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FastDeploy/tests/spec_decode/test_ngram_gpu_kernel.py
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#!/usr/bin/env python3
# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""
Correctness + latency test for GPU ngram_match & hybrid_mtp_ngram kernels.
Run on AI Studio V100:
cd FastDeploy && pip install -e . && python tests/spec_decode/test_ngram_gpu_kernel.py
Or standalone (compile custom ops first):
bash build.sh 0 && python tests/spec_decode/test_ngram_gpu_kernel.py
"""
import os
import sys
import time
import unittest
import numpy as np
import paddle
# Ensure FastDeploy ops are importable
sys.path.insert(0, os.path.join(os.path.dirname(__file__), "../.."))
def _cpu_ngram_match(
input_ids,
input_ids_len,
token_ids_all,
prompt_lens,
step_idx,
draft_token_num,
draft_tokens,
seq_lens_this_time,
seq_lens_encoder,
seq_lens_decoder,
max_dec_len,
max_ngram_size,
max_draft_tokens_param,
threshold=128,
):
"""Pure NumPy reference matching the original ngram_match.cc logic."""
# Flatten (N,1) shaped arrays to 1D for scalar indexing
max_dec_len = max_dec_len.ravel()
step_idx = step_idx.ravel()
draft_token_num = draft_token_num.ravel()
prompt_lens = prompt_lens.ravel()
input_ids_len = input_ids_len.ravel()
max_batch_size = seq_lens_this_time.shape[0]
unprocessed = sum(1 for b in range(max_batch_size) if seq_lens_encoder[b] > 0 or seq_lens_decoder[b] > 0)
for batch_idx in range(max_batch_size):
remaining = int(max_dec_len[batch_idx] - step_idx[batch_idx] - 1)
mdt = min(int(draft_token_num[batch_idx]), remaining)
if seq_lens_encoder[batch_idx] > 0:
continue
elif seq_lens_decoder[batch_idx] == 0:
seq_lens_this_time[batch_idx] = 0
continue
cur_input_ids = input_ids[batch_idx]
cur_draft = draft_tokens[batch_idx]
prompt_len = int(prompt_lens[batch_idx])
cur_pre_ids = token_ids_all[batch_idx, prompt_len:]
cur_step = int(step_idx[batch_idx])
cur_ids_len = int(input_ids_len[batch_idx])
seq_lens_this_time[batch_idx] = 1
unprocessed -= 1
sum_tok = sum(int(seq_lens_this_time[i]) for i in range(batch_idx + 1))
left_min = unprocessed
if sum_tok + mdt + left_min > threshold:
mdt = min(mdt, threshold - sum_tok - left_min)
if sum_tok + left_min >= threshold - 1:
continue
for ngram_size in range(max_ngram_size, 0, -1):
if cur_step < ngram_size:
continue
ngram = cur_pre_ids[cur_step + 1 - ngram_size : cur_step + 1]
# Search in input_ids
match_input = False
for i in range(cur_ids_len - ngram_size + 1):
if np.array_equal(cur_input_ids[i : i + ngram_size], ngram):
start = i + ngram_size
end = min(start + mdt, cur_ids_len)
if start >= end:
continue
n = end - start
seq_lens_this_time[batch_idx] = n + 1
cur_draft[1 : 1 + n] = cur_input_ids[start : start + n]
match_input = True
break
if match_input:
break
# Search in pre_ids
found = False
for i in range(cur_step - ngram_size + 1):
if np.array_equal(cur_pre_ids[i : i + ngram_size], ngram):
start = i + ngram_size
end = min(start + mdt, cur_step)
if start >= end:
continue
n = end - start
seq_lens_this_time[batch_idx] = n + 1
cur_draft[1 : 1 + n] = cur_pre_ids[start : start + n]
found = True
break
if found:
break
def _cpu_hybrid_mtp_ngram(
input_ids,
input_ids_len,
pre_ids,
step_idx,
draft_token_num,
draft_tokens,
seq_lens_this_time,
seq_lens_decoder,
max_dec_len,
max_ngram_size,
min_ngram_size,
max_draft_tokens_param,
threshold=1024,
):
"""Pure NumPy reference matching the original ngram_match_mixed.cu CPU logic."""
# Flatten (N,1) shaped arrays to 1D for scalar indexing
max_dec_len = max_dec_len.ravel()
step_idx = step_idx.ravel()
draft_token_num = draft_token_num.ravel()
input_ids_len = input_ids_len.ravel()
max_batch_size = seq_lens_this_time.shape[0]
unprocessed = sum(1 for b in range(max_batch_size) if seq_lens_decoder[b] > 0)
for batch_idx in range(max_batch_size):
ori_slt = int(seq_lens_this_time[batch_idx])
remaining = int(max_dec_len[batch_idx] - step_idx[batch_idx] - 1)
max_q = min(max_draft_tokens_param - ori_slt + 1, remaining)
if ori_slt == 0 or max_q <= 0:
continue
cur_input_ids = input_ids[batch_idx]
cur_draft = draft_tokens[batch_idx]
cur_pre = pre_ids[batch_idx]
cur_step = int(step_idx[batch_idx])
cur_ids_len = int(input_ids_len[batch_idx])
unprocessed -= 1
sum_tok = sum(int(seq_lens_this_time[i]) for i in range(batch_idx + 1))
left_min = unprocessed
if sum_tok + max_q + left_min > threshold:
max_q = min(max_q, threshold - sum_tok - left_min)
if sum_tok + left_min >= threshold - 1:
continue
match_global = False
for ngram_size in range(max_ngram_size, min_ngram_size - 1, -1):
if match_global:
break
if cur_step < ngram_size:
continue
ngram = cur_pre[cur_step + 1 - ngram_size : cur_step + 1]
# Search in input_ids
for i in range(cur_ids_len - ngram_size + 1):
if match_global:
break
if np.array_equal(cur_input_ids[i : i + ngram_size], ngram):
start = i + ngram_size
end = min(start + max_q, cur_ids_len)
if start >= end:
continue
n = end - start
seq_lens_this_time[batch_idx] = ori_slt + n
cur_draft[ori_slt : ori_slt + n] = cur_input_ids[start : start + n]
match_global = True
# Search in pre_ids
if not match_global:
for i in range(cur_step - ngram_size + 1):
if match_global:
break
if np.array_equal(cur_pre[i : i + ngram_size], ngram):
start = i + ngram_size
end = min(start + max_q, cur_step)
if start >= end:
continue
n = end - start
seq_lens_this_time[batch_idx] = ori_slt + n
cur_draft[ori_slt : ori_slt + n] = cur_pre[start : start + n]
match_global = True
def _make_ngram_test_data(batch_size=4, input_len=64, max_model_len=256, max_draft=10, seed=42):
"""Create realistic test tensors for ngram_match op."""
rng = np.random.RandomState(seed)
vocab_size = 1000
# Ensure max_model_len can hold prompt + generated tokens
max_model_len = max(max_model_len, input_len + 64)
# Create prompt tokens with repeating patterns to ensure ngram matches
input_ids = rng.randint(0, vocab_size, (batch_size, input_len)).astype(np.int64)
input_ids_len = np.full((batch_size, 1), input_len, dtype=np.int64)
# token_ids_all: [batch, max_model_len] — prompt + generated
token_ids_all = np.zeros((batch_size, max_model_len), dtype=np.int64)
prompt_lens = np.full((batch_size, 1), input_len, dtype=np.int64)
step_idx = np.zeros((batch_size, 1), dtype=np.int64)
draft_token_num = np.full((batch_size, 1), max_draft, dtype=np.int32)
draft_tokens = np.zeros((batch_size, max_draft + 1), dtype=np.int64)
seq_lens_this_time = np.ones(batch_size, dtype=np.int32)
seq_lens_encoder = np.zeros(batch_size, dtype=np.int32)
seq_lens_decoder = np.ones(batch_size, dtype=np.int32)
max_dec_len = np.full((batch_size, 1), 200, dtype=np.int64)
for b in range(batch_size):
# Copy prompt into token_ids_all
token_ids_all[b, :input_len] = input_ids[b]
# Simulate generated tokens: copy contiguous blocks from prompt
# to guarantee ngram matches exist
gen_len = 20
src = rng.randint(0, max(1, input_len - gen_len))
token_ids_all[b, input_len : input_len + gen_len] = input_ids[b, src : src + gen_len]
# step_idx = last valid position (0-based index)
step_idx[b] = gen_len - 1
return {
"input_ids": input_ids,
"input_ids_len": input_ids_len,
"token_ids_all": token_ids_all,
"prompt_lens": prompt_lens,
"step_idx": step_idx,
"draft_token_num": draft_token_num,
"draft_tokens": draft_tokens,
"seq_lens_this_time": seq_lens_this_time,
"seq_lens_encoder": seq_lens_encoder,
"seq_lens_decoder": seq_lens_decoder,
"max_dec_len": max_dec_len,
}
def _make_mixed_test_data(batch_size=4, input_len=64, pre_ids_len=256, max_draft=10, seed=42):
"""Create realistic test tensors for hybrid_mtp_ngram op."""
rng = np.random.RandomState(seed)
vocab_size = 1000
input_ids = rng.randint(0, vocab_size, (batch_size, input_len)).astype(np.int64)
input_ids_len = np.full((batch_size, 1), input_len, dtype=np.int64)
pre_ids = np.zeros((batch_size, pre_ids_len), dtype=np.int64)
step_idx = np.zeros((batch_size, 1), dtype=np.int64)
draft_token_num = np.full((batch_size, 1), max_draft, dtype=np.int32)
draft_tokens = np.zeros((batch_size, max_draft + 1), dtype=np.int64)
# For mixed: seq_lens_this_time starts at 1 (already has 1 draft token)
seq_lens_this_time = np.ones(batch_size, dtype=np.int32)
seq_lens_decoder = np.ones(batch_size, dtype=np.int32)
max_dec_len = np.full((batch_size, 1), 200, dtype=np.int64)
for b in range(batch_size):
# Copy contiguous blocks from prompt to guarantee ngram matches
gen_len = 20
src = rng.randint(0, max(1, input_len - gen_len))
pre_ids[b, :gen_len] = input_ids[b, src : src + gen_len]
# step_idx = last valid position (0-based index)
step_idx[b] = gen_len - 1
return {
"input_ids": input_ids,
"input_ids_len": input_ids_len,
"pre_ids": pre_ids,
"step_idx": step_idx,
"draft_token_num": draft_token_num,
"draft_tokens": draft_tokens,
"seq_lens_this_time": seq_lens_this_time,
"seq_lens_decoder": seq_lens_decoder,
"max_dec_len": max_dec_len,
}
def _to_gpu(np_dict):
"""Convert numpy dict to GPU paddle tensors."""
out = {}
for k, v in np_dict.items():
out[k] = paddle.to_tensor(v, place=paddle.CUDAPlace(0))
return out
class TestNgramMatchKernel(unittest.TestCase):
"""Test ngram_match GPU kernel correctness against CPU reference."""
@classmethod
def setUpClass(cls):
if not paddle.is_compiled_with_cuda():
raise unittest.SkipTest("CUDA not available")
paddle.set_device("gpu")
# Import GPU ops (requires FastDeploy build)
try:
from fastdeploy.model_executor.ops.gpu import ngram_match
cls.ngram_match = staticmethod(ngram_match)
except Exception as e:
raise unittest.SkipTest(f"Cannot import ngram_match op: {e}")
def test_correctness_basic(self):
"""Basic correctness: GPU output matches CPU reference."""
data = _make_ngram_test_data(batch_size=4, seed=42)
max_ngram_size = 3
max_draft_tokens = 10
# CPU reference
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_ngram_match(
data["input_ids"],
data["input_ids_len"],
data["token_ids_all"],
data["prompt_lens"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_encoder"],
data["seq_lens_decoder"],
data["max_dec_len"],
max_ngram_size,
max_draft_tokens,
)
# GPU kernel
gpu_data = _to_gpu(data)
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
max_ngram_size,
max_draft_tokens,
)
paddle.device.synchronize()
gpu_draft = gpu_data["draft_tokens"].numpy()
gpu_slt = gpu_data["seq_lens_this_time"].numpy()
np.testing.assert_array_equal(gpu_slt, cpu_slt, err_msg="seq_lens_this_time mismatch")
np.testing.assert_array_equal(gpu_draft, cpu_draft, err_msg="draft_tokens mismatch")
def test_correctness_varied_seeds(self):
"""Test across multiple random seeds."""
for seed in [42]:
with self.subTest(seed=seed):
data = _make_ngram_test_data(batch_size=8, seed=seed)
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_ngram_match(
data["input_ids"],
data["input_ids_len"],
data["token_ids_all"],
data["prompt_lens"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_encoder"],
data["seq_lens_decoder"],
data["max_dec_len"],
3,
10,
)
gpu_data = _to_gpu(data)
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
np.testing.assert_array_equal(gpu_data["seq_lens_this_time"].numpy(), cpu_slt)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft)
def test_large_batch_long_seq(self):
"""bsz=256, seq_len=16k — scale test.
Uses high threshold to ensure all batches exercise the parallel search
path (default threshold=128 would skip all batches at bsz=256).
"""
high_threshold = 100000
data = _make_ngram_test_data(batch_size=256, input_len=16384, max_model_len=16384 + 64, seed=77)
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_ngram_match(
data["input_ids"],
data["input_ids_len"],
data["token_ids_all"],
data["prompt_lens"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_encoder"],
data["seq_lens_decoder"],
data["max_dec_len"],
3,
10,
threshold=high_threshold,
)
gpu_data = _to_gpu(data)
old_env = os.environ.get("INFER_WITH_REFERENCE_TOKENUM_THRESHOLD")
os.environ["INFER_WITH_REFERENCE_TOKENUM_THRESHOLD"] = str(high_threshold)
try:
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
finally:
if old_env is None:
os.environ.pop("INFER_WITH_REFERENCE_TOKENUM_THRESHOLD", None)
else:
os.environ["INFER_WITH_REFERENCE_TOKENUM_THRESHOLD"] = old_env
np.testing.assert_array_equal(gpu_data["seq_lens_this_time"].numpy(), cpu_slt)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft)
def test_single_batch_long_seq(self):
"""bsz=1, seq_len=16k — single long sequence."""
data = _make_ngram_test_data(batch_size=1, input_len=16384, max_model_len=16384 + 64, seed=88)
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_ngram_match(
data["input_ids"],
data["input_ids_len"],
data["token_ids_all"],
data["prompt_lens"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_encoder"],
data["seq_lens_decoder"],
data["max_dec_len"],
3,
10,
)
gpu_data = _to_gpu(data)
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
np.testing.assert_array_equal(gpu_data["seq_lens_this_time"].numpy(), cpu_slt)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft)
def test_many_short_seqs(self):
"""bsz=256, seq_len=1k — many short sequences."""
high_threshold = 100000
data = _make_ngram_test_data(batch_size=256, input_len=1024, seed=55)
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_ngram_match(
data["input_ids"],
data["input_ids_len"],
data["token_ids_all"],
data["prompt_lens"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_encoder"],
data["seq_lens_decoder"],
data["max_dec_len"],
3,
10,
threshold=high_threshold,
)
gpu_data = _to_gpu(data)
old_env = os.environ.get("INFER_WITH_REFERENCE_TOKENUM_THRESHOLD")
os.environ["INFER_WITH_REFERENCE_TOKENUM_THRESHOLD"] = str(high_threshold)
try:
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
finally:
if old_env is None:
os.environ.pop("INFER_WITH_REFERENCE_TOKENUM_THRESHOLD", None)
else:
os.environ["INFER_WITH_REFERENCE_TOKENUM_THRESHOLD"] = old_env
np.testing.assert_array_equal(gpu_data["seq_lens_this_time"].numpy(), cpu_slt)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft)
def test_latency(self):
"""Benchmark: GPU kernel latency vs CPU transfer overhead."""
# Warmup
for _ in range(1):
d = _to_gpu(_make_ngram_test_data(batch_size=32, input_len=512, seed=42))
self.ngram_match(
d["input_ids"],
d["input_ids_len"],
d["token_ids_all"],
d["prompt_lens"],
d["step_idx"],
d["draft_token_num"],
d["draft_tokens"],
d["seq_lens_this_time"],
d["seq_lens_encoder"],
d["seq_lens_decoder"],
d["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
# GPU path: kernel execution only (pre-created tensors, no data transfer)
gpu_data = _to_gpu(_make_ngram_test_data(batch_size=32, input_len=512, seed=42))
cpu_data = _make_ngram_test_data(batch_size=32, input_len=512, seed=42)
n_runs = 1
paddle.device.synchronize()
t0 = time.perf_counter()
for _ in range(n_runs):
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
t1 = time.perf_counter()
gpu_time_ms = (t1 - t0) / n_runs * 1000
# CPU path: simulate the old copy-to-CPU-and-back pattern
paddle.device.synchronize()
t0 = time.perf_counter()
for _ in range(n_runs):
# Simulate old path: copy all tensors to CPU then back
cpu_tensors = {k: paddle.to_tensor(v, place=paddle.CPUPlace()) for k, v in cpu_data.items()}
_ = cpu_tensors["draft_tokens"].cuda()
_ = cpu_tensors["seq_lens_this_time"].cuda()
paddle.device.synchronize()
t1 = time.perf_counter()
cpu_copy_time_ms = (t1 - t0) / n_runs * 1000
print(f"\n{'='*60}")
print(f"LATENCY BENCHMARK (batch=32, input_len=512, {n_runs} runs)")
print(f" GPU kernel (zero-copy): {gpu_time_ms:.3f} ms/call")
print(f" CPU path (copy overhead): {cpu_copy_time_ms:.3f} ms/call")
print(f" Speedup: {cpu_copy_time_ms / gpu_time_ms:.2f}x")
print(f"{'='*60}")
def test_latency_scaling(self):
"""Benchmark GPU kernel across batch sizes to show Phase 2 scales."""
batch_sizes = [32, 128, 256, 512, 1024]
input_len = 512
n_runs = 1
results = []
for bsz in batch_sizes:
# Pre-create tensors once per batch size (excluded from timing)
gpu_data = _to_gpu(_make_ngram_test_data(batch_size=bsz, input_len=input_len, seed=42))
cpu_data = _make_ngram_test_data(batch_size=bsz, input_len=input_len, seed=42)
# Warmup
for _ in range(1):
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
# GPU kernel (pure kernel time — no data creation/transfer)
paddle.device.synchronize()
t0 = time.perf_counter()
for _ in range(n_runs):
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
gpu_ms = (time.perf_counter() - t0) / n_runs * 1000
# CPU path: simulate the old copy-to-CPU-and-back pattern
paddle.device.synchronize()
t0 = time.perf_counter()
for _ in range(n_runs):
cpu_tensors = {k: paddle.to_tensor(v, place=paddle.CPUPlace()) for k, v in cpu_data.items()}
_ = cpu_tensors["draft_tokens"].cuda()
_ = cpu_tensors["seq_lens_this_time"].cuda()
paddle.device.synchronize()
cpu_ms = (time.perf_counter() - t0) / n_runs * 1000
results.append((bsz, gpu_ms, cpu_ms))
print(f"\n{'='*72}")
print(f"SCALING BENCHMARK (input_len={input_len}, {n_runs} runs per config)")
print(f"{'─'*72}")
print(f"{'batch':>6} {'GPU (ms)':>10} {'CPU (ms)':>10} {'Speedup':>8} {'GPU/batch(µs)':>14}")
print(f"{'─'*72}")
for bsz, gpu_ms, cpu_ms in results:
speedup = cpu_ms / gpu_ms
per_batch_us = gpu_ms / bsz * 1000
print(f"{bsz:>6} {gpu_ms:>10.3f} {cpu_ms:>10.3f} {speedup:>7.2f}x {per_batch_us:>14.3f}")
print(f"{'='*72}")
def test_latency_extreme(self):
"""Benchmark: GPU kernel at extreme scale (bsz=256, seq_len=16k).
Addresses the NCU profiler worst-case scenario (bsz=256 + 16k)
raised in review. Tests with production-realistic thresholds
(8192, 16384) rather than the unlimited threshold used in
correctness tests.
"""
configs = [
{"threshold": 8192, "label": "threshold=8192"},
{"threshold": 16384, "label": "threshold=16384"},
]
batch_size = 256
input_len = 16384
n_runs = 1
# Pre-create tensors once (excluded from timing)
gpu_data = _to_gpu(
_make_ngram_test_data(
batch_size=batch_size,
input_len=input_len,
max_model_len=input_len + 64,
seed=77,
)
)
cpu_data = _make_ngram_test_data(
batch_size=batch_size,
input_len=input_len,
max_model_len=input_len + 64,
seed=77,
)
print(f"\n{'='*72}")
print(f"EXTREME BENCHMARK (batch={batch_size}, seq_len={input_len}, {n_runs} runs)")
print(f"{'─'*72}")
for cfg in configs:
threshold = cfg["threshold"]
old_env = os.environ.get("INFER_WITH_REFERENCE_TOKENUM_THRESHOLD")
os.environ["INFER_WITH_REFERENCE_TOKENUM_THRESHOLD"] = str(threshold)
try:
# Warmup
for _ in range(1):
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
# GPU kernel timing
paddle.device.synchronize()
t0 = time.perf_counter()
for _ in range(n_runs):
self.ngram_match(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["token_ids_all"],
gpu_data["prompt_lens"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_encoder"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
10,
)
paddle.device.synchronize()
t1 = time.perf_counter()
gpu_ms = (t1 - t0) / n_runs * 1000
finally:
if old_env is None:
os.environ.pop("INFER_WITH_REFERENCE_TOKENUM_THRESHOLD", None)
else:
os.environ["INFER_WITH_REFERENCE_TOKENUM_THRESHOLD"] = old_env
# CPU path: simulate copy-to-CPU-and-back overhead at extreme scale
cpu_runs = 1
paddle.device.synchronize()
t0 = time.perf_counter()
for _ in range(cpu_runs):
cpu_tensors = {k: paddle.to_tensor(v, place=paddle.CPUPlace()) for k, v in cpu_data.items()}
_ = cpu_tensors["draft_tokens"].cuda()
_ = cpu_tensors["seq_lens_this_time"].cuda()
paddle.device.synchronize()
t1 = time.perf_counter()
cpu_ms = (t1 - t0) / cpu_runs * 1000
speedup = cpu_ms / gpu_ms if gpu_ms > 0 else float("inf")
print(f" [{cfg['label']}]")
print(f" GPU kernel: {gpu_ms:.3f} ms/call ({gpu_ms * 1000:.1f} us)")
print(f" CPU path: {cpu_ms:.3f} ms/call")
print(f" Speedup: {speedup:.1f}x")
print()
print(f"{'='*72}")
class TestHybridMtpNgramKernel(unittest.TestCase):
"""Test hybrid_mtp_ngram GPU kernel correctness against CPU reference."""
@classmethod
def setUpClass(cls):
if not paddle.is_compiled_with_cuda():
raise unittest.SkipTest("CUDA not available")
paddle.set_device("gpu")
try:
from fastdeploy.model_executor.ops.gpu import hybrid_mtp_ngram
cls.hybrid_mtp_ngram = staticmethod(hybrid_mtp_ngram)
except Exception as e:
raise unittest.SkipTest(f"Cannot import hybrid_mtp_ngram op: {e}")
def test_correctness_basic(self):
"""Basic correctness: GPU output matches CPU reference."""
data = _make_mixed_test_data(batch_size=4, seed=42)
max_ngram_size = 3
min_ngram_size = 1
max_draft_tokens = 10
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_hybrid_mtp_ngram(
data["input_ids"],
data["input_ids_len"],
data["pre_ids"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_decoder"],
data["max_dec_len"],
max_ngram_size,
min_ngram_size,
max_draft_tokens,
)
gpu_data = _to_gpu(data)
self.hybrid_mtp_ngram(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["pre_ids"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
max_ngram_size,
min_ngram_size,
max_draft_tokens,
)
paddle.device.synchronize()
np.testing.assert_array_equal(
gpu_data["seq_lens_this_time"].numpy(), cpu_slt, err_msg="seq_lens_this_time mismatch"
)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft, err_msg="draft_tokens mismatch")
def test_correctness_varied_seeds(self):
"""Test across multiple random seeds."""
for seed in [42]:
with self.subTest(seed=seed):
data = _make_mixed_test_data(batch_size=8, seed=seed)
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_hybrid_mtp_ngram(
data["input_ids"],
data["input_ids_len"],
data["pre_ids"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_decoder"],
data["max_dec_len"],
3,
1,
10,
)
gpu_data = _to_gpu(data)
self.hybrid_mtp_ngram(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["pre_ids"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
1,
10,
)
paddle.device.synchronize()
np.testing.assert_array_equal(gpu_data["seq_lens_this_time"].numpy(), cpu_slt)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft)
def test_large_batch_long_seq(self):
"""bsz=256, seq_len=16k — scale test.
Uses high threshold to ensure all batches exercise the parallel search
path (default threshold=1024 would skip many batches at bsz=256).
"""
high_threshold = 100000
data = _make_mixed_test_data(batch_size=256, input_len=16384, pre_ids_len=16384 + 64, seed=77)
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_hybrid_mtp_ngram(
data["input_ids"],
data["input_ids_len"],
data["pre_ids"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_decoder"],
data["max_dec_len"],
3,
1,
10,
threshold=high_threshold,
)
gpu_data = _to_gpu(data)
old_env = os.environ.get("SPEC_TOKENUM_THRESHOLD")
os.environ["SPEC_TOKENUM_THRESHOLD"] = str(high_threshold)
try:
self.hybrid_mtp_ngram(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["pre_ids"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
1,
10,
)
paddle.device.synchronize()
finally:
if old_env is None:
os.environ.pop("SPEC_TOKENUM_THRESHOLD", None)
else:
os.environ["SPEC_TOKENUM_THRESHOLD"] = old_env
np.testing.assert_array_equal(gpu_data["seq_lens_this_time"].numpy(), cpu_slt)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft)
def test_single_batch_long_seq(self):
"""bsz=1, seq_len=16k — single long sequence."""
data = _make_mixed_test_data(batch_size=1, input_len=16384, pre_ids_len=16384 + 64, seed=88)
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_hybrid_mtp_ngram(
data["input_ids"],
data["input_ids_len"],
data["pre_ids"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_decoder"],
data["max_dec_len"],
3,
1,
10,
)
gpu_data = _to_gpu(data)
self.hybrid_mtp_ngram(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["pre_ids"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
1,
10,
)
paddle.device.synchronize()
np.testing.assert_array_equal(gpu_data["seq_lens_this_time"].numpy(), cpu_slt)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft)
def test_many_short_seqs(self):
"""bsz=256, seq_len=1k — many short sequences."""
high_threshold = 100000
data = _make_mixed_test_data(batch_size=256, input_len=1024, seed=55)
cpu_draft = data["draft_tokens"].copy()
cpu_slt = data["seq_lens_this_time"].copy()
_cpu_hybrid_mtp_ngram(
data["input_ids"],
data["input_ids_len"],
data["pre_ids"],
data["step_idx"],
data["draft_token_num"],
cpu_draft,
cpu_slt,
data["seq_lens_decoder"],
data["max_dec_len"],
3,
1,
10,
threshold=high_threshold,
)
gpu_data = _to_gpu(data)
old_env = os.environ.get("SPEC_TOKENUM_THRESHOLD")
os.environ["SPEC_TOKENUM_THRESHOLD"] = str(high_threshold)
try:
self.hybrid_mtp_ngram(
gpu_data["input_ids"],
gpu_data["input_ids_len"],
gpu_data["pre_ids"],
gpu_data["step_idx"],
gpu_data["draft_token_num"],
gpu_data["draft_tokens"],
gpu_data["seq_lens_this_time"],
gpu_data["seq_lens_decoder"],
gpu_data["max_dec_len"],
3,
1,
10,
)
paddle.device.synchronize()
finally:
if old_env is None:
os.environ.pop("SPEC_TOKENUM_THRESHOLD", None)
else:
os.environ["SPEC_TOKENUM_THRESHOLD"] = old_env
np.testing.assert_array_equal(gpu_data["seq_lens_this_time"].numpy(), cpu_slt)
np.testing.assert_array_equal(gpu_data["draft_tokens"].numpy(), cpu_draft)
if __name__ == "__main__":
unittest.main(verbosity=2)
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