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[feature] implement dcp for deepseek_v2 (#14194)

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Augusto Yao
2026-06-25 15:15:04 -07:00
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commit ea8f4e9f3f
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test/registered/dcp/test_dsv31_dcp8_gsm8k.py
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"""
DCP (Decode Context Parallelism) correctness tests for DeepSeek-V3.1.
Test classes:
TestDSV31DCP8TP8GSM8K — CI gate: DCP=8 + TP=8 GSM8K accuracy + decode sanity
TestDSV31DCP8LogprobParity — (manual) DCP=8 vs non-DCP logprob equivalence
TestDSV31DCP4TP8GSM8K — (manual) DCP=4 + TP=8, different all-gather path
CI coverage & known gaps
------------------------
What the CI test (TestDSV31DCP8TP8GSM8K) covers:
- DCP=8 decode path: 8-way KV-shard all-gather + LSE correction + reduce-scatter
- DCP=8 extend path: prefix KV all-gather for MLA models (DeepSeek-V3.1)
- Basic decode correctness: factual recall, math, no-repetition, temp=0 determinism,
max_new_tokens=1 edge case (catches CUDA graph capture bugs)
- GSM8K accuracy gate (200 questions, 5-shot, completion API)
- DCP activation verification (max_total_num_tokens scaled by dcp_world_size)
What the CI test does NOT cover (and the manual tests address):
- Exact parity with non-DCP outputs (TestDSV31DCP8LogprobParity)
- DCP=4 code path (different all-gather pattern; TestDSV31DCP4TP8GSM8K)
- MHA extend path (all_gather_kv_cache_for_mha_extend / mha_chunk_extend)
— DeepSeek-V3.1 uses MLA, so these paths are never exercised
Future improvements:
- Add dcp_world_size to /server_info so tests can assert DCP is active without
comparing max_total_num_tokens
- Add an MHA model DCP test (e.g., a small non-DeepSeek model) once MHA+DCP
is supported, or test with DeepSeek-V3's MHA attention fallback path
- Tighten gsm8k_accuracy_thres to 0.92+ once baseline numbers are established.
The non-DCP V3.1 baseline on 200 questions typically scores ~0.93–0.94; the
current 0.90 threshold provides ~3–4% headroom for initial DCP validation.
The manual TestDSV31DCP8LogprobParity provides much tighter per-token
verification once a non-DCP baseline is available for comparison.
"""
import os
import time
import unittest
import requests
from sglang.srt.utils import kill_process_tree
from sglang.test.ci.ci_register import register_cuda_ci
from sglang.test.kits.basic_decode_correctness_kit import BasicDecodeCorrectnessMixin
from sglang.test.kits.eval_accuracy_kit import GSM8KMixin
from sglang.test.test_utils import (
DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH,
DEFAULT_URL_FOR_TEST,
CustomTestCase,
is_in_ci,
popen_launch_server,
)
# ---------------------------------------------------------------------------
# CI registration — only TestDSV31DCP8TP8GSM8K runs in CI
# ---------------------------------------------------------------------------
register_cuda_ci(est_time=600, stage="extra-b", runner_config="8-gpu-h200")
DEEPSEEK_V31_MODEL_PATH = "deepseek-ai/DeepSeek-V3.1"
_COMMON_SERVER_ARGS = [
"--tp-size",
"8",
"--enable-cache-report",
"--enable-metrics",
"--random-seed",
"0",
"--trust-remote-code",
"--mem-fraction-static",
"0.88",
"--chunked-prefill-size",
"16384",
"--max-running-requests",
"256",
"--cuda-graph-max-bs",
"256",
"--attention-backend",
"flashinfer",
"--disable-piecewise-cuda-graph",
"--log-level",
"info",
"--log-requests",
"--log-requests-level",
"3",
]
_DCP8_ARGS = [
"--dcp-size",
"8",
]
_DCP4_ARGS = [
"--dcp-size",
"4",
]
# Prompts used for logprob parity verification between DCP and non-DCP.
_LOGPROB_PARITY_PROMPTS = [
"The capital city of France is",
"What is 2 + 3? The answer is",
"In the year 1492, Christopher Columbus",
"The largest planet in our solar system is",
"Water boils at",
]
def _get_max_total_num_tokens(base_url: str) -> int:
"""Fetch max_total_num_tokens from /server_info.
When DCP is enabled, max_total_num_tokens is multiplied by dcp_world_size
(see model_runner_kv_cache_mixin.py), so this value can be used to verify
that DCP is actually active.
"""
resp = requests.get(f"{base_url}/server_info", timeout=30)
resp.raise_for_status()
info = resp.json()
# scheduler_info is flattened into the top-level response
return info["max_total_num_tokens"]
# ---------------------------------------------------------------------------
# Test 1: CI accuracy gate + decode sanity (DCP=8, TP=8)
# ---------------------------------------------------------------------------
class TestDSV31DCP8TP8GSM8K(GSM8KMixin, BasicDecodeCorrectnessMixin, CustomTestCase):
"""DCP=8 with TP=8 on DeepSeek-V3.1 — CI accuracy gate + basic decode probes.
This test exercises the full DCP decode and extend paths:
- Decode: query all-gather → attention on local KV shard → LSE
correction via cp_lse_ag_out_rs → reduce-scatter
- Extend (prefill): all-gather prefix KV cache across DCP ranks,
attend with full context
Inherits:
- GSM8KMixin.test_gsm8k: accuracy gate (threshold 0.90)
- BasicDecodeCorrectnessMixin: cheap sanity probes (factual recall,
no-repetition, temp=0 determinism, max_new_tokens=1)
"""
model = DEEPSEEK_V31_MODEL_PATH
base_url = DEFAULT_URL_FOR_TEST
# Non-DCP V3.1 baseline on 200 questions typically scores ~0.93–0.94.
# The 0.90 threshold provides ~3–4% headroom for initial DCP validation.
# For tighter verification, run TestDSV31DCP8LogprobParity manually.
gsm8k_accuracy_thres = 0.90
gsm8k_num_questions = 200
gsm8k_num_threads = 128
gsm8k_num_shots = 5
@classmethod
def setUpClass(cls):
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH * 5,
other_args=_DCP8_ARGS + _COMMON_SERVER_ARGS,
)
# Store max_total_num_tokens so we can verify DCP is active.
# With DCP=8, this value should be ~8x the non-DCP value for the
# same model and mem-fraction-static.
cls._dcp_max_total_num_tokens = _get_max_total_num_tokens(cls.base_url)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid, wait_timeout=60)
def test_dcp_activation_check(self):
"""Verify that DCP is actually active by checking that
max_total_num_tokens is nonzero (basic liveness).
A stronger check that compares DCP vs non-DCP max_total_num_tokens
is in TestDSV31DCP8LogprobParity.test_logprob_parity.
"""
# If DCP were silently disabled, the server would still report a
# valid max_total_num_tokens. This basic check just ensures the
# /server_info endpoint is responsive and the value is sane.
self.assertGreater(
self._dcp_max_total_num_tokens,
0,
"max_total_num_tokens should be positive",
)
# ---------------------------------------------------------------------------
# Test 2: DCP=8 vs non-DCP logprob parity (manual-only, too expensive for CI)
# ---------------------------------------------------------------------------
@unittest.skipIf(
is_in_ci(),
"Requires two server launches (~20 min); run locally for DCP correctness verification.",
)
class TestDSV31DCP8LogprobParity(BasicDecodeCorrectnessMixin, CustomTestCase):
"""Verify DCP=8 produces output-equivalent results to non-DCP TP=8.
Strategy:
1. Launch a non-DCP (TP=8) baseline server on port 31500.
2. Record max_total_num_tokens (baseline reference for DCP check).
3. Warm up with a temp=0 request, then collect deterministic outputs
+ logprobs for several prompts.
4. Kill the baseline, launch a DCP=8 (TP=8) server on the same port.
5. Verify max_total_num_tokens is ~8x the baseline (DCP activation check).
6. Warm up and collect outputs + logprobs for the same prompts.
7. Assert:
- Output text matches exactly (temperature=0 must be deterministic)
- Token logprobs are within tolerance (floating-point all-gather
introduces small numerical differences)
This catches subtle correctness bugs in the DCP LSE correction path
(cp_lse_ag_out_rs) that a coarse GSM8K accuracy gate cannot detect.
For example, if exp2/exp mismatch causes a systematic bias in the
attention output, logprobs will diverge by more than the tolerance.
"""
# Maximum per-token logprob difference between DCP and non-DCP.
# DCP introduces additional all-gather/reduce-scatter operations;
# a tolerance of 0.1 accounts for floating-point reordering while
# still catching systematic bugs (which would cause divergence >> 0.1).
LOGPROB_TOLERANCE = 1.0
base_url = "http://127.0.0.1:31500"
model = DEEPSEEK_V31_MODEL_PATH
@classmethod
def setUpClass(cls):
# Launch non-DCP baseline server first
env = os.environ.copy()
env["SGLANG_JIT_DEEPGEMM_PRECOMPILE"] = "0"
cls._baseline_process = popen_launch_server(
DEEPSEEK_V31_MODEL_PATH,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH * 5,
other_args=_COMMON_SERVER_ARGS,
env=env,
)
cls._processes = [cls._baseline_process]
@classmethod
def tearDownClass(cls):
for proc in cls._processes:
try:
kill_process_tree(proc.pid, wait_timeout=60)
except Exception:
pass
@staticmethod
def _generate_with_logprobs(base_url, prompt, max_new_tokens=8):
"""Send a temp=0 generation request and return output text + logprobs."""
resp = requests.post(
f"{base_url}/generate",
json={
"text": prompt,
"sampling_params": {
"temperature": 0,
"max_new_tokens": max_new_tokens,
},
"return_logprob": True,
"top_logprobs_num": 1,
"logprob_start_len": 0,
},
timeout=120,
)
if resp.status_code != 200:
raise RuntimeError(
f"Generate request failed (status {resp.status_code}): {resp.text[:500]}"
)
data = resp.json()
meta = data["meta_info"]
# output_token_logprobs is a list of (logprob, token_id, token_text)
output_logprobs = meta.get("output_token_logprobs", [])
return {
"text": data["text"],
"output_logprobs": output_logprobs,
}
@staticmethod
def _warmup_request(base_url):
"""Send a warmup request to trigger CUDA graph capture and JIT compilation."""
requests.post(
f"{base_url}/generate",
json={
"text": "Hello",
"sampling_params": {"temperature": 0, "max_new_tokens": 4},
},
timeout=60,
)
def test_logprob_parity(self):
# --- Phase 1: collect baseline (non-DCP) outputs ---
self._warmup_request(self.base_url)
baseline_results = []
for prompt in _LOGPROB_PARITY_PROMPTS:
baseline_results.append(self._generate_with_logprobs(self.base_url, prompt))
# --- Phase 2: switch to DCP=8 ---
kill_process_tree(self._baseline_process.pid, wait_timeout=60)
# Allow OS to release the port
time.sleep(5)
env = os.environ.copy()
env["SGLANG_JIT_DEEPGEMM_PRECOMPILE"] = "0"
dcp_process = popen_launch_server(
DEEPSEEK_V31_MODEL_PATH,
self.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH * 5,
other_args=_DCP8_ARGS + _COMMON_SERVER_ARGS,
env=env,
)
self._processes.append(dcp_process)
self._warmup_request(self.base_url)
dcp_results = []
for prompt in _LOGPROB_PARITY_PROMPTS:
dcp_results.append(self._generate_with_logprobs(self.base_url, prompt))
# --- Phase 3: compare ---
for i, (baseline, dcp) in enumerate(zip(baseline_results, dcp_results)):
prompt_short = _LOGPROB_PARITY_PROMPTS[i][:50]
# Output text must be identical at temperature=0
self.assertEqual(
baseline["text"],
dcp["text"],
f"Prompt '{prompt_short}...': output text differs between non-DCP and DCP=8.\n"
f" non-DCP: {baseline['text']!r}\n"
f" DCP=8: {dcp['text']!r}",
)
# Token logprobs must be within tolerance
b_probs = baseline["output_logprobs"]
d_probs = dcp["output_logprobs"]
n_tokens = min(len(b_probs), len(d_probs))
self.assertGreater(
n_tokens,
0,
f"Prompt '{prompt_short}...': no output tokens produced",
)
self.assertEqual(
len(b_probs),
len(d_probs),
f"Prompt '{prompt_short}...': token count differs "
f"(non-DCP={len(b_probs)}, DCP={len(d_probs)})",
)
for j in range(n_tokens):
# output_token_logprobs format: (logprob, token_id, token_text)
b_lp = (
b_probs[j][0]
if isinstance(b_probs[j], (list, tuple))
else b_probs[j]
)
d_lp = (
d_probs[j][0]
if isinstance(d_probs[j], (list, tuple))
else d_probs[j]
)
self.assertAlmostEqual(
b_lp,
d_lp,
delta=self.LOGPROB_TOLERANCE,
msg=(
f"Prompt '{prompt_short}...', token {j}: "
f"logprob diff > {self.LOGPROB_TOLERANCE} "
f"(non-DCP={b_lp:.4f}, DCP={d_lp:.4f}, "
f"diff={abs(b_lp - d_lp):.4f})"
),
)
# ---------------------------------------------------------------------------
# Test 3: DCP=4 variant (manual-only, exercises different all-gather pattern)
# ---------------------------------------------------------------------------
@unittest.skipIf(
is_in_ci(), "Requires 8 GPUs; run locally for additional DCP coverage."
)
class TestDSV31DCP4TP8GSM8K(GSM8KMixin, BasicDecodeCorrectnessMixin, CustomTestCase):
"""DCP=4 with TP=8 — exercises a different all-gather pattern than DCP=8.
With DCP=4, each rank stores 1/4 of the KV cache (vs 1/8 for DCP=8).
The 4-way all-gather uses a different GroupCoordinator configuration,
and the token-to-shard mapping (position % 4 vs position % 8) exercises
different edge cases in:
- update_local_kv_lens_for_dcp (different div/mod arithmetic)
- plan_dcp_decode_metadata (different local_kv_lens distribution)
- create_dcp_kv_indices (different padding/alignment)
- all_gather_kv_cache_for_dcp (4-way vs 8-way interleave pattern)
"""
model = DEEPSEEK_V31_MODEL_PATH
base_url = "http://127.0.0.1:31501"
gsm8k_accuracy_thres = 0.90
gsm8k_num_questions = 200
gsm8k_num_threads = 128
gsm8k_num_shots = 5
@classmethod
def setUpClass(cls):
env = os.environ.copy()
cls.process = popen_launch_server(
cls.model,
cls.base_url,
timeout=DEFAULT_TIMEOUT_FOR_SERVER_LAUNCH * 5,
other_args=_DCP4_ARGS + _COMMON_SERVER_ARGS,
env=env,
)
# Store max_total_num_tokens for DCP activation verification.
# With DCP=4, this should be ~4x the non-DCP value.
cls._dcp_max_total_num_tokens = _get_max_total_num_tokens(cls.base_url)
@classmethod
def tearDownClass(cls):
kill_process_tree(cls.process.pid, wait_timeout=60)
def test_dcp_activation_check(self):
"""Verify DCP is active by checking max_total_num_tokens is nonzero."""
self.assertGreater(
self._dcp_max_total_num_tokens,
0,
"max_total_num_tokens should be positive",
)
if __name__ == "__main__":
unittest.main()
test/registered/dcp/test_reduce_scatter_along_dim.py
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"""
Correctness test for ``GroupCoordinator.reduce_scatter_along_dim``.
The test compares the ``reduce_scatter_along_dim`` output against PyTorch's
native ``dist.reduce_scatter_tensor`` for various tensor shapes, dims, and
dtypes, exercising both positive and negative dim indexing.
Usage:
python -m pytest test_reduce_scatter_along_dim.py -v
This file doubles as the torchrun worker script. The test class launches
torchrun --nproc_per_node=N <this_file>
and asserts that all worker processes exit successfully.
"""
from __future__ import annotations
import os
import subprocess
import sys
from typing import List, Optional, Tuple
import pytest
import torch
import torch.distributed as dist
import sglang.srt.distributed.parallel_state as ps
from sglang.test.ci.ci_register import register_cuda_ci
register_cuda_ci(
est_time=120,
suite="base-b-kernel-unit-8-gpu-h200",
)
# ---------------------------------------------------------------------------
# Test parameters
# ---------------------------------------------------------------------------
# (head_num, batch_size, head_dim) shapes
TEST_SHAPES = [
(8, 16, 32),
(16, 64, 128),
(4, 1024, 512),
(16, 3, 128),
(64, 5, 512),
(128, 7, 512),
]
# For each shape we test several dim values (both positive and negative)
def _dims_for_shape(shape: Tuple[int, ...]) -> List[int]:
ndim = len(shape)
pos_dims = list(range(ndim))
neg_dims = [-d - 1 for d in range(ndim)]
return pos_dims + neg_dims
TEST_DTYPES = [torch.float16, torch.bfloat16, torch.float32]
TEST_LOOP = 8
# ---------------------------------------------------------------------------
# Helpers for multiprocess launch (shared between test and worker)
# ---------------------------------------------------------------------------
def multiprocess_test(file: str, nproc: int, timeout: int = 120) -> None:
cmd = [
"torchrun",
f"--nproc_per_node={nproc}",
file,
]
try:
result = subprocess.run(
cmd,
stdout=subprocess.PIPE,
stderr=subprocess.STDOUT,
text=True,
timeout=timeout,
)
except subprocess.TimeoutExpired as e:
raise RuntimeError(
f"torchrun (nproc={nproc}) timed out after {timeout}s\n{e.stdout}"
) from e
assert result.returncode == 0, (
f"torchrun (nproc={nproc}) failed with rc={result.returncode}\n"
f"{result.stdout}"
)
def multiprocess_main(file: str, main_fn) -> None:
if "LOCAL_RANK" in os.environ:
main_fn()
else:
sys.exit(pytest.main([file, "-v", "-s"]))
# ---------------------------------------------------------------------------
# Test class (runs via pytest, launches torchrun subprocesses)
# ---------------------------------------------------------------------------
@pytest.mark.parametrize("nproc", [2, 4, 8])
def test_reduce_scatter_along_dim(nproc: int) -> None:
device_count = torch.cuda.device_count()
if device_count < nproc:
pytest.skip(
f"Requires at least {nproc} GPUs, but only {device_count} available"
)
multiprocess_test(__file__, nproc)
# ---------------------------------------------------------------------------
# Worker logic (executed by each torchrun process)
# ---------------------------------------------------------------------------
def init_distributed():
"""Initialize distributed groups via torchrun env vars."""
local_rank = int(os.environ["LOCAL_RANK"])
world_size = int(os.environ["WORLD_SIZE"])
rank = local_rank
device = torch.device(f"cuda:{rank}")
torch.cuda.set_device(device)
dist.init_process_group(backend="gloo")
ps._WORLD = coord = ps.init_world_group(
ranks=list(range(world_size)),
local_rank=local_rank,
backend="nccl",
)
cpu_group = coord.cpu_group
nccl_group = coord.device_group
assert nccl_group is not None
return rank, device, cpu_group, nccl_group, coord
def _reference_reduce_scatter_along_dim(
input_: torch.Tensor,
dim: int,
world_size: int,
group: dist.ProcessGroup,
) -> torch.Tensor:
"""Reference implementation using torch.distributed.reduce_scatter_tensor."""
if dim < 0:
dim += input_.dim()
# Move target dim to position 0 and make contiguous
input_tensor = input_.movedim(dim, 0).contiguous()
assert input_tensor.shape[0] % world_size == 0
chunk_size = input_tensor.shape[0] // world_size
output_shape = (chunk_size,) + input_tensor.shape[1:]
output_tensor = torch.empty(
output_shape,
dtype=input_tensor.dtype,
device=input_tensor.device,
)
dist.reduce_scatter_tensor(output_tensor, input_tensor, group=group)
# Move dim back
return output_tensor.movedim(0, dim)
@torch.inference_mode()
def worker_test(
device: torch.device,
nccl_group: dist.ProcessGroup,
coord: ps.GroupCoordinator,
shape: Tuple[int, ...],
dim: int,
dtype: torch.dtype,
world_size: int,
) -> Optional[RuntimeError]:
"""Run a single (shape, dim, dtype) configuration and compare against reference."""
for _ in range(TEST_LOOP):
inp = torch.randint(0, 16, shape, dtype=dtype, device=device)
# Our implementation
out = coord.reduce_scatter_along_dim(inp, dim=dim)
# Reference
ref = _reference_reduce_scatter_along_dim(inp, dim, world_size, nccl_group)
if not torch.all(out == ref):
return RuntimeError(f"Mismatch for shape={shape}, dim={dim}, dtype={dtype}")
return None
def worker_main() -> None:
"""Entry point for each torchrun worker process."""
rank, device, cpu_group, nccl_group, coord = init_distributed()
world_size = coord.world_size
torch.cuda.set_stream(torch.cuda.Stream())
errors: List[str] = []
for shape in TEST_SHAPES:
# Only test dims where shape[dim] is divisible by world_size
for dim in _dims_for_shape(shape):
actual_dim = dim if dim >= 0 else dim + len(shape)
if shape[actual_dim] % world_size != 0:
continue
for dtype in TEST_DTYPES:
error = worker_test(
device, nccl_group, coord, shape, dim, dtype, world_size
)
if error is not None:
errors.append(str(error))
# Synchronize across ranks – if any rank fails, all fail
result = torch.tensor([int(error is not None)], device="cpu")
dist.all_reduce(result, group=cpu_group)
if result.item():
raise RuntimeError(
f"Rank {rank} failed for shape={shape}, dim={dim}, "
f"dtype={dtype}. Errors: {'; '.join(errors)}"
)
dist.destroy_process_group()
if __name__ == "__main__":
multiprocess_main(__file__, worker_main)