astrai/inference/__init__.py

@@ -13,7 +13,7 @@ from astrai.inference.engine import (
     GenerationRequest,
     InferenceEngine,
 )
-from astrai.inference.sample import (
+from astrai.inference.sampling import (
     BaseSamplingStrategy,
     SamplingPipeline,
     TemperatureStrategy,
@@ -21,8 +21,11 @@ from astrai.inference.sample import (
     TopPStrategy,
     sample,
 )
-from astrai.inference.scheduler import InferenceScheduler
-from astrai.inference.task import STOP, Task, TaskStatus
+from astrai.inference.scheduler import (
+    InferenceScheduler,
+    Task,
+    TaskStatus,
+)
 
 __all__ = [
     # Engine / Requests
@@ -31,7 +34,6 @@ __all__ = [
     "GenerationParams",
     # Scheduler
     "InferenceScheduler",
-    "STOP",
     "Task",
     "TaskStatus",
     # Sampling (Strategy pattern)

astrai/inference/cache.py

@@ -1,9 +1,16 @@
-from collections import OrderedDict
-from typing import Callable, Dict, List, Optional, Tuple
+"""Page-based KV cache with page-table-indirected read/write.
+
+Provides:
+  - PagedCache: paged KV cache combining page pool and tensor storage.
+"""
+
+from typing import Dict, List, Tuple
 
 import torch
 from torch import Tensor
 
+STOP = object()
+
 
 def page_hash(token_ids: List[int], page_idx: int, page_size: int) -> int:
     start = page_idx * page_size
@@ -14,136 +21,18 @@ def page_hash(token_ids: List[int], page_idx: int, page_size: int) -> int:
     return h
 
 
-class PagePool:
-    """Bitmask page allocator with ref-counting and LRU eviction."""
-
-    def __init__(self, n_pages: int, on_evict: Optional[Callable[[int], None]] = None):
-        self._free_mask = (1 << n_pages) - 1
-        self._refs: List[int] = [0] * n_pages
-        self._lru: OrderedDict[int, None] = OrderedDict()
-        self._on_evict = on_evict
-
-    def alloc(self) -> int:
-        if self._free_mask:
-            lsb = self._free_mask & -self._free_mask
-            idx = lsb.bit_length() - 1
-            self._free_mask ^= lsb
-            self._refs[idx] = 1
-            return idx
-        if self._lru:
-            idx, _ = self._lru.popitem(last=False)
-            if self._on_evict:
-                self._on_evict(idx)
-            self._refs[idx] = 1
-            self._free_mask &= ~(1 << idx)
-            return idx
-        return -1
-
-    def free(self, idx: int, keep_cached: bool = False) -> None:
-        self._refs[idx] -= 1
-        if self._refs[idx] == 0:
-            if keep_cached:
-                self._lru[idx] = None
-            else:
-                self._free_mask |= 1 << idx
-
-    def inc_ref(self, idx: int) -> None:
-        self._refs[idx] += 1
-
-    def touch(self, idx: int) -> None:
-        self._lru.move_to_end(idx)
-
-    def remove_from_lru(self, idx: int) -> None:
-        self._lru.pop(idx, None)
-
-
-class PrefixCache:
-    """Hash-based prefix matching: maps page hashes to physical page indices."""
-
-    def __init__(self, page_size: int):
-        self._page_size = page_size
-        self._page_to_hash: Dict[int, int] = {}
-        self._hash_to_page: Dict[int, int] = {}
-
-    def on_evict(self, idx: int) -> None:
-        h = self._page_to_hash.pop(idx, None)
-        if h is not None:
-            self._hash_to_page.pop(h, None)
-
-    def has_page(self, idx: int) -> bool:
-        return idx in self._page_to_hash
-
-    def lookup(self, token_ids: List[int], pool: PagePool) -> List[int]:
-        full_pages = len(token_ids) // self._page_size
-        hits: List[int] = []
-        for i in range(full_pages):
-            h = page_hash(token_ids, i, self._page_size)
-            p = self._hash_to_page.get(h)
-            if p is None:
-                break
-            pool.touch(p)
-            hits.append(p)
-        return hits
-
-    def record(
-        self,
-        page_idx: int,
-        token_ids: List[int],
-        logical_page_idx: int,
-        pool: PagePool,
-    ) -> None:
-        h = page_hash(token_ids, logical_page_idx, self._page_size)
-        old_h = self._page_to_hash.pop(page_idx, None)
-        if old_h is not None:
-            self._hash_to_page.pop(old_h, None)
-        self._page_to_hash[page_idx] = h
-        self._hash_to_page[h] = page_idx
-        pool.remove_from_lru(page_idx)
-
-
-class TaskTable:
-    """Maps task_ids to page tables and cached token counts."""
-
-    def __init__(self, pool: PagePool, page_size: int):
-        self._pool = pool
-        self._page_size = page_size
-        self._pages: Dict[str, List[int]] = {}
-        self._cached: Dict[str, int] = {}
-
-    def set(self, task_id: str, page_table: List[int], cached: int) -> None:
-        self._pages[task_id] = page_table
-        self._cached[task_id] = cached
-
-    def get(self, task_id: str) -> List[int]:
-        return self._pages.get(task_id, [])
-
-    def get_cached(self, task_id: str) -> int:
-        return self._cached.get(task_id, 0)
-
-    def pop(self, task_id: str) -> Tuple[List[int], int]:
-        pages = self._pages.pop(task_id, [])
-        cached = self._cached.pop(task_id, 0)
-        return pages, cached
-
-    def extend(self, task_id: str, pos: int) -> bool:
-        page_table = self._pages[task_id]
-        needed = (pos + 1 + self._page_size - 1) // self._page_size
-        while len(page_table) < needed:
-            p = self._pool.alloc()
-            if p < 0:
-                return False
-            page_table.append(p)
-        return True
-
-    def table_tensor(self, task_ids: List[str], device: torch.device) -> Tensor:
-        states = [self._pages.get(tid, []) for tid in task_ids]
-        max_pages = max((len(s) for s in states), default=0)
-        rows = [s + [-1] * (max_pages - len(s)) for s in states]
-        return torch.tensor(rows, dtype=torch.long, device=device)
-
-
 class PagedCache:
-    """Facade: paged KV-cache backed by PagePool, PrefixCache, and TaskTable."""
+    """Paged KV cache with page-table-indirected read/write and persistent prefix caching.
+
+    Combines:
+      - Page pool (ref-counted alloc/free via bitmask)
+      - KV tensor storage (k_cache, v_cache)
+      - Prefix-cache hash lookup (page_content_hash -> physical_page_idx)
+      - LRU eviction for persistent cross-batch prefix caching
+
+    Pages with recorded hashes persist after refcount reaches 0 (pinned).
+    They are evicted via LRU only when alloc() finds no free pages.
+    """
 
     def __init__(
         self,
@@ -156,10 +45,8 @@ class PagedCache:
         dtype: torch.dtype,
     ):
         self.page_size = page_size
-        self._prefix = PrefixCache(page_size)
-        self._pool = PagePool(n_pages, on_evict=self._prefix.on_evict)
-        self._table = TaskTable(self._pool, page_size)
-
+        self._free_mask = (1 << n_pages) - 1
+        self._refs: List[int] = [0] * n_pages
         self.k_cache = torch.empty(
             (n_layers, n_pages, page_size, n_kv_heads, head_dim),
             device=device,
@@ -170,81 +57,95 @@ class PagedCache:
             device=device,
             dtype=dtype,
         )
+        self._page_to_hash: Dict[int, int] = {}
+        self._hash_to_page: Dict[int, int] = {}
+        self._lru: List[int] = []
+        self._pin: List[bool] = [False] * n_pages
+
+    def _touch(self, idx: int) -> None:
+        if self._refs[idx] == 0 and idx in self._lru:
+            self._lru.remove(idx)
+            self._lru.append(idx)
+
+    def _evict_one(self) -> int:
+        while self._lru:
+            idx = self._lru.pop(0)
+            h = self._page_to_hash.pop(idx, None)
+            if h is not None:
+                self._hash_to_page.pop(h, None)
+            self._pin[idx] = False
+            self._refs[idx] = 1
+            return idx
+        return -1
+
+    def record_page(
+        self, page_idx: int, token_ids: List[int], logical_page_idx: int
+    ) -> None:
+        h = page_hash(token_ids, logical_page_idx, self.page_size)
+        old_h = self._page_to_hash.pop(page_idx, None)
+        if old_h is not None:
+            self._hash_to_page.pop(old_h, None)
+        self._page_to_hash[page_idx] = h
+        self._hash_to_page[h] = page_idx
+        self._pin[page_idx] = True
+        if page_idx in self._lru:
+            self._lru.remove(page_idx)
+
+    def lookup_prefix(self, token_ids: List[int]) -> List[int]:
+        full_pages = len(token_ids) // self.page_size
+        hits: List[int] = []
+        for i in range(full_pages):
+            h = page_hash(token_ids, i, self.page_size)
+            p = self._hash_to_page.get(h)
+            if p is None:
+                break
+            self._touch(p)
+            hits.append(p)
+        return hits
+
+    def inc_ref(self, idx: int) -> None:
+        self._refs[idx] += 1
+        if self._refs[idx] == 1 and idx in self._lru:
+            self._lru.remove(idx)
+
+    def alloc(self) -> int:
+        if self._free_mask:
+            lsb = self._free_mask & -self._free_mask
+            idx = lsb.bit_length() - 1
+            self._free_mask ^= lsb
+            self._refs[idx] = 1
+            if idx in self._lru:
+                self._lru.remove(idx)
+            return idx
+        return self._evict_one()
 
     def alloc_n(self, n: int) -> List[int]:
-        pages: List[int] = []
-        for _ in range(n):
-            p = self._pool.alloc()
-            if p < 0:
-                for page in pages:
-                    self.free(page)
+        pages = [self.alloc() for _ in range(n)]
+        if any(p < 0 for p in pages):
+            for p in pages:
+                if p >= 0:
+                    self.free(p)
             return []
-            pages.append(p)
         return pages
 
     def free(self, idx: int) -> None:
-        cached = self._prefix.has_page(idx)
-        self._pool.free(idx, keep_cached=cached)
-        if not cached:
-            self._prefix.on_evict(idx)
-
-    def task_alloc(self, task_id: str, prompt_ids: List[int]) -> bool:
-        hits = self._prefix.lookup(prompt_ids, self._pool)
-        cached = len(hits) * self.page_size
-        for p in hits:
-            self._pool.inc_ref(p)
-
-        remaining = len(prompt_ids) - cached
-        n_new = (
-            (remaining + self.page_size - 1) // self.page_size if remaining > 0 else 0
-        )
-        new_pages: List[int] = []
-        if n_new > 0:
-            for _ in range(n_new):
-                p = self._pool.alloc()
-                if p < 0:
-                    for hp in hits:
-                        self.free(hp)
-                    for np in new_pages:
-                        self.free(np)
-                    return False
-                new_pages.append(p)
-
-        self._table.set(task_id, hits + new_pages, cached)
-        return True
-
-    def task_free(self, task_id: str) -> None:
-        page_table, _ = self._table.pop(task_id)
-        for idx in page_table:
-            self.free(idx)
-
-    def task_extend(self, task_id: str, pos: int) -> bool:
-        return self._table.extend(task_id, pos)
-
-    def task_cached(self, task_id: str) -> int:
-        return self._table.get_cached(task_id)
-
-    def task_record_hashes(
-        self, task_id: str, prompt_ids: List[int], start_logical_page: int = 0
-    ) -> None:
-        page_table = self._table.get(task_id)
-        full_pages = len(prompt_ids) // self.page_size
-        for i in range(start_logical_page, full_pages):
-            self._prefix.record(page_table[i], prompt_ids, i, self._pool)
-
-    def make_table_tensor(self, task_ids: List[str], device: torch.device) -> Tensor:
-        return self._table.table_tensor(task_ids, device)
+        self._refs[idx] -= 1
+        if self._refs[idx] == 0:
+            h = self._page_to_hash.get(idx)
+            if h is not None and self._pin[idx]:
+                self._lru.append(idx)
+            else:
+                self._free_mask |= 1 << idx
+                h = self._page_to_hash.pop(idx, None)
+                if h is not None:
+                    self._hash_to_page.pop(h, None)
+                self._pin[idx] = False
 
     def bind(self, page_table: Tensor, total_len: int = 0) -> "CacheView":
         return CacheView(self, page_table, total_len)
 
     def write(
-        self,
-        layer_id: int,
-        page_table: Tensor,
-        start_pos: int,
-        k: Tensor,
-        v: Tensor,
+        self, layer_id: int, page_table: Tensor, start_pos: int, k: Tensor, v: Tensor
     ) -> None:
         seq_len = k.size(1)
         if seq_len == 0:
@@ -268,21 +169,25 @@ class PagedCache:
             ]
             written += chunk
 
-    def gather(
-        self, layer_id: int, page_table: Tensor, total_len: int
-    ) -> Tuple[Tensor, Tensor]:
+    def gather(self, layer_id: int, page_table: Tensor) -> Tuple[Tensor, Tensor]:
+        # page_table: [batch, max_pages] with -1 padding for tasks with fewer pages.
+        # clamp(min=0) maps -1 to page 0 (irrelevant data) — truncated by CacheView total_len.
         safe = page_table.clamp(min=0)
         k = self.k_cache[layer_id, safe]
         v = self.v_cache[layer_id, safe]
         k = k.flatten(1, 2)
         v = v.flatten(1, 2)
-        k = k[:, :total_len]
-        v = v[:, :total_len]
         return k, v
 
 
 class CacheView:
-    """Bundles PagedCache + page_table + total_len for attention layers."""
+    """Per-batch view that bundles PagedCache + page_table + total_len.
+
+    Attention layers receive this as ``paged_cache`` and only see
+    ``write()`` / ``gather()``, never raw page tables or length params.
+    """
+
+    __slots__ = ("_cache", "_page_table", "_total_len")
 
     def __init__(self, cache: PagedCache, page_table: Tensor, total_len: int = 0):
         self._cache = cache
@@ -293,4 +198,8 @@ class CacheView:
         self._cache.write(layer_id, self._page_table, start_pos, k, v)
 
     def gather(self, layer_id: int) -> Tuple[Tensor, Tensor]:
-        return self._cache.gather(layer_id, self._page_table, self._total_len)
+        k, v = self._cache.gather(layer_id, self._page_table)
+        if self._total_len:
+            k = k[:, : self._total_len]
+            v = v[:, : self._total_len]
+        return k, v

astrai/inference/engine.py

@@ -16,8 +16,8 @@ from typing import Any, AsyncGenerator, Dict, Generator, List, Optional, Tuple,
 import torch
 import torch.nn as nn
 
+from astrai.inference.cache import STOP
 from astrai.inference.scheduler import InferenceScheduler
-from astrai.inference.task import STOP
 from astrai.tokenize import AutoTokenizer
 
 

astrai/inference/executor.py

@@ -1,133 +0,0 @@
-import logging
-from typing import List, Optional
-
-import torch
-
-from astrai.inference.cache import PagedCache
-from astrai.inference.sample import sample
-from astrai.inference.task import STOP, Task, TaskStatus
-from astrai.model.automodel import AutoModel
-from astrai.tokenize.tokenizer import AutoTokenizer
-
-logger = logging.getLogger(__name__)
-
-
-class Executor:
-    """Model forward passes for prefill and decode phases."""
-
-    def __init__(
-        self,
-        model: AutoModel,
-        tokenizer: AutoTokenizer,
-        page_cache: PagedCache,
-        device: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-    ):
-        self.model = model
-        self.tokenizer = tokenizer
-        self.page_cache = page_cache
-        self.device = device or next(model.parameters()).device
-        self.dtype = dtype or next(model.parameters()).dtype
-
-    def execute_prefill(
-        self, tasks: List[Task], prompt_len: int, start_pos: int = 0
-    ) -> None:
-        if start_pos >= prompt_len:
-            return
-
-        tasks = sorted(tasks, key=lambda t: t.task_id)
-        batch_sz = len(tasks)
-
-        seq_len = prompt_len - start_pos
-        input_ids = torch.empty(batch_sz, seq_len, dtype=torch.long, device=self.device)
-        input_mask = torch.ones(
-            batch_sz, prompt_len, dtype=torch.bool, device=self.device
-        )
-
-        for i, t in enumerate(tasks):
-            input_ids[i] = torch.tensor(
-                t.prompt_ids[start_pos:prompt_len], device=self.device
-            )
-
-        task_ids = [t.task_id for t in tasks]
-        page_tables = self.page_cache.make_table_tensor(task_ids, self.device)
-
-        with torch.inference_mode():
-            self.model(
-                input_ids,
-                input_mask=input_mask,
-                start_pos=start_pos,
-                paged_cache=self.page_cache.bind(page_tables, total_len=prompt_len),
-            )
-
-        start_logical_page = start_pos // self.page_cache.page_size
-        for t in tasks:
-            self.page_cache.task_record_hashes(
-                t.task_id, t.prompt_ids, start_logical_page=start_logical_page
-            )
-
-    def execute_decode(self, tasks: List[Task], start_pos: int) -> None:
-        if not tasks:
-            return
-
-        tasks = sorted(tasks, key=lambda t: t.task_id)
-
-        valid: List[Task] = []
-        for t in tasks:
-            if self.page_cache.task_extend(t.task_id, start_pos):
-                valid.append(t)
-            else:
-                t.status = TaskStatus.ABORTED
-                if t.stream_callback:
-                    t.stream_callback(STOP)
-
-        if not valid:
-            return
-
-        tasks = valid
-        batch_sz = len(tasks)
-
-        input_ids = torch.tensor(
-            [t.output_ids[-1] if t.output_ids else t.prompt_ids[-1] for t in tasks],
-            dtype=torch.long,
-            device=self.device,
-        )
-
-        active_mask = torch.ones((batch_sz, 1), dtype=torch.bool, device=self.device)
-
-        task_ids = [t.task_id for t in tasks]
-        page_tables = self.page_cache.make_table_tensor(task_ids, self.device)
-        total_len = start_pos + 1
-
-        temperatures = torch.tensor([t.temperature for t in tasks], device=self.device)
-        top_ks = torch.tensor([t.top_k for t in tasks], device=self.device)
-        top_ps = torch.tensor([t.top_p for t in tasks], device=self.device)
-
-        with torch.inference_mode():
-            outputs = self.model(
-                input_ids.unsqueeze(1),
-                input_mask=active_mask,
-                paged_cache=self.page_cache.bind(page_tables, total_len=total_len),
-                start_pos=start_pos,
-            )
-            logits = outputs["logits"][:, -1, :]
-
-        next_tokens = sample(
-            logits,
-            temperature=temperatures,
-            top_k=top_ks,
-            top_p=top_ps,
-        ).tolist()
-
-        for t, ntok in zip(tasks, next_tokens):
-            t.output_ids.append(ntok)
-            t.output_tokens += 1
-            pos = t.input_tokens + t.output_tokens
-            self.page_cache.task_extend(t.task_id, pos)
-            if t.stream_callback:
-                t.stream_callback(self.tokenizer.decode([ntok]))
-
-        for t in tasks:
-            if t.is_finished(self.tokenizer.stop_ids):
-                if t.stream_callback:
-                    t.stream_callback(STOP)

astrai/inference/scheduler.py

@@ -1,20 +1,85 @@
+"""Inference scheduler for single-GPU continuous batching with paged KV cache."""
+
 import logging
 import threading
-from typing import Any, Dict, List, Optional, Tuple
+import time
+import uuid
+from enum import Enum
+from typing import Any, Callable, Dict, List, Optional, Tuple
 
 import torch
+from torch import Tensor
 
-from astrai.inference.cache import PagedCache
-from astrai.inference.executor import Executor
-from astrai.inference.task import STOP, Task, TaskManager
+from astrai.inference.cache import STOP, PagedCache
+from astrai.inference.sampling import sample
 from astrai.model.automodel import AutoModel
 from astrai.tokenize.tokenizer import AutoTokenizer
 
 logger = logging.getLogger(__name__)
 
 
+class TaskStatus(Enum):
+    """Task states in the continuous batching lifecycle."""
+
+    PENDING = "pending"
+    RUNNING = "running"
+    FINISHED = "finished"
+    ABORTED = "aborted"
+
+
+class Task:
+    """Represents a single generation request with paged KV cache tracking."""
+
+    def __init__(
+        self,
+        task_id: str,
+        prompt_ids: List[int],
+        max_tokens: int = 1024,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+        stream_callback: Optional[Callable[[str], None]] = None,
+    ):
+        self.task_id = task_id
+        self.prompt_ids = prompt_ids
+        self.max_tokens = max_tokens
+        self.temperature = temperature
+        self.top_p = top_p
+        self.top_k = top_k
+
+        self.status = TaskStatus.PENDING
+        self.output_ids: List[int] = []
+        self.input_tokens: int = 0
+        self.output_tokens: int = 0
+        self.page_table: List[int] = []
+        self.n_pages: int = 0
+        self._prefix_cached_tokens: int = 0
+        self.arrival_time = time.time()
+        self.finish_time: Optional[float] = None
+        self.stream_callback = stream_callback
+        self._pages_freed: bool = False
+
+    @property
+    def next_pos(self) -> int:
+        return self.input_tokens + len(self.output_ids)
+
+    def is_finished(self, stop_ids: List[int]) -> bool:
+        if self.output_tokens >= self.max_tokens:
+            return True
+        if self.output_ids and self.output_ids[-1] in stop_ids:
+            return True
+        return False
+
+
 class InferenceScheduler:
-    """Four-phase continuous batching loop: cleanup -> refill -> prefill -> decode."""
+    """Continuous batching scheduler with paged KV cache.
+
+    Runs a background generation loop with four phases per iteration:
+      1. Cleanup finished tasks and release resources.
+      2. Refill active batch from the waiting queue.
+      3. Prefill newly activated tasks.
+      4. Decode the largest same-position group of active tasks.
+    """
 
     def __init__(
         self,
@@ -29,110 +94,319 @@ class InferenceScheduler:
     ):
         config = model.config
 
+        self.model = model
+        self.tokenizer = tokenizer
+        self.max_batch_size = max_batch_size
         self.max_seq_len = max_seq_len or config.max_len
+        self.max_prompt_len = max_prompt_len
+        self.page_size = page_size
         self.device = device or next(model.parameters()).device
         self.dtype = dtype or next(model.parameters()).dtype
 
+        n_kv_heads = config.n_kv_heads
+        head_dim = config.dim // config.n_heads
+        n_layers = config.n_layers
         n_pages = (
             max_batch_size * (self.max_seq_len + page_size) + page_size - 1
         ) // page_size
 
-        self._page_cache = PagedCache(
-            config.n_layers,
+        self.page_cache = PagedCache(
+            n_layers,
             n_pages,
             page_size,
-            config.n_kv_heads,
-            config.dim // config.n_heads,
+            n_kv_heads,
+            head_dim,
             self.device,
             self.dtype,
         )
 
-        self._task_mgr = TaskManager(
-            tokenizer=tokenizer,
-            max_batch_size=max_batch_size,
-            max_seq_len=self.max_seq_len,
-            max_prompt_len=max_prompt_len,
-        )
-
-        self._executor = Executor(
-            model=model,
-            tokenizer=tokenizer,
-            page_cache=self._page_cache,
-            device=self.device,
-            dtype=self.dtype,
-        )
+        self.waiting_queue: List[Task] = []
+        self.active_tasks: List[Task] = []
 
         self._running = False
+        self._task_event = threading.Event()
+        self._lock = threading.Lock()
 
-    def add_task(self, prompt: str, **kwargs) -> str:
-        return self._task_mgr.add_task(prompt, **kwargs)
+        self._total_tasks = 0
+        self._total_tokens = 0
+
+    def _n_pages_for(self, n_tokens: int) -> int:
+        return (n_tokens + self.page_size - 1) // self.page_size
+
+    def add_task(
+        self,
+        prompt: str,
+        max_tokens: int = 1024,
+        temperature: float = 1.0,
+        top_p: float = 1.0,
+        top_k: int = 50,
+        stream_callback: Optional[Callable[[str], None]] = None,
+    ) -> str:
+        task_id = f"task_{int(time.time())}_{uuid.uuid4().hex[:8]}"
+        prompt_ids = self.tokenizer.encode(prompt)
+        if len(prompt_ids) > self.max_prompt_len:
+            prompt_ids = prompt_ids[-self.max_prompt_len :]
+
+        if len(prompt_ids) >= self.max_seq_len:
+            if stream_callback:
+                stream_callback(STOP)
+            return task_id
+
+        max_tokens = min(max_tokens, self.max_seq_len - len(prompt_ids))
+
+        task = Task(
+            task_id=task_id,
+            prompt_ids=prompt_ids,
+            max_tokens=max_tokens,
+            temperature=temperature,
+            top_p=top_p,
+            top_k=top_k,
+            stream_callback=stream_callback,
+        )
+
+        with self._lock:
+            self.waiting_queue.append(task)
+            self._total_tasks += 1
+
+        self._task_event.set()
+        return task_id
 
     def remove_task(self, task_id: str) -> None:
-        for task in self._task_mgr.remove_task(task_id):
-            self._page_cache.task_free(task.task_id)
+        with self._lock:
+            removed_active = [t for t in self.active_tasks if t.task_id == task_id]
+            self.waiting_queue = [t for t in self.waiting_queue if t.task_id != task_id]
+            self.active_tasks = [t for t in self.active_tasks if t.task_id != task_id]
 
-    def get_stats(self) -> Dict[str, Any]:
-        return self._task_mgr.get_stats()
+        for task in removed_active:
+            if not task._pages_freed:
+                self._free_pages(task.page_table)
+                task.page_table.clear()
+                task.n_pages = 0
+                task._pages_freed = True
+
+    def _free_pages(self, indices: List[int]) -> None:
+        for idx in indices:
+            self.page_cache.free(idx)
+
+    def _record_page_hashes(self, task: Task, start_logical_page: int = 0) -> None:
+        full_pages = len(task.prompt_ids) // self.page_size
+        for i in range(start_logical_page, full_pages):
+            self.page_cache.record_page(task.page_table[i], task.prompt_ids, i)
+
+    def _remove_finished_tasks(self) -> None:
+        finished = []
+        for task in self.active_tasks:
+            if task.status == TaskStatus.ABORTED:
+                task.finish_time = time.time()
+                finished.append(task)
+            elif task.is_finished(self.tokenizer.stop_ids):
+                task.status = TaskStatus.FINISHED
+                task.finish_time = time.time()
+                finished.append(task)
+                self._total_tokens += task.output_tokens
+
+        for task in finished:
+            if not task._pages_freed:
+                self._free_pages(task.page_table)
+                task.page_table.clear()
+                task.n_pages = 0
+                task._pages_freed = True
+
+        self.active_tasks = [
+            t
+            for t in self.active_tasks
+            if t.status not in (TaskStatus.FINISHED, TaskStatus.ABORTED)
+        ]
+
+    def _refill_active_batch(self) -> None:
+        available = self.max_batch_size - len(self.active_tasks)
+        if available <= 0:
+            return
+
+        to_add: List[Task] = []
+        with self._lock:
+            n = min(available, len(self.waiting_queue))
+            for _ in range(n):
+                to_add.append(self.waiting_queue.pop(0))
+
+        failed: List[Task] = []
+        for task in to_add:
+            prompt_len = len(task.prompt_ids)
+
+            hit_pages = self.page_cache.lookup_prefix(task.prompt_ids)
+            cached_tokens = len(hit_pages) * self.page_size
+            for p in hit_pages:
+                self.page_cache.inc_ref(p)
+
+            remaining = prompt_len - cached_tokens
+            n_new = self._n_pages_for(remaining) if remaining > 0 else 0
+            new_pages = self.page_cache.alloc_n(n_new) if n_new > 0 else []
+
+            if remaining > 0 and not new_pages:
+                for p in hit_pages:
+                    self.page_cache.free(p)
+                failed.append(task)
+                continue
+
+            task.page_table = hit_pages + new_pages
+            task.n_pages = len(task.page_table)
+            task._prefix_cached_tokens = cached_tokens
+            task.status = TaskStatus.RUNNING
+            self.active_tasks.append(task)
+
+        if failed:
+            with self._lock:
+                self.waiting_queue[:0] = failed
+
+    def _execute_prefill(
+        self, tasks: List[Task], prompt_len: int, start_pos: int = 0
+    ) -> None:
+        tasks = sorted(tasks, key=lambda t: t.task_id)
+        batch_sz = len(tasks)
+
+        seq_len = prompt_len - start_pos
+        input_ids = torch.empty(batch_sz, seq_len, dtype=torch.long, device=self.device)
+        input_mask = torch.ones(
+            batch_sz, prompt_len, dtype=torch.bool, device=self.device
+        )
+
+        for i, t in enumerate(tasks):
+            input_ids[i] = torch.tensor(
+                t.prompt_ids[start_pos:prompt_len], device=self.device
+            )
+
+        page_tables = self._make_page_table_tensor(tasks)
+
+        with torch.inference_mode():
+            self.model(
+                input_ids,
+                input_mask=input_mask,
+                start_pos=start_pos,
+                paged_cache=self.page_cache.bind(page_tables, total_len=prompt_len),
+            )
+
+        start_logical_page = start_pos // self.page_size
+        for t in tasks:
+            self._record_page_hashes(t, start_logical_page=start_logical_page)
+
+    def _execute_decode(self, tasks: List[Task], start_pos: int) -> None:
+        if not tasks:
+            return
+
+        tasks = sorted(tasks, key=lambda t: t.task_id)
+
+        valid: List[Task] = []
+        for t in tasks:
+            if self._maybe_alloc_page(t, start_pos):
+                valid.append(t)
+            else:
+                t.status = TaskStatus.ABORTED
+                if t.stream_callback:
+                    t.stream_callback(STOP)
+
+        if not valid:
+            return
+
+        tasks = valid
+        batch_sz = len(tasks)
+
+        input_ids = torch.tensor(
+            [t.output_ids[-1] if t.output_ids else t.prompt_ids[-1] for t in tasks],
+            dtype=torch.long,
+            device=self.device,
+        )
+
+        active_mask = torch.ones((batch_sz, 1), dtype=torch.bool, device=self.device)
+
+        page_tables = self._make_page_table_tensor(tasks)
+        total_len = start_pos + 1
+
+        temperatures = torch.tensor([t.temperature for t in tasks], device=self.device)
+        top_ks = torch.tensor([t.top_k for t in tasks], device=self.device)
+        top_ps = torch.tensor([t.top_p for t in tasks], device=self.device)
+
+        with torch.inference_mode():
+            outputs = self.model(
+                input_ids.unsqueeze(1),
+                input_mask=active_mask,
+                paged_cache=self.page_cache.bind(page_tables, total_len=total_len),
+                start_pos=start_pos,
+            )
+            logits = outputs["logits"][:, -1, :]
+
+        next_tokens = sample(
+            logits,
+            temperature=temperatures,
+            top_k=top_ks,
+            top_p=top_ps,
+        ).tolist()
+
+        for t, ntok in zip(tasks, next_tokens):
+            t.output_ids.append(ntok)
+            t.output_tokens += 1
+            pos = t.input_tokens + t.output_tokens
+            self._maybe_alloc_page(t, pos)
+            if t.stream_callback:
+                t.stream_callback(self.tokenizer.decode([ntok]))
+
+        for t in tasks:
+            if t.is_finished(self.tokenizer.stop_ids):
+                if t.stream_callback:
+                    t.stream_callback(STOP)
+
+    def _make_page_table_tensor(self, tasks: List[Task]) -> Tensor:
+        max_pages = max(t.n_pages for t in tasks)
+        rows = [t.page_table + [-1] * (max_pages - t.n_pages) for t in tasks]
+        return torch.tensor(rows, dtype=torch.long, device=self.device)
+
+    def _maybe_alloc_page(self, task: Task, pos: int) -> bool:
+        needed = self._n_pages_for(pos + 1)
+        while task.n_pages < needed:
+            p = self.page_cache.alloc()
+            if p < 0:
+                return False
+            task.page_table.append(p)
+            task.n_pages += 1
+        return True
 
     def _run_generation_loop(self) -> None:
         try:
             while self._running:
-                finished = self._task_mgr.remove_finished_tasks(
-                    self._task_mgr.tokenizer.stop_ids
-                )
-                for task in finished:
-                    self._page_cache.task_free(task.task_id)
+                self._remove_finished_tasks()
+                self._refill_active_batch()
 
-                available = self._task_mgr.max_batch_size - len(
-                    self._task_mgr.active_tasks
-                )
-                if available > 0:
-                    candidates = self._task_mgr.pull_candidates(available)
-                    failed = []
-                    for task in candidates:
-                        if self._page_cache.task_alloc(task.task_id, task.prompt_ids):
-                            self._task_mgr.activate(task)
-                        else:
-                            failed.append(task)
-                    if failed:
-                        self._task_mgr.return_to_waiting(failed)
-
-                if not self._task_mgr.has_work():
-                    self._task_mgr.wait_for_tasks(timeout=1.0)
+                if not self.active_tasks and not self.waiting_queue:
+                    self._task_event.clear()
+                    self._task_event.wait(timeout=1.0)
                     continue
 
-                to_prefill = [
-                    t for t in self._task_mgr.active_tasks if t.output_tokens == 0
-                ]
+                to_prefill = [t for t in self.active_tasks if t.output_tokens == 0]
                 if to_prefill:
                     for t in to_prefill:
                         t.input_tokens = len(t.prompt_ids)
 
                     groups: Dict[Tuple[int, int], List[Task]] = {}
                     for t in to_prefill:
-                        key = (
-                            len(t.prompt_ids),
-                            self._page_cache.task_cached(t.task_id),
-                        )
+                        key = (len(t.prompt_ids), t._prefix_cached_tokens)
                         groups.setdefault(key, []).append(t)
 
                     for (prompt_len, start_pos), group in groups.items():
-                        self._executor.execute_prefill(group, prompt_len, start_pos)
+                        if start_pos < prompt_len:
+                            self._execute_prefill(group, prompt_len, start_pos)
 
                 pos_groups: Dict[int, List[Task]] = {}
-                for t in self._task_mgr.active_tasks:
+                for t in self.active_tasks:
                     pos_groups.setdefault(t.next_pos, []).append(t)
 
                 if pos_groups:
                     best_pos = max(pos_groups, key=lambda p: len(pos_groups[p]))
-                    self._executor.execute_decode(pos_groups[best_pos], best_pos)
-
+                    self._execute_decode(pos_groups[best_pos], best_pos)
         except Exception as e:
             logger.error(f"Scheduler loop crashed: {e}", exc_info=True)
-            for task in self._task_mgr.active_tasks:
+            for task in self.active_tasks:
                 if task.stream_callback:
                     task.stream_callback(STOP)
-            for task in self._task_mgr.waiting_queue:
+            for task in self.waiting_queue:
                 if task.stream_callback:
                     task.stream_callback(STOP)
             raise
@@ -146,10 +420,18 @@ class InferenceScheduler:
 
     def stop(self) -> None:
         self._running = False
-        self._task_mgr.wake()
+        self._task_event.set()
         if hasattr(self, "_loop_thread"):
             self._loop_thread.join(timeout=2.0)
-        self._task_mgr.waiting_queue.clear()
-        self._task_mgr.active_tasks.clear()
+        self.waiting_queue.clear()
+        self.active_tasks.clear()
         if torch.cuda.is_available():
             torch.cuda.empty_cache()
+
+    def get_stats(self) -> Dict[str, Any]:
+        return {
+            "total_tasks": self._total_tasks,
+            "total_tokens": self._total_tokens,
+            "active_tasks": len(self.active_tasks),
+            "waiting_queue": len(self.waiting_queue),
+        }

astrai/inference/task.py

@@ -1,184 +0,0 @@
-import logging
-import threading
-import time
-import uuid
-from enum import Enum
-from typing import Any, Callable, Dict, List, Optional
-
-from astrai.tokenize.tokenizer import AutoTokenizer
-
-logger = logging.getLogger(__name__)
-
-STOP = object()
-
-
-class TaskStatus(Enum):
-    """Task lifecycle states."""
-
-    PENDING = "pending"
-    RUNNING = "running"
-    FINISHED = "finished"
-    ABORTED = "aborted"
-
-
-class Task:
-    """Single generation request: prompt, sampling params, output state."""
-
-    def __init__(
-        self,
-        task_id: str,
-        prompt_ids: List[int],
-        max_tokens: int = 1024,
-        temperature: float = 1.0,
-        top_p: float = 1.0,
-        top_k: int = 50,
-        stream_callback: Optional[Callable[[str], None]] = None,
-    ):
-        self.task_id = task_id
-        self.prompt_ids = prompt_ids
-        self.max_tokens = max_tokens
-        self.temperature = temperature
-        self.top_p = top_p
-        self.top_k = top_k
-
-        self.status = TaskStatus.PENDING
-        self.output_ids: List[int] = []
-        self.input_tokens: int = 0
-        self.output_tokens: int = 0
-        self.arrival_time = time.time()
-        self.finish_time: Optional[float] = None
-        self.stream_callback = stream_callback
-
-    @property
-    def next_pos(self) -> int:
-        return self.input_tokens + len(self.output_ids)
-
-    def is_finished(self, stop_ids: List[int]) -> bool:
-        if self.output_tokens >= self.max_tokens:
-            return True
-        if self.output_ids and self.output_ids[-1] in stop_ids:
-            return True
-        return False
-
-
-class TaskManager:
-    """Thread-safe task queues and lifecycle transitions (no page ops)."""
-
-    def __init__(
-        self,
-        tokenizer: AutoTokenizer,
-        max_batch_size: int = 16,
-        max_seq_len: int = 8192,
-        max_prompt_len: int = 512,
-    ):
-        self.tokenizer = tokenizer
-        self.max_batch_size = max_batch_size
-        self.max_seq_len = max_seq_len
-        self.max_prompt_len = max_prompt_len
-
-        self.waiting_queue: List[Task] = []
-        self.active_tasks: List[Task] = []
-
-        self._task_event = threading.Event()
-        self._lock = threading.Lock()
-
-        self._total_tasks = 0
-        self._total_tokens = 0
-
-    def add_task(
-        self,
-        prompt: str,
-        max_tokens: int = 1024,
-        temperature: float = 1.0,
-        top_p: float = 1.0,
-        top_k: int = 50,
-        stream_callback: Optional[Callable[[str], None]] = None,
-    ) -> str:
-        task_id = f"task_{int(time.time())}_{uuid.uuid4().hex[:8]}"
-        prompt_ids = self.tokenizer.encode(prompt)
-        if len(prompt_ids) > self.max_prompt_len:
-            prompt_ids = prompt_ids[-self.max_prompt_len :]
-
-        if len(prompt_ids) >= self.max_seq_len:
-            if stream_callback:
-                stream_callback(STOP)
-            return task_id
-
-        max_tokens = min(max_tokens, self.max_seq_len - len(prompt_ids))
-
-        task = Task(
-            task_id=task_id,
-            prompt_ids=prompt_ids,
-            max_tokens=max_tokens,
-            temperature=temperature,
-            top_p=top_p,
-            top_k=top_k,
-            stream_callback=stream_callback,
-        )
-
-        with self._lock:
-            self.waiting_queue.append(task)
-            self._total_tasks += 1
-
-        self._task_event.set()
-        return task_id
-
-    def remove_task(self, task_id: str) -> List[Task]:
-        with self._lock:
-            removed_active = [t for t in self.active_tasks if t.task_id == task_id]
-            self.waiting_queue = [t for t in self.waiting_queue if t.task_id != task_id]
-            self.active_tasks = [t for t in self.active_tasks if t.task_id != task_id]
-        return removed_active
-
-    def get_stats(self) -> Dict[str, Any]:
-        return {
-            "total_tasks": self._total_tasks,
-            "total_tokens": self._total_tokens,
-            "active_tasks": len(self.active_tasks),
-            "waiting_queue": len(self.waiting_queue),
-        }
-
-    def remove_finished_tasks(self, stop_ids: List[int]) -> List[Task]:
-        finished = []
-        for task in self.active_tasks:
-            if task.status == TaskStatus.ABORTED:
-                task.finish_time = time.time()
-                finished.append(task)
-            elif task.is_finished(stop_ids):
-                task.status = TaskStatus.FINISHED
-                task.finish_time = time.time()
-                finished.append(task)
-                self._total_tokens += task.output_tokens
-
-        self.active_tasks = [
-            t
-            for t in self.active_tasks
-            if t.status not in (TaskStatus.FINISHED, TaskStatus.ABORTED)
-        ]
-        return finished
-
-    def pull_candidates(self, n: int) -> List[Task]:
-        to_add: List[Task] = []
-        with self._lock:
-            take = min(n, len(self.waiting_queue))
-            for _ in range(take):
-                to_add.append(self.waiting_queue.pop(0))
-        return to_add
-
-    def activate(self, task: Task) -> None:
-        task.status = TaskStatus.RUNNING
-        self.active_tasks.append(task)
-
-    def return_to_waiting(self, tasks: List[Task]) -> None:
-        with self._lock:
-            self.waiting_queue[:0] = tasks
-
-    def has_work(self) -> bool:
-        return bool(self.active_tasks or self.waiting_queue)
-
-    def wait_for_tasks(self, timeout: float = 1.0) -> None:
-        self._task_event.clear()
-        self._task_event.wait(timeout=timeout)
-
-    def wake(self) -> None:
-        self._task_event.set()