feat: run:ai gpu_direct model loading with quantization-aware device maps

src/nnsight/modeling/loader.py

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+"""Streaming model weight loading via run:ai SafetensorsStreamer."""
+
+import json
+import os
+import struct
+import time
+import threading
+from pathlib import Path
+
+import torch
+
+# -- Safetensors dtype string → torch.dtype -----------------------------------
+
+_SAFETENSORS_DTYPE = {
+    "F64": torch.float64,
+    "F32": torch.float32,
+    "F16": torch.float16,
+    "BF16": torch.bfloat16,
+    "I64": torch.int64,
+    "I32": torch.int32,
+    "I16": torch.int16,
+    "I8": torch.int8,
+    "U8": torch.uint8,
+    "BOOL": torch.bool,
+}
+
+_FLOATING_DTYPES = frozenset(
+    {torch.float16, torch.bfloat16, torch.float32, torch.float64}
+)
+
+def resolve_shard_paths(repo_id: str, revision: str = "main") -> list[str]:
+    """Resolve local .safetensors shard paths from HF cache.
+
+    Raises ValueError if no .safetensors files found (no .bin fallback —
+    SafetensorsStreamer only handles safetensors format).
+    """
+    from huggingface_hub import snapshot_download
+
+    model_dir = snapshot_download(repo_id, revision=revision, local_files_only=True)
+    paths = sorted(Path(model_dir).glob("*.safetensors"))
+    if not paths:
+        raise ValueError(
+            f"No .safetensors files found for {repo_id} (rev={revision}). "
+            f"SafetensorsStreamer requires safetensors format."
+        )
+    return [str(p) for p in paths]
+
+
+# -- Shard-by-shard streaming -------------------------------------------------
+
+
+def _parse_safetensors_keys(
+    shard_paths: list[str],
+) -> tuple[dict[str, tuple[str, list[int], str]], dict[str, int]]:
+    """Parse safetensors JSON headers to build key→metadata mapping.
+
+    Reads only the 8-byte length prefix + JSON header per file — no tensor
+    data is touched.  ~0.2 ms per file vs ~130 ms for ``safe_open`` on
+    networked FS.
+
+    Returns:
+        key_map: ``{key: (shard_path, shape, dtype_str)}``
+        shard_key_counts: ``{shard_path: num_keys}``
+    """
+    key_map: dict[str, tuple[str, list[int], str]] = {}
+    shard_key_counts: dict[str, int] = {}
+
+    for path in shard_paths:
+        with open(path, "rb") as f:
+            (header_size,) = struct.unpack("<Q", f.read(8))
+            header = json.loads(f.read(header_size))
+
+        count = 0
+        for key, meta in header.items():
+            if key == "__metadata__":
+                continue
+            key_map[key] = (path, meta["shape"], meta["dtype"])
+            count += 1
+        shard_key_counts[path] = count
+
+    return key_map, shard_key_counts
+
+
+class RunAIShardCache:
+    """Incremental shard cache with per-tensor notification.
+
+    Stores tensors one-by-one as Run:AI produces them and wakes waiting
+    workers immediately.  This enables pipelining between disk I/O
+    (loader thread) and GPU transfers (worker threads).
+
+    When ``device_map`` is provided and contains GPU targets, tensors are
+    copied **directly from the Run:AI buffer to GPU** during streaming,
+    bypassing the intermediate CPU clone.  HF's ``_materialize_copy().to()``
+    then becomes a no-op (tensor is already on the target device/dtype).
+
+    Thread coordination handles up to ``GLOBAL_WORKERS`` concurrent callers
+    from transformers' ``ThreadPoolExecutor``.
+    """
+
+    def __init__(
+        self,
+        concurrency: int,
+        device_map: dict | None = None,
+        torch_dtype: torch.dtype | None = None,
+    ) -> None:
+        self._concurrency = concurrency
+        self._device_map = device_map
+        self._torch_dtype = torch_dtype
+        self._gpu_direct = (
+            device_map is not None and torch.cuda.is_available()
+        )
+        self._lock = threading.Lock()
+        self._condition = threading.Condition(self._lock)
+
+        # Per-tensor storage (flat, not grouped by shard)
+        self._tensors: dict[str, torch.Tensor] = {}
+
+        # Shard-level tracking
+        self._refcounts: dict[str, int] = {}
+        self._shard_loading: set[str] = set()
+        self._errors: dict[str, Exception] = {}
+
+        # Profiling stats (thread-safe via _lock/_condition)
+        self.stats_shard_wall_s = 0.0  # wall time of _stream_shard_incremental
+        self.stats_io_wait_s = 0.0     # time waiting for streamer (uncovered I/O)
+        self.stats_clone_s = 0.0       # time cloning tensors (CPU-target path)
+        self.stats_gpu_copy_s = 0.0    # time in buffer→GPU .to() copies
+        self.stats_notify_s = 0.0      # time holding lock for store+notify
+        self.stats_consumer_wait_s = 0.0  # cumulative consumer wait on Condition
+        self.stats_pop_count = 0       # tensors served
+
+    def register(self, shard_path: str, num_keys: int) -> None:
+        """Set the initial refcount for a shard (= number of keys it holds)."""
+        self._refcounts[shard_path] = num_keys
+
+    def get(self, shard_path: str, key: str) -> torch.Tensor:
+        """Return the tensor for *key*, streaming the shard if needed."""
+        should_load = False
+        t_wait_start = time.perf_counter()
+
+        with self._condition:
+            while True:
+                if shard_path in self._errors:
+                    raise self._errors[shard_path]
+
+                if key in self._tensors:
+                    self.stats_consumer_wait_s += time.perf_counter() - t_wait_start
+                    return self._pop_tensor(shard_path, key)
+
+                if shard_path not in self._shard_loading:
+                    self._shard_loading.add(shard_path)
+                    should_load = True
+                    break
+
+                # Wait for notification (spurious wakeups handled by loop)
+                self._condition.wait()
+
+        if should_load:
+            try:
+                self._stream_shard_incremental(shard_path)
+            except Exception as e:
+                with self._condition:
+                    self._errors[shard_path] = e
+                    self._condition.notify_all()
+                raise
+
+            # Loader's own tensor is now available
+            with self._condition:
+                if shard_path in self._errors:
+                    raise self._errors[shard_path]
+                return self._pop_tensor(shard_path, key)
+
+    # -- internals -------------------------------------------------------------
+
+    def _pop_tensor(self, shard_path: str, key: str) -> torch.Tensor:
+        """Pop *key* from the cache and clean up shard tracking if exhausted."""
+        tensor = self._tensors.pop(key)
+        self._refcounts[shard_path] -= 1
+        self.stats_pop_count += 1
+        if self._refcounts[shard_path] == 0:
+            del self._refcounts[shard_path]
+            self._shard_loading.discard(shard_path)
+        return tensor
+
+    def _resolve_device(self, checkpoint_key: str) -> torch.device:
+        """Map a checkpoint key to its target device via the device_map."""
+        from transformers.integrations.accelerate import expand_device_map
+
+        expanded = expand_device_map(self._device_map, [checkpoint_key])
+        device = expanded[checkpoint_key]
+        if isinstance(device, int):
+            return torch.device("cuda", device)
+        if device == "disk":
+            return torch.device("cpu")
+        return torch.device(device)
+
+    def _resolve_dtype(self, tensor: torch.Tensor) -> torch.dtype | None:
+        """Return target dtype for a tensor, or None to keep original.
+
+        Only floating-point tensors are cast; integer/bool buffers are
+        left as-is.
+        """
+        if self._torch_dtype is None:
+            return None
+        if tensor.dtype in _FLOATING_DTYPES:
+            return self._torch_dtype
+        return None
+
+    @staticmethod
+    def _enable_expandable_segments() -> None:
+        """Enable CUDA expandable segments to avoid fragmentation.
+
+        Streaming hundreds of individually-sized tensors to GPU causes
+        fragmentation in the default CUDA caching allocator.  Expandable
+        segments let the allocator grow incrementally instead of reserving
+        large fixed blocks, with no measurable performance penalty.
+        """
+        key = "PYTORCH_CUDA_ALLOC_CONF"
+        conf = os.environ.get(key, "")
+        if "expandable_segments" not in conf:
+            entry = "expandable_segments:True"
+            os.environ[key] = f"{conf},{entry}" if conf else entry
+
+    def _stream_shard_incremental(self, shard_path: str) -> None:
+        """Stream a shard via run:ai, storing and notifying per tensor."""
+        from runai_model_streamer import SafetensorsStreamer
+
+        os.environ["RUNAI_STREAMER_CONCURRENCY"] = str(self._concurrency)
+
+        # Avoid CUDA memory fragmentation from streaming many varied-size tensors
+        if self._gpu_direct:
+            self._enable_expandable_segments()
+
+        t_shard_start = time.perf_counter()
+        t_iter_start = t_shard_start
+        with SafetensorsStreamer() as streamer:
+            streamer.stream_files([shard_path], device="cpu")
+            for name, tensor in streamer.get_tensors():
+                t_yield = time.perf_counter()
+                io_wait = t_yield - t_iter_start
+
+                if self._gpu_direct:
+                    target_device = self._resolve_device(name)
+                    target_dtype = self._resolve_dtype(tensor)
+                    if target_device.type == "cuda":
+                        # Direct buffer → GPU (one copy, no intermediate clone)
+                        result = tensor.to(
+                            device=target_device,
+                            dtype=target_dtype or tensor.dtype,
+                        )
+                        t_copied = time.perf_counter()
+                        with self._condition:
+                            self.stats_io_wait_s += io_wait
+                            self.stats_gpu_copy_s += t_copied - t_yield
+                            self._tensors[name] = result
+                            self._condition.notify_all()
+                    else:
+                        # CPU/disk target — must clone since buffer is transient
+                        cloned = tensor.clone()
+                        t_cloned = time.perf_counter()
+                        if target_dtype is not None:
+                            cloned = cloned.to(dtype=target_dtype)
+                        with self._condition:
+                            self.stats_io_wait_s += io_wait
+                            self.stats_clone_s += t_cloned - t_yield
+                            self._tensors[name] = cloned
+                            self._condition.notify_all()
+                else:
+                    # CPU-only path (no device_map or no CUDA)
+                    cloned = tensor.clone()
+                    t_cloned = time.perf_counter()
+                    with self._condition:
+                        self.stats_io_wait_s += io_wait
+                        self.stats_clone_s += t_cloned - t_yield
+                        self._tensors[name] = cloned
+                        self._condition.notify_all()
+
+                t_iter_start = time.perf_counter()
+        shard_wall = time.perf_counter() - t_shard_start
+
+        with self._condition:
+            self.stats_shard_wall_s += shard_wall
+
+
+class LazyRunAITensor:
+    """Drop-in replacement for ``SafetensorSlice``.
+
+    Implements the minimal interface that transformers'
+    ``convert_and_load_state_dict_in_model`` expects from state-dict values:
+    ``__getitem__``, ``.dtype``, ``.shape``, ``.get_shape()``, and
+    ``.is_floating_point()``.
+
+    The actual tensor is streamed on the first (and only) ``__getitem__``
+    call via the shared :class:`RunAIShardCache`.
+    """
+
+    def __init__(
+        self,
+        shard_path: str,
+        key: str,
+        cache: RunAIShardCache,
+        shape: list[int],
+        dtype_str: str,
+    ) -> None:
+        self._shard_path = shard_path
+        self._key = key
+        self._cache = cache
+        self._shape_list = shape
+        self._dtype = _SAFETENSORS_DTYPE[dtype_str]
+
+    def __getitem__(self, idx):
+        tensor = self._cache.get(self._shard_path, self._key)
+        return tensor[idx]
+
+    def to(self, *args, **kwargs):
+        """Materialize the tensor and move it to the target device/dtype."""
+        tensor = self._cache.get(self._shard_path, self._key)
+        return tensor.to(*args, **kwargs)
+
+    @property
+    def dtype(self) -> torch.dtype:
+        return self._dtype
+
+    @property
+    def shape(self) -> torch.Size:
+        return torch.Size(self._shape_list)
+
+    def get_shape(self) -> list[int]:
+        return list(self._shape_list)
+
+    def is_floating_point(self) -> bool:
+        return self._dtype in _FLOATING_DTYPES
+
+
+def build_lazy_state_dict(
+    shard_paths: list[str],
+    concurrency: int = 16,
+    device_map: dict | None = None,
+    torch_dtype: torch.dtype | None = None,
+) -> dict[str, LazyRunAITensor]:
+    """Build a lazy state dict backed by run:ai streaming.
+
+    Values are :class:`LazyRunAITensor` instances that stream the
+    underlying safetensors shard on first ``__getitem__`` access.  Shards
+    are evicted from memory once all their keys have been consumed,
+    keeping peak memory to ~1 tensor instead of the full model.
+
+    Args:
+        shard_paths: Paths to ``.safetensors`` shard files.
+        concurrency: Passed to ``RUNAI_STREAMER_CONCURRENCY``.
+        device_map: Resolved device map (param name → device).  When
+            provided with GPU targets, the streaming cache copies tensors
+            directly from the Run:AI buffer to GPU, so that HF's
+            ``_materialize_copy().to()`` is a no-op.
+        torch_dtype: Target dtype for floating-point tensors.  Applied
+            during GPU transfer so HF's dtype cast is also a no-op.
+
+    Returns:
+        Dict mapping checkpoint key names to :class:`LazyRunAITensor`.
+    """
+    # Fail fast if run:ai is not installed.
+    from runai_model_streamer import SafetensorsStreamer  # noqa: F401
+
+    key_map, shard_key_counts = _parse_safetensors_keys(shard_paths)
+
+    cache = RunAIShardCache(
+        concurrency, device_map=device_map, torch_dtype=torch_dtype,
+    )
+    for shard_path, count in shard_key_counts.items():
+        cache.register(shard_path, count)
+
+    return {
+        key: LazyRunAITensor(shard_path, key, cache, shape, dtype_str)
+        for key, (shard_path, shape, dtype_str) in key_map.items()
+    }