", specifier = ">=0.17.1" }, { name = "tiktoken", specifier = ">=0.12.0" }, { name = "tomlkit", specifier = ">=0.14.0" }, + { name = "torch", marker = "sys_platform == 'linux' and extra == 'cuda'", specifier = ">=2.10.0", index = "https://download.pytorch.org/whl/cu130" }, { name = "types-aiofiles", specifier = ">=24.1.0.20250708" }, + { name = "vllm", marker = "sys_platform == 'linux' and extra == 'cuda'", git = "https://github.com/hmellor/vllm.git?branch=transformers-v5" }, { name = "zstandard", specifier = ">=0.23.0" }, ] +provides-extras = ["cuda"] [package.metadata.requires-dev] dev = [ @@ -540,18 +914,18 @@ name = "exo-bench" version = "0.1.0" source = { editable = "bench" } dependencies = [ - { name = "datasets", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "httpx", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "huggingface-hub", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "human-eval", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "jinja2", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "lm-eval", extra = ["api", "math"], marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "loguru", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "math-verify", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "numpy", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "protobuf", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "tiktoken", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, - { name = "transformers", marker = "sys_platform == 'darwin' or sys_platform == 'linux'" }, + { name = "datasets", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "httpx", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "huggingface-hub", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "human-eval", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "jinja2", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "lm-eval", extra = ["api", "math"], marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "loguru", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "math-verify", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "numpy", version = "2.4.3", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "protobuf", version = "7.34.0", source = { registry = "https://pypi.org/simple" }, marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "tiktoken", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "transformers", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux' and extra == 'project-9-exo-bench') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or (sys_platform == 'darwin' and extra == 'project-9-exo-bench') or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, ] [package.metadata] @@ -577,9 +951,9 @@ source = { editable = "rust/exo_pyo3_bindings" } [package.dev-dependencies] dev = [ - { name = "exo-pyo3-bindings", 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'" }, + { name = "exo-pyo3-bindings", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "pytest", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench') or sys_platform == 'darwin' or (sys_platform != 'linux' and extra == 'extra-3-exo-cuda' and extra == 'project-9-exo-bench')" }, + { name = "pytest-asyncio", marker = "(platform_machine == 'aarch64' and sys_platform == 'linux') or (platform_machine != 'aarch64' and extra == 'extra-3-exo-cuda' and extra 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