ViperEkura
3fa7e66676
- TaskCacheRegistry -> TaskCacheManager (independent, held by scheduler) - TaskCacheState co-locates 5 parallel dicts into one dataclass - AllocationStrategy base class + PagedStrategy subclass (page_size is a parameter) - _rollback() helper for unified cleanup (no duplicate free paths) - Task._kv_len + prefill_done property (explicit, no output_tokens proxy) - Steady-state detection single-sourced in TaskCacheManager.bind() - PagePool is now pure physical layer (no task knowledge) - Removed dead _page_to_hash dict in RadixCache
503 lines
16 KiB
Python
503 lines
16 KiB
Python
import json
|
|
from pathlib import Path
|
|
from typing import Optional, Union
|
|
|
|
import click
|
|
import torch
|
|
|
|
from astrai.config import BaseModelConfig, ConfigFactory
|
|
from astrai.extension import ATTN_BACKEND, AttentionBackendFactory, attn_backend
|
|
from astrai.inference.core.cache import PagePool, TaskCacheManager
|
|
from astrai.inference.core.graph import CudaGraphContext
|
|
from astrai.inference.core.workspace import InferenceWorkspace
|
|
from astrai.model import AutoModel, AutoRegressiveLM
|
|
|
|
_DTYPES = ["bfloat16", "float16", "float32"]
|
|
_CACHES = ["contiguous", "paged"]
|
|
_BACKENDS = AttentionBackendFactory.list_registered()
|
|
|
|
# Default 1B GQA preset matching the project checkpoint architecture.
|
|
_DEFAULT_CONFIG = {
|
|
"vocab_size": 100000,
|
|
"hidden_size": 1536,
|
|
"num_hidden_layers": 24,
|
|
"intermediate_size": 6912,
|
|
"num_attention_heads": 24,
|
|
"num_key_value_heads": 4,
|
|
"max_position_embeddings": 32768,
|
|
"rms_norm_eps": 1e-05,
|
|
"tie_word_embeddings": False,
|
|
}
|
|
|
|
|
|
class BenchmarkResult:
|
|
def __init__(
|
|
self,
|
|
name: str,
|
|
batch_size: int,
|
|
seq_len: int,
|
|
tokens_per_second: float,
|
|
latency_ms: float,
|
|
metadata: Optional[dict] = None,
|
|
):
|
|
self.name = name
|
|
self.batch_size = batch_size
|
|
self.seq_len = seq_len
|
|
self.tokens_per_second = tokens_per_second
|
|
self.latency_ms = latency_ms
|
|
self.metadata = metadata or {}
|
|
|
|
|
|
class GenerationBenchmark:
|
|
def __init__(
|
|
self,
|
|
model: AutoModel,
|
|
config: BaseModelConfig,
|
|
device: str = "cuda",
|
|
dtype: torch.dtype = torch.bfloat16,
|
|
cache_type: str = "contiguous",
|
|
backend: Union[str, ATTN_BACKEND] = ATTN_BACKEND.CUDA,
|
|
cuda_graph: bool = False,
|
|
):
|
|
self.device = device
|
|
self.dtype = dtype
|
|
self.cache_type = cache_type
|
|
self.model = model
|
|
self.config = config
|
|
self.backend = backend
|
|
self.cuda_graph = cuda_graph
|
|
|
|
def _make_pool(self, batch_size: int, max_seq_len: int) -> PagePool:
|
|
return PagePool(
|
|
n_layers=self.config.num_hidden_layers,
|
|
n_kv_heads=self.config.num_key_value_heads,
|
|
head_dim=self.config.hidden_size // self.config.num_attention_heads,
|
|
max_batch_size=batch_size,
|
|
max_seq_len=max_seq_len,
|
|
device=self.device,
|
|
dtype=self.dtype,
|
|
page_size=1,
|
|
n_tokens=None,
|
|
)
|
|
|
|
@staticmethod
|
|
def _make_workspace(pool: PagePool, config: BaseModelConfig) -> InferenceWorkspace:
|
|
return InferenceWorkspace(
|
|
pool.max_batch_size,
|
|
pool.max_seq_len,
|
|
max_q_heads=config.num_attention_heads,
|
|
head_dim=config.hidden_size // config.num_attention_heads,
|
|
device=pool.device,
|
|
dtype=pool.dtype,
|
|
)
|
|
|
|
@staticmethod
|
|
def _make_task_cache(pool: PagePool) -> TaskCacheManager:
|
|
return TaskCacheManager(
|
|
strategy=pool._strategy,
|
|
req_pool=pool._req_pool,
|
|
max_seq_len=pool.max_seq_len,
|
|
pool=pool,
|
|
)
|
|
|
|
def _run_prefill(
|
|
self,
|
|
pool: PagePool,
|
|
task_cache: TaskCacheManager,
|
|
batch_size: int,
|
|
prompt_len: int,
|
|
workspace: InferenceWorkspace,
|
|
) -> list:
|
|
input_ids = torch.randint(
|
|
0, self.config.vocab_size, (batch_size, prompt_len), device=self.device
|
|
)
|
|
position_ids = (
|
|
torch.arange(0, prompt_len, dtype=torch.long, device=self.device)
|
|
.unsqueeze(0)
|
|
.expand(batch_size, -1)
|
|
)
|
|
input_mask = position_ids.unsqueeze(-1) >= torch.arange(
|
|
prompt_len, device=self.device
|
|
)
|
|
|
|
task_ids = [f"bench_{i}" for i in range(batch_size)]
|
|
for tid in task_ids:
|
|
task_cache.task_alloc(tid, list(range(prompt_len)))
|
|
|
|
kv_cache = task_cache.bind(task_ids, workspace, self.device, start_pos=0)
|
|
with torch.inference_mode(), attn_backend(self.backend):
|
|
self.model(
|
|
input_ids,
|
|
input_mask=input_mask,
|
|
kv_cache=kv_cache,
|
|
position_ids=position_ids,
|
|
)
|
|
torch.cuda.synchronize()
|
|
return task_ids
|
|
|
|
def _run_decode_step(
|
|
self,
|
|
pool: PagePool,
|
|
task_cache: TaskCacheManager,
|
|
task_ids: list,
|
|
seq_len: int,
|
|
workspace: InferenceWorkspace,
|
|
):
|
|
batch_size = len(task_ids)
|
|
input_ids = torch.randint(
|
|
0, self.config.vocab_size, (batch_size, 1), device=self.device
|
|
)
|
|
position_ids = torch.tensor(
|
|
[[seq_len] for _ in range(batch_size)], dtype=torch.long, device=self.device
|
|
)
|
|
total_len = seq_len + 1
|
|
for tid in task_ids:
|
|
task_cache.task_extend(tid, seq_len)
|
|
input_mask = position_ids[:, :, None] >= torch.arange(
|
|
total_len, device=self.device
|
|
)
|
|
kv_cache = task_cache.bind(task_ids, workspace, self.device)
|
|
with torch.inference_mode(), attn_backend(self.backend):
|
|
self.model(
|
|
input_ids,
|
|
input_mask=input_mask,
|
|
kv_cache=kv_cache,
|
|
position_ids=position_ids,
|
|
)
|
|
|
|
def run_prefill_benchmark(
|
|
self,
|
|
batch_size: int = 4,
|
|
prompt_length: int = 512,
|
|
num_trials: int = 5,
|
|
) -> BenchmarkResult:
|
|
import time
|
|
|
|
pool = self._make_pool(batch_size, prompt_length)
|
|
workspace = self._make_workspace(pool, self.config)
|
|
task_cache = self._make_task_cache(pool)
|
|
task_ids = [f"bench_prefill_{i}" for i in range(batch_size)]
|
|
for tid in task_ids:
|
|
task_cache.task_alloc(tid, list(range(prompt_length)))
|
|
|
|
input_ids = torch.randint(
|
|
0, self.config.vocab_size, (batch_size, prompt_length), device=self.device
|
|
)
|
|
position_ids = (
|
|
torch.arange(0, prompt_length, dtype=torch.long, device=self.device)
|
|
.unsqueeze(0)
|
|
.expand(batch_size, -1)
|
|
)
|
|
input_mask = position_ids.unsqueeze(-1) >= torch.arange(
|
|
prompt_length, device=self.device
|
|
)
|
|
kv_cache = task_cache.bind(task_ids, workspace, self.device, start_pos=0)
|
|
|
|
for _ in range(3):
|
|
with torch.inference_mode(), attn_backend(self.backend):
|
|
self.model(
|
|
input_ids,
|
|
input_mask=input_mask,
|
|
kv_cache=kv_cache,
|
|
position_ids=position_ids,
|
|
)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.perf_counter()
|
|
for _ in range(num_trials):
|
|
with torch.inference_mode(), attn_backend(self.backend):
|
|
self.model(
|
|
input_ids,
|
|
input_mask=input_mask,
|
|
kv_cache=kv_cache,
|
|
position_ids=position_ids,
|
|
)
|
|
torch.cuda.synchronize()
|
|
elapsed = time.perf_counter() - t0
|
|
tokens = batch_size * prompt_length * num_trials
|
|
tps = tokens / elapsed
|
|
return BenchmarkResult(
|
|
name="prefill",
|
|
batch_size=batch_size,
|
|
seq_len=prompt_length,
|
|
tokens_per_second=tps,
|
|
latency_ms=elapsed / num_trials * 1000,
|
|
metadata={"benchmark_type": "prefill", "num_trials": num_trials},
|
|
)
|
|
|
|
def run_decoding_benchmark(
|
|
self,
|
|
batch_size: int = 4,
|
|
prompt_length: int = 512,
|
|
gen_length: int = 128,
|
|
num_trials: int = 5,
|
|
) -> BenchmarkResult:
|
|
if self.cuda_graph and self.backend == "cuda":
|
|
return self._run_graph_decode_benchmark(
|
|
batch_size, prompt_length, gen_length, num_trials
|
|
)
|
|
return self._run_plain_decode_benchmark(
|
|
batch_size, prompt_length, gen_length, num_trials
|
|
)
|
|
|
|
def _run_graph_decode_benchmark(
|
|
self,
|
|
batch_size: int,
|
|
prompt_length: int,
|
|
gen_length: int,
|
|
num_trials: int,
|
|
) -> BenchmarkResult:
|
|
import time
|
|
|
|
max_seq_len = prompt_length + 5 + gen_length * num_trials
|
|
pool = self._make_pool(batch_size, max_seq_len)
|
|
workspace = self._make_workspace(pool, self.config)
|
|
task_cache = self._make_task_cache(pool)
|
|
task_ids = self._run_prefill(
|
|
pool, task_cache, batch_size, prompt_length, workspace
|
|
)
|
|
|
|
b = batch_size
|
|
input_ids_buf = torch.zeros(b, 1, dtype=torch.long, device=self.device)
|
|
position_ids_buf = torch.zeros(b, dtype=torch.long, device=self.device)
|
|
arange = torch.arange(max_seq_len, device=self.device)
|
|
|
|
gctx = CudaGraphContext(enabled=True)
|
|
graph_key = (b,)
|
|
|
|
def _decode_graph_step(seq_len):
|
|
input_ids_buf.copy_(
|
|
torch.randint(0, self.config.vocab_size, (b, 1), device=self.device)
|
|
)
|
|
position_ids_buf[:] = seq_len
|
|
for tid in task_ids:
|
|
task_cache.task_extend(tid, seq_len)
|
|
kv_cache = task_cache.bind(task_ids, workspace, self.device)
|
|
|
|
input_mask = torch.ge(
|
|
position_ids_buf[:, None],
|
|
arange,
|
|
out=workspace.input_mask[:b, 0, :max_seq_len],
|
|
)
|
|
input_mask = input_mask.unsqueeze(1)
|
|
|
|
with torch.inference_mode(), attn_backend(self.backend):
|
|
return gctx.forward(
|
|
self.model,
|
|
key=graph_key,
|
|
input_ids=input_ids_buf,
|
|
input_mask=input_mask,
|
|
kv_cache=kv_cache,
|
|
position_ids=position_ids_buf.unsqueeze(1),
|
|
)
|
|
|
|
for i in range(5):
|
|
_decode_graph_step(prompt_length + i)
|
|
torch.cuda.synchronize()
|
|
|
|
t0 = time.perf_counter()
|
|
for i in range(gen_length * num_trials):
|
|
_decode_graph_step(prompt_length + 5 + i)
|
|
torch.cuda.synchronize()
|
|
elapsed = time.perf_counter() - t0
|
|
tokens = batch_size * gen_length * num_trials
|
|
tps = tokens / elapsed
|
|
return BenchmarkResult(
|
|
name="decode",
|
|
batch_size=batch_size,
|
|
seq_len=gen_length,
|
|
tokens_per_second=tps,
|
|
latency_ms=elapsed / (gen_length * num_trials) * 1000,
|
|
metadata={
|
|
"benchmark_type": "decode",
|
|
"num_trials": num_trials,
|
|
"prompt_length": prompt_length,
|
|
"cuda_graph": True,
|
|
},
|
|
)
|
|
|
|
def _run_plain_decode_benchmark(
|
|
self,
|
|
batch_size: int,
|
|
prompt_length: int,
|
|
gen_length: int,
|
|
num_trials: int,
|
|
) -> BenchmarkResult:
|
|
import time
|
|
|
|
max_seq_len = prompt_length + 5 + gen_length * num_trials
|
|
pool = self._make_pool(batch_size, max_seq_len)
|
|
workspace = self._make_workspace(pool, self.config)
|
|
task_cache = self._make_task_cache(pool)
|
|
task_ids = self._run_prefill(
|
|
pool, task_cache, batch_size, prompt_length, workspace
|
|
)
|
|
|
|
for i in range(5):
|
|
self._run_decode_step(
|
|
pool, task_cache, task_ids, prompt_length + i, workspace
|
|
)
|
|
torch.cuda.synchronize()
|
|
|
|
t0 = time.perf_counter()
|
|
for i in range(gen_length * num_trials):
|
|
self._run_decode_step(
|
|
pool, task_cache, task_ids, prompt_length + 5 + i, workspace
|
|
)
|
|
torch.cuda.synchronize()
|
|
elapsed = time.perf_counter() - t0
|
|
tokens = batch_size * gen_length * num_trials
|
|
tps = tokens / elapsed
|
|
return BenchmarkResult(
|
|
name="decode",
|
|
batch_size=batch_size,
|
|
seq_len=gen_length,
|
|
tokens_per_second=tps,
|
|
latency_ms=elapsed / (gen_length * num_trials) * 1000,
|
|
metadata={
|
|
"benchmark_type": "decode",
|
|
"num_trials": num_trials,
|
|
"prompt_length": prompt_length,
|
|
},
|
|
)
|
|
|
|
|
|
def print_benchmark_result(result: BenchmarkResult) -> None:
|
|
print("-" * 80)
|
|
print(f"{result.name.upper()} — Batch={result.batch_size}, SeqLen={result.seq_len}")
|
|
print(f" Throughput : {result.tokens_per_second:.1f} tokens/s")
|
|
print(f" Latency : {result.latency_ms:.2f} ms/step")
|
|
for k, v in result.metadata.items():
|
|
if k != "benchmark_type":
|
|
print(f" {k.replace('_', ' ').title()}: {v}")
|
|
print("-" * 80)
|
|
|
|
|
|
@click.command(name="benchmark", help="Benchmark model throughput and latency.")
|
|
@click.option("--device", default="cuda", help="Device.")
|
|
@click.option(
|
|
"--dtype", type=click.Choice(_DTYPES), default="bfloat16", help="Data type."
|
|
)
|
|
@click.option(
|
|
"--cache", type=click.Choice(_CACHES), default="contiguous", help="KV cache type."
|
|
)
|
|
@click.option(
|
|
"--backend",
|
|
type=click.Choice(_BACKENDS),
|
|
default="cuda",
|
|
help="Attention backend.",
|
|
)
|
|
@click.option(
|
|
"--compare",
|
|
is_flag=True,
|
|
help="Run both backends and print side-by-side speed comparison.",
|
|
)
|
|
@click.option("--batch_size", type=int, default=4, help="Batch size.")
|
|
@click.option("--prompt_length", type=int, default=512, help="Prompt length.")
|
|
@click.option("--gen_length", type=int, default=128, help="Generation length.")
|
|
@click.option("--num_trials", type=int, default=5, help="Number of trials.")
|
|
@click.option("--prefill_only", is_flag=True, help="Prefill benchmark only.")
|
|
@click.option("--decode_only", is_flag=True, help="Decode benchmark only.")
|
|
@click.option(
|
|
"--cuda-graph",
|
|
is_flag=True,
|
|
help="Enable CUDA graph capture for decode (cuda backend only).",
|
|
)
|
|
@click.option(
|
|
"--ckpt",
|
|
required=False,
|
|
default=None,
|
|
type=click.Path(exists=True, file_okay=False, dir_okay=True, path_type=Path),
|
|
help="Checkpoint directory. If omitted, a randomly-initialized model is "
|
|
"built from --config or the default 1B GQA preset.",
|
|
)
|
|
@click.option(
|
|
"--config",
|
|
"config_path",
|
|
required=False,
|
|
default=None,
|
|
type=click.Path(exists=True, file_okay=True, dir_okay=False, path_type=Path),
|
|
help="Optional model config JSON (used when --ckpt is omitted to define the "
|
|
"architecture). Defaults to the 1B GQA preset.",
|
|
)
|
|
def benchmark_command(
|
|
device: str,
|
|
dtype: str,
|
|
cache: str,
|
|
backend: str,
|
|
compare: bool,
|
|
batch_size: int,
|
|
prompt_length: int,
|
|
gen_length: int,
|
|
num_trials: int,
|
|
prefill_only: bool,
|
|
decode_only: bool,
|
|
cuda_graph: bool,
|
|
ckpt: Optional[str],
|
|
config_path: Optional[Path],
|
|
) -> None:
|
|
"""Benchmark model throughput and latency."""
|
|
dtype_map: dict[str, torch.dtype] = {
|
|
"bfloat16": torch.bfloat16,
|
|
"float16": torch.float16,
|
|
"float32": torch.float32,
|
|
}
|
|
|
|
if ckpt is not None:
|
|
click.echo(f"Loading model from {ckpt} ...")
|
|
config = ConfigFactory.load(
|
|
json.loads((Path(ckpt) / "config.json").read_text(encoding="utf-8-sig"))
|
|
)
|
|
model = AutoModel.from_pretrained(ckpt)
|
|
else:
|
|
raw = dict(_DEFAULT_CONFIG)
|
|
if config_path is not None:
|
|
raw.update(json.loads(config_path.read_text(encoding="utf-8-sig")))
|
|
config = ConfigFactory.load(raw)
|
|
model = AutoRegressiveLM(config)
|
|
click.echo(
|
|
f"Using randomly-initialized model "
|
|
f"({sum(p.numel() for p in model.parameters()) / 1e9:.2f}B params)"
|
|
)
|
|
|
|
model.to(device=device, dtype=dtype_map[dtype])
|
|
model.eval()
|
|
|
|
backends = _BACKENDS if compare else [backend]
|
|
|
|
for name in backends:
|
|
bench = GenerationBenchmark(
|
|
model=model,
|
|
config=config,
|
|
device=device,
|
|
dtype=dtype_map[dtype],
|
|
cache_type=cache,
|
|
backend=name,
|
|
cuda_graph=cuda_graph,
|
|
)
|
|
|
|
click.secho(
|
|
f"Benchmark: device={device} dtype={dtype} backend={name}", bold=True
|
|
)
|
|
|
|
if not decode_only:
|
|
result = bench.run_prefill_benchmark(
|
|
batch_size=batch_size,
|
|
prompt_length=prompt_length,
|
|
num_trials=num_trials,
|
|
)
|
|
print_benchmark_result(result)
|
|
|
|
if not prefill_only:
|
|
result = bench.run_decoding_benchmark(
|
|
batch_size=batch_size,
|
|
prompt_length=prompt_length,
|
|
gen_length=gen_length,
|
|
num_trials=num_trials,
|
|
)
|
|
print_benchmark_result(result)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
benchmark_command()
|