ViperEkura
47b3ed4e44
- InferenceEngine/Scheduler accept an explicit backend - capture request-level attn_backend context onto Task - split prefill/decode batches by backend instance - ASTR_BACKEND env overrides ContextVar as process-wide policy - report resolved backend and CUDA-graph state in benchmark
431 lines
14 KiB
Python
431 lines
14 KiB
Python
import logging
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import time
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from contextlib import contextmanager
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from dataclasses import dataclass
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from typing import List, Optional
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import torch
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from torch import Tensor
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from astrai.extension.attention_backend import (
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CudaBackend,
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get_backend,
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)
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from astrai.inference.cache import PagePool, TaskCacheManager
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from astrai.inference.runtime.graph import CudaGraphContext
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from astrai.inference.runtime.sample import sample
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from astrai.inference.task import Task
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from astrai.inference.workspace import InferenceWorkspace
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from astrai.model.automodel import AutoModel
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logger = logging.getLogger(__name__)
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@contextmanager
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def timed(label: str, log: Optional[logging.Logger] = None):
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"""GPU-precise timer via CUDA events; falls back to perf_counter on CPU."""
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log = log or logger
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if not log.isEnabledFor(logging.DEBUG):
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yield
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return
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use_cuda = torch.cuda.is_available()
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if use_cuda:
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start = torch.cuda.Event(enable_timing=True)
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end = torch.cuda.Event(enable_timing=True)
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start.record()
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else:
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tic = time.perf_counter()
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yield
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if use_cuda:
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end.record()
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torch.cuda.synchronize()
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elapsed_ms = start.elapsed_time(end)
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else:
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elapsed_ms = (time.perf_counter() - tic) * 1000
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log.debug("%s %.2fms", label, elapsed_ms)
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@dataclass
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class SamplingBatchInfo:
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"""Per-batch sampling parameters, cached across decode steps.
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Sampling params are constant for a given ordered task set, so they are
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built once (pinned-memory async H2D) and reused until the task set
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changes. ``top_ks`` is int32 to match the native consumers.
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"""
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temperatures: Tensor # float32 [B]
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top_ks: Tensor # int32 [B]
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top_ps: Tensor # float32 [B]
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freq_penalties: Tensor # float32 [B]
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has_freq: bool # any frequency_penalty != 0 (avoids per-step GPU .any())
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@dataclass
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class DecodeSteadyState:
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"""Cached decode metadata for the steady-state case.
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When the same ordered task set decodes one token per step, sampling
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params and task signature are reused; only positions advance by 1.
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"""
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task_sig: tuple
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positions: list[int]
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sampling_info: SamplingBatchInfo
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def _build_sampling_batch_info(tasks: List[Task], device) -> SamplingBatchInfo:
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pin = str(device).startswith("cuda")
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freq_penalties = torch.tensor(
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[t.frequency_penalty for t in tasks], dtype=torch.float32, pin_memory=pin
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).to(device, non_blocking=True)
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return SamplingBatchInfo(
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temperatures=torch.tensor(
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[t.temperature for t in tasks], dtype=torch.float32, pin_memory=pin
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).to(device, non_blocking=True),
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top_ks=torch.tensor(
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[t.top_k for t in tasks], dtype=torch.int32, pin_memory=pin
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).to(device, non_blocking=True),
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top_ps=torch.tensor(
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[t.top_p for t in tasks], dtype=torch.float32, pin_memory=pin
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).to(device, non_blocking=True),
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freq_penalties=freq_penalties,
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has_freq=bool((freq_penalties != 0).any()),
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)
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def _warmup_cuda_graphs(
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model: AutoModel,
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pool: PagePool,
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task_cache: TaskCacheManager,
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ws: InferenceWorkspace,
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gctx: CudaGraphContext,
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max_batch_size: int,
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prompt_len: int = 1,
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device: Optional[str] = None,
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):
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dev = device or next(model.parameters()).device
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# Prefill warmup: cuBLAS auto-tunes for the actual prompt-length tensor
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# shapes on first call (F.linear is the dominant cost). This also warms
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# up the CUDA context (driver init) and compiles the graph-capture trace
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# that follows. Custom .so kernels do NOT need this — they are pre-built.
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warmup_len = 64
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tid = "_warmup_prefill"
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if task_cache.task_alloc(tid, list(range(warmup_len))):
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with (
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torch.inference_mode(),
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timed("warmup prefill", logger),
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):
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kv = task_cache.bind([tid], ws, start_pos=0)
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ids_in = torch.arange(warmup_len, device=dev).unsqueeze(0)
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pos_in = ids_in
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model(
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ids_in,
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input_mask=pos_in.unsqueeze(-1) >= torch.arange(warmup_len, device=dev),
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kv_cache=kv,
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position_ids=pos_in,
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)
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task_cache.task_free(tid)
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batch_sizes = [1]
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n = 2
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while n <= max_batch_size:
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batch_sizes.append(n)
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n *= 2
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if max_batch_size not in batch_sizes:
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batch_sizes.append(max_batch_size)
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for b in batch_sizes:
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task_ids = [f"_warmup_decode_{b}_{i}" for i in range(b)]
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prompt_tokens = [list(range(prompt_len)) for _ in range(b)]
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alloc_ok = True
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for tid, pt in zip(task_ids, prompt_tokens):
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if not task_cache.task_alloc(tid, pt):
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alloc_ok = False
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break
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if not alloc_ok:
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for tid in task_ids:
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task_cache.task_free(tid)
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continue
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with (
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torch.inference_mode(),
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timed(f"warmup decode b={b}", logger),
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):
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for step in range(2):
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seq_pos = step
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ws.position_ids[:b] = seq_pos
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for tid in task_ids:
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task_cache.task_extend(tid, seq_pos)
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kv = task_cache.bind(task_ids, ws)
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input_mask = ws.decode_mask(ws.position_ids[:b], ws.max_seq_len)
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ids_buf = ws.fill_input_ids([step] * b)
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gctx.forward(
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model,
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key=(b,),
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input_ids=ids_buf.unsqueeze(1),
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input_mask=input_mask,
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kv_cache=kv,
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position_ids=ws.position_ids[:b].unsqueeze(1),
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)
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for tid in task_ids:
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task_cache.task_free(tid)
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torch.cuda.synchronize()
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class Executor:
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"""Model forward passes for prefill and decode phases."""
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def __init__(
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self,
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model: AutoModel,
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kv_cache: PagePool,
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task_cache: TaskCacheManager,
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device: Optional[str] = None,
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dtype: Optional[torch.dtype] = None,
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enable_cuda_graph: bool = True,
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):
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self.model = model
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self.kv_cache = kv_cache
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self.task_cache = task_cache
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self.device = device or next(model.parameters()).device
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self.dtype = dtype or next(model.parameters()).dtype
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# Per-step decode cache for the steady-state case (same ordered
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# task set decodes one token per step). Sampling params stay
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# constant; only positions advance.
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self._decode_cache: Optional[DecodeSteadyState] = None
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# Pre-allocated fixed-shape buffers for the decode hot path
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# (input_ids, decode mask, KV bind metadata). Eagerly sized at init
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# so the workspace is CUDA-graph-capture friendly — no allocation
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# during capture.
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config = model.config
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max_q_heads = config.num_attention_heads
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head_dim = config.hidden_size // config.num_attention_heads
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backend = get_backend()
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self._graph_supported = backend.supports_graph() and CudaBackend.supports(
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head_dim=head_dim
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)
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self._workspace = InferenceWorkspace(
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max_batch_size=kv_cache.max_batch_size,
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max_seq_len=kv_cache.max_seq_len,
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max_q_heads=max_q_heads,
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head_dim=head_dim,
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device=self.device,
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dtype=self.dtype,
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)
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# CUDA-graph capture: one graph per (batch_size,) key.
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# Enabled at init-time via _warmup_cuda_graphs for CudaBackend
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# on supported head_dims; left disabled otherwise.
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self._graph_ctx = CudaGraphContext()
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if enable_cuda_graph:
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self._try_enable_cuda_graph()
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def _try_enable_cuda_graph(self):
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if not self._graph_supported:
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return
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self._graph_ctx.set_enabled(True)
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_warmup_cuda_graphs(
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self.model,
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self.kv_cache,
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self.task_cache,
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self._workspace,
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self._graph_ctx,
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max_batch_size=self.kv_cache.max_batch_size,
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device=self.device,
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)
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@property
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def cuda_graph_enabled(self) -> bool:
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return self._graph_ctx.enabled and self._graph_supported
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def _sample_logits(
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self,
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logits: Tensor,
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tasks: List[Task],
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return_logprobs: bool = False,
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info: Optional[SamplingBatchInfo] = None,
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):
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info = info or _build_sampling_batch_info(tasks, self.device)
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if info.has_freq:
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history_lists = [
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t.prompt_ids[-t.rep_window :] + t.output_ids for t in tasks
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]
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history_lens = [len(ids) for ids in history_lists]
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max_len = max(history_lens, default=0)
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padded_ids = torch.zeros(
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len(tasks), max_len, dtype=torch.long, device=self.device
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)
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padded_mask = torch.zeros(
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len(tasks), max_len, dtype=torch.bool, device=self.device
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)
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for i, ids in enumerate(history_lists):
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length = len(ids)
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padded_ids[i, :length] = torch.as_tensor(
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ids, dtype=torch.long, device=self.device
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)
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padded_mask[i, :length] = True
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else:
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padded_ids = None
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padded_mask = None
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result = sample(
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logits,
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temperature=info.temperatures,
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top_k=info.top_ks,
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top_p=info.top_ps,
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frequency_penalty=info.freq_penalties,
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input_ids=padded_ids,
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input_mask=padded_mask,
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return_logprobs=return_logprobs,
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)
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if not return_logprobs:
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return result.tolist()
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tokens, logprobs = result
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tokens_list = tokens.tolist()
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logprobs_list = logprobs.tolist()
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for task, logprob in zip(tasks, logprobs_list):
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task.output_logprobs.append(float(logprob))
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return list(zip(tokens_list, logprobs_list))
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def execute_prefill(
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self,
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tasks: List[Task],
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prompt_len: int,
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start_pos: int = 0,
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return_logprobs: bool = False,
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):
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if start_pos >= prompt_len:
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return []
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tasks = sorted(tasks, key=lambda t: t.task_id)
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batch_sz = len(tasks)
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input_ids = torch.tensor(
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[t.prompt_ids[start_pos:prompt_len] for t in tasks],
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dtype=torch.long,
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device=self.device,
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)
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task_ids = [t.task_id for t in tasks]
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position_ids = (
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torch.arange(start_pos, prompt_len, dtype=torch.long, device=self.device)
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.unsqueeze(0)
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.expand(batch_sz, -1)
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)
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input_mask = position_ids.unsqueeze(-1) >= torch.arange(
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prompt_len, device=self.device
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)
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with (
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torch.inference_mode(),
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timed(f"execute_prefill b={batch_sz} prompt_len={prompt_len}", logger),
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):
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outputs = self.model(
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input_ids,
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input_mask=input_mask,
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position_ids=position_ids,
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kv_cache=self.task_cache.bind(
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task_ids,
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self._workspace,
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start_pos=start_pos,
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),
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)
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logits = outputs["logits"][:, -1, :]
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return tasks, self._sample_logits(logits, tasks, return_logprobs)
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def execute_decode(
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self, tasks: List[Task], return_logprobs: bool = False
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) -> List[int]:
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"""Decode next token for each task.
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Args:
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return_logprobs: When ``True``, also record (and return)
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the log-probability of each sampled token under the
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post-strategy sampling distribution. The logprob is
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appended to ``task.output_logprobs`` and the return
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list becomes ``List[Tuple[int, float]]``.
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Returns:
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``List[int]`` of sampled token IDs, or
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``List[Tuple[int, float]]`` of ``(token_id, logprob)`` when
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``return_logprobs`` is ``True``.
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"""
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if not tasks:
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return []
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b = len(tasks)
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ws = self._workspace
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# ---- pre-replay: update input buffers in-place ----
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input_ids = ws.fill_input_ids(
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[t.output_ids[-1] if t.output_ids else t.prompt_ids[-1] for t in tasks]
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)
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task_ids = [t.task_id for t in tasks]
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cur_positions = [t.next_pos for t in tasks]
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kv_cache = self.task_cache.bind(task_ids, ws)
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task_sig = tuple(task_ids)
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reuse_decode_state = (
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self.task_cache.bind_was_steady
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and self._decode_cache is not None
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and self._decode_cache.task_sig == task_sig
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)
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if reuse_decode_state:
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info = self._decode_cache.sampling_info
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ws.position_ids[:b] += 1
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else:
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info = _build_sampling_batch_info(tasks, self.device)
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ws.position_ids[:b].copy_(
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torch.tensor(cur_positions, dtype=torch.long, device=self.device)
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)
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self._decode_cache = DecodeSteadyState(task_sig, cur_positions, info)
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total_len = max(cur_positions) + 1
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input_mask = ws.decode_mask(ws.position_ids[:b], total_len)
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# ---- forward (graph replay or live run + capture) ----
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use_graph = (
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self._graph_ctx.enabled
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and self._graph_supported
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and get_backend().supports_graph()
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)
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key = (b,)
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if use_graph:
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input_mask = ws.decode_mask(ws.position_ids[:b], ws.max_seq_len)
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with (
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torch.inference_mode(),
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timed(f"execute_decode forward b={b}", logger),
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):
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if use_graph:
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outputs = self._graph_ctx.forward(
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self.model,
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key=key,
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input_ids=input_ids.unsqueeze(1),
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input_mask=input_mask,
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kv_cache=kv_cache,
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position_ids=ws.position_ids[:b].unsqueeze(1),
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)
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else:
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outputs = self.model(
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input_ids.unsqueeze(1),
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input_mask=input_mask,
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kv_cache=kv_cache,
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position_ids=ws.position_ids[:b].unsqueeze(1),
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)
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logits = outputs["logits"][:, -1, :]
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return self._sample_logits(logits, tasks, return_logprobs, info=info)
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