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[PP] Support prefill CUDA graph proxy tensors (#36248)

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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YAMY
2026-08-30 23:11:45 -07:00
committed by GitHub
co-authored by github-actions[bot]
parent 5d92e60783
commit b77cac06a9
12 changed files with 353 additions and 64 deletions
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python/sglang/srt/arg_groups/cuda_graph_hook.py
+18
View File
@@ -115,6 +115,24 @@ def apply_cuda_graph_compatibility(server_args: Any):
if (Phase.PREFILL, "backend") in server_args._cuda_graph_config_locked:
return
# PP prefill graph replay is opt-in. It is most useful for small
# aggregate forwards, while enabling it implicitly would also capture
# large buckets that can be slower than eager. An explicit backend
# selection bypasses this default policy.
if cfg.pp_size > 1 and cfg.cuda_graph_config.prefill.backend == Backend.BREAKABLE:
logger.info(
"Disabling breakable prefill CUDA graph by default for pipeline "
"parallelism. Set --cuda-graph-backend-prefill=breakable to opt in."
)
declare_resolution(
server_args,
"_apply_cuda_graph_compatibility",
cuda_graph_config=with_phase(
cfg.cuda_graph_config, Phase.PREFILL, backend=Backend.DISABLED
),
)
return
# Breakable is the CUDA default but not multimodal-compatible;
# piecewise-allowlisted archs run their validated decoder prefill
# there instead. Archs also on the breakable allowlist keep it --
python/sglang/srt/arg_groups/memory_hook.py
+15 -1
View File
@@ -16,10 +16,12 @@ from sglang.srt.arg_groups.overrides import (
use_mla_backend,
)
from sglang.srt.environ import envs
from sglang.srt.model_executor.cuda_graph_config import Backend
from sglang.srt.model_executor.cuda_graph_config import Backend, Phase
logger = logging.getLogger(__name__)
_DEFAULT_PP_PREFILL_CUDA_GRAPH_MAX_TOKENS = 8192
def handle_gpu_memory_settings(server_args: Any, gpu_mem):
"""
@@ -189,6 +191,18 @@ def handle_gpu_memory_settings(server_args: Any, gpu_mem):
else:
prefill_cuda_graph_config.max_bs = 2048
# For opt-in PP breakable graphs, capture small aggregate-token
# buckets by default and leave larger forwards on the eager path.
# Explicit max_bs or bs settings retain their existing semantics.
if (
cfg.pp_size > 1
and prefill_cuda_graph_config.backend == Backend.BREAKABLE
and (Phase.PREFILL, "bs") not in server_args._cuda_graph_config_locked
and prefill_cuda_graph_config.max_bs
> _DEFAULT_PP_PREFILL_CUDA_GRAPH_MAX_TOKENS
):
prefill_cuda_graph_config.max_bs = _DEFAULT_PP_PREFILL_CUDA_GRAPH_MAX_TOKENS
# If max_total_tokens is set, cap prefill max_bs to not exceed max_total_tokens.
if cfg.max_total_tokens is not None:
prefill_cuda_graph_config.max_bs = min(
python/sglang/srt/layers/cp/bcg.py
+1
View File
@@ -53,6 +53,7 @@ def supports_prefill_cp_bcg(server_args: ServerArgs) -> bool:
prefill_attention_backend, _ = attention_backends_of(resolved_view(server_args))
return (
cfg.enable_prefill_cp
and cfg.pp_size == 1
and resolved.attn_cp_size == cfg.tp_size
and cfg.cp_strategy == "zigzag"
and prefill_attention_backend == "trtllm_mha"
python/sglang/srt/model_executor/cuda_graph_buffer_registry.py
+4 -1
View File
@@ -953,7 +953,10 @@ def build_prefill_registry(
def _pp_source(key):
def _fn(_fb, ctx):
ppx = ctx.pp_proxy_tensors
return None if ppx is None else ppx.tensors[key]
# Proxy contracts vary by model. The capture buffers are a
# stable-address superset; only copy fields present in the
# live proxy for this model.
return None if ppx is None else ppx.tensors.get(key)
return _fn
python/sglang/srt/model_executor/model_runner_components/cuda_graph_setup.py
+14
View File
@@ -45,6 +45,7 @@ from sglang.srt.runtime_context import (
get_disagg,
get_exec,
get_flags,
get_parallel,
get_schedule,
get_spec,
)
@@ -72,6 +73,8 @@ def _align_pipeline_layers(layers: list, layer_model) -> list:
f"invalid pipeline layer range [{start_layer}, {end_layer}) for "
f"{len(layer_model.layers)} layers"
)
if len(layers) == len(layer_model.layers):
return layers
assert (
len(layers) <= end_layer - start_layer
), f"found {len(layers)} layers in PP range [{start_layer}, {end_layer})"
@@ -365,6 +368,17 @@ def capture_prefill_graph(
prefill_config = get_exec().graph.cuda_graph_config.prefill
prefill_backend = prefill_config.backend
parallel = get_parallel()
if (
prefill_backend == Backend.BREAKABLE
and parallel.enable_prefill_cp
and parallel.pp_size > 1
):
logger.warning(
"Disable prefill CUDA graph because pipeline parallelism combined "
"with prefill context parallelism is not validated."
)
return result(eager_runner)
context_length = model_runner.model_config.context_len
if prefill_backend == Backend.FULL:
max_capture_requests = prefill_config.full_prefill_max_req
python/sglang/srt/model_executor/model_runner_components/layer_setup.py
+3 -4
View File
@@ -62,12 +62,11 @@ def compute_attention_and_moe_layers(layer_model: Any) -> AttentionAndMoeLayers:
# Mamba layer with split op support - store the layer itself
attn_layer = layer
if attn_layer is not None:
# Keep these lists aligned with global layer ids. Pipeline-parallel
# models retain placeholders outside the local stage, while real
# attention modules use their global layer_id during graph replay.
attention_layers.append(attn_layer)
mha_companion_layers.append(mha_companion_layer)
elif hasattr(layer, "mixer"):
attention_layers.append(None)
mha_companion_layers.append(None)
moe_block = None
moe_fusion = None
python/sglang/srt/model_executor/runner/prefill_cuda_graph_runner.py
+40 -10
View File
@@ -185,6 +185,23 @@ def _resolve_transformer_layer_model(model: torch.nn.Module) -> torch.nn.Module:
return layer_model
def _build_layer_model_forward_kwargs(
layer_model: torch.nn.Module,
forward_batch: ForwardBatch,
pp_proxy_tensors: Optional[PPProxyTensors],
) -> Dict[str, Any]:
"""Bind optional transformer inputs by name across model signatures."""
parameters = inspect.signature(layer_model.forward).parameters
kwargs = {}
for embeds_name in ("input_embeds", "inputs_embeds"):
if embeds_name in parameters:
kwargs[embeds_name] = forward_batch.input_embeds
break
if pp_proxy_tensors is not None and "pp_proxy_tensors" in parameters:
kwargs["pp_proxy_tensors"] = pp_proxy_tensors
return kwargs
def _slice_output_rows(output: Any, num_tokens: int) -> Any:
"""Slice every tensor leaf in a transformer-body output by token rows.
@@ -333,6 +350,7 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner):
dtype=self.model_runner.dtype,
enable_mamba_track=self.mamba_track_enabled,
pp_size=self.pp_size,
is_first_pp_rank=self.model_runner.pp_group.is_first_rank,
hc_hidden_size=getattr(
self.model_runner.model_config, "hc_hidden_size", None
),
@@ -653,12 +671,12 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner):
return forward_batch.positions
def _static_pp_proxy_tensors(self, num_tokens: int) -> Optional[PPProxyTensors]:
def _capture_pp_proxy_tensors(self, num_tokens: int) -> Optional[PPProxyTensors]:
buffers = self.buffers.pp_proxy_tensors
if buffers is None:
if buffers is None or self.model_runner.pp_group.is_first_rank:
return None
return PPProxyTensors(
{key: value[:num_tokens] for key, value in buffers.items()}
{name: buffer[:num_tokens] for name, buffer in buffers.items()}
)
@contextmanager
@@ -717,20 +735,28 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner):
set_is_extend_in_batch(False)
with self._prefill_forward_context(forward_batch):
pp_kwargs = self.model_runner._pp_kwargs(
self._static_pp_proxy_tensors(num_tokens)
)
pp_proxy_tensors = self._capture_pp_proxy_tensors(num_tokens)
if self._uses_eager_prefill_tail():
# BCG / Full: capture the transformer body only.
positions = self._get_layer_model_positions(forward_batch)
input_ids = forward_batch.input_ids
kwargs = _build_layer_model_forward_kwargs(
self.layer_model, forward_batch, pp_proxy_tensors
)
if pp_proxy_tensors is not None:
input_ids = None
for embeds_name in ("input_embeds", "inputs_embeds"):
if embeds_name in kwargs:
kwargs[embeds_name] = None
break
return self.layer_model.forward(
forward_batch.input_ids,
input_ids,
positions,
forward_batch,
forward_batch.input_embeds,
**pp_kwargs,
**kwargs,
)
# tc_piecewise: compile/capture the outer model.forward path.
pp_kwargs = self.model_runner._pp_kwargs(pp_proxy_tensors)
return self.model_runner.model.forward(
forward_batch.input_ids,
forward_batch.positions,
@@ -1765,6 +1791,10 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner):
# uses real request metadata instead of padded slots. BCG has no
# request-slot padding, so static_forward_batch is already the serving batch.
tail_batch = forward_batch if full_path else static_forward_batch
if not full_path:
# MTP consumes the target model's live multimodal embeddings in its
# eager wrapper before the captured transformer body is replayed.
tail_batch.mm_input_embeds = forward_batch.mm_input_embeds
try:
with self._prefill_forward_context(
static_forward_batch,
@@ -1836,7 +1866,7 @@ class PrefillCudaGraphRunner(BaseCudaGraphRunner):
if isinstance(output, EmbeddingPoolerOutput):
return output
assert isinstance(output, PPProxyTensors)
return output[: self.raw_num_tokens]
return _slice_output_rows(output, self.raw_num_tokens)
def _validate_capture_hidden_mode(self, forward_batch: ForwardBatch) -> None:
if self.capture_hidden_mode < forward_batch.capture_hidden_mode:
python/sglang/srt/model_executor/runner_utils/buffers.py
+55 -42
View File
@@ -60,6 +60,38 @@ def _grouped_foreach_copy_(dsts: List[torch.Tensor], srcs: List[torch.Tensor]) -
foreach_copy(group_dsts, group_srcs)
def _allocate_pp_proxy_tensors(
*,
max_num_tokens: int,
max_hidden_tokens: int,
hidden_size: int,
dtype: torch.dtype,
hc_hidden_size: Optional[int] = None,
pp_proxy_topk_size: Optional[int] = None,
pp_proxy_residual_num_blocks: Optional[int] = None,
) -> Dict[str, torch.Tensor]:
"""Allocate the stable buffers consumed by an incoming PP proxy."""
is_mhc = hc_hidden_size is not None
pp_hidden_size = hc_hidden_size if is_mhc else hidden_size
pp_proxy_tensors = {
"hidden_states": torch.zeros((max_hidden_tokens, pp_hidden_size), dtype=dtype),
}
if not is_mhc:
# Only Kimi K3 supplies num_blocks: its PP bank is token-major
# [T, blocks, H]. Other models use the phase-specific hidden-token bound.
residual_shape = (
(max_num_tokens, pp_proxy_residual_num_blocks, hidden_size)
if pp_proxy_residual_num_blocks is not None
else (max_hidden_tokens, hidden_size)
)
pp_proxy_tensors["residual"] = torch.zeros(residual_shape, dtype=dtype)
if pp_proxy_topk_size is not None:
pp_proxy_tensors["topk_indices"] = torch.zeros(
(max_num_tokens, pp_proxy_topk_size), dtype=torch.int32
)
return pp_proxy_tensors
@dataclass
class DecodeInputBuffers(ForwardInputBuffers):
input_ids: torch.Tensor
@@ -129,29 +161,19 @@ class DecodeInputBuffers(ForwardInputBuffers):
torch.zeros((max_bs,), dtype=torch.bool) if enable_mamba_track else None
)
if pp_size > 1:
is_mhc = hc_hidden_size is not None
hs = hc_hidden_size if is_mhc else hidden_size
pp_proxy_tensors = {
"hidden_states": torch.zeros((max_num_token, hs), dtype=dtype),
}
if not is_mhc:
# Only Kimi K3 supplies num_blocks: its PP bank is token-major
# [T, blocks, H]. Other models keep the legacy [max_bs, H].
residual_shape = (
(max_num_token, pp_proxy_residual_num_blocks, hidden_size)
if pp_proxy_residual_num_blocks is not None
else (max_num_token, hidden_size)
pp_proxy_tensors = (
_allocate_pp_proxy_tensors(
max_num_tokens=max_num_token,
max_hidden_tokens=max_num_token,
hidden_size=hidden_size,
dtype=dtype,
hc_hidden_size=hc_hidden_size,
pp_proxy_topk_size=pp_proxy_topk_size,
pp_proxy_residual_num_blocks=pp_proxy_residual_num_blocks,
)
pp_proxy_tensors["residual"] = torch.zeros(
residual_shape, dtype=dtype
if pp_size > 1
else None
)
if pp_proxy_topk_size is not None:
pp_proxy_tensors["topk_indices"] = torch.zeros(
(max_num_token, pp_proxy_topk_size), dtype=torch.int32
)
else:
pp_proxy_tensors = None
if is_encoder_decoder:
encoder_lens = torch.full(
@@ -357,6 +379,7 @@ class PrefillInputBuffers(ForwardInputBuffers):
dtype: torch.dtype,
enable_mamba_track: bool,
pp_size: int = 1,
is_first_pp_rank: bool = False,
hc_hidden_size: Optional[int] = None,
pp_proxy_topk_size: Optional[int] = None,
pp_proxy_residual_num_blocks: Optional[int] = None,
@@ -387,29 +410,19 @@ class PrefillInputBuffers(ForwardInputBuffers):
input_embeds = None
mrope_positions = None
if pp_size > 1:
is_mhc = hc_hidden_size is not None
pp_hidden_size = hc_hidden_size if is_mhc else hidden_size
pp_proxy_tensors = {
"hidden_states": torch.zeros(
(max_num_tokens, pp_hidden_size), dtype=dtype
pp_proxy_tensors = (
_allocate_pp_proxy_tensors(
max_num_tokens=max_num_tokens,
max_hidden_tokens=max_num_tokens,
hidden_size=hidden_size,
dtype=dtype,
hc_hidden_size=hc_hidden_size,
pp_proxy_topk_size=pp_proxy_topk_size,
pp_proxy_residual_num_blocks=pp_proxy_residual_num_blocks,
)
}
if not is_mhc:
residual_shape = (
(max_num_tokens, pp_proxy_residual_num_blocks, hidden_size)
if pp_proxy_residual_num_blocks is not None
else (max_num_tokens, hidden_size)
if pp_size > 1 and not is_first_pp_rank
else None
)
pp_proxy_tensors["residual"] = torch.zeros(
residual_shape, dtype=dtype
)
if pp_proxy_topk_size is not None:
pp_proxy_tensors["topk_indices"] = torch.zeros(
(max_num_tokens, pp_proxy_topk_size), dtype=torch.int32
)
else:
pp_proxy_tensors = None
return cls(
input_ids=input_ids,
test/registered/unit/model_executor/model_runner_components/test_layer_setup.py
+14
View File
@@ -31,6 +31,20 @@ class TestComputeAttentionAndMoeLayers(unittest.TestCase):
self.assertEqual(mha_companion_layers, [attn_mha])
self.assertNotIn("_pcg_mha_companion", vars(attn_mqa))
def test_pipeline_placeholders_preserve_global_layer_ids(self):
local_attention = SimpleNamespace()
layer_model = SimpleNamespace(
layers=[SimpleNamespace(), SimpleNamespace()]
+ [SimpleNamespace(self_attn=SimpleNamespace(attn=local_attention))]
)
attention_layers, _, _, _, mha_companion_layers = (
compute_attention_and_moe_layers(layer_model)
)
self.assertEqual(attention_layers, [None, None, local_attention])
self.assertEqual(mha_companion_layers, [None, None, None])
if __name__ == "__main__":
unittest.main()
test/registered/unit/model_executor/test_prefill_cuda_graph_runner.py
+2
View File
@@ -100,6 +100,8 @@ class TestPrefillCudaGraphRunnerChunkedPrefix(CustomTestCase):
# out of the bags.
override = get_context().override_server_args(
enable_lora=False,
enable_prefill_cp=False,
pp_size=1,
cuda_graph_config=SimpleNamespace(
prefill=SimpleNamespace(bs=[1], backend=Backend.BREAKABLE)
),
test/registered/unit/model_executor/test_prefill_cuda_graph_runner_helpers.py
+127
View File
@@ -1,13 +1,19 @@
"""Unit tests for prefill CUDA graph wrapper helpers."""
import unittest
from contextlib import nullcontext
from functools import partial
from types import SimpleNamespace
import torch
from sglang.srt.model_executor.cuda_graph_buffer_registry import (
build_prefill_registry,
)
from sglang.srt.model_executor.forward_batch_info import PPProxyTensors
from sglang.srt.model_executor.runner.prefill_cuda_graph_runner import (
PrefillCudaGraphRunner,
_build_layer_model_forward_kwargs,
_resolve_transformer_layer_model,
)
from sglang.srt.model_executor.runner_utils.buffers import PrefillInputBuffers
@@ -26,7 +32,95 @@ class _LayerModel:
return input_embeds
def _make_pp_buffers_and_registry():
base = torch.zeros(3, dtype=torch.int64)
buffers = SimpleNamespace(
**{name: base.clone() for name in ("input_ids", "positions", "out_cache_loc")},
pp_proxy_tensors={
key: torch.zeros((3, 2)) for key in ("hidden_states", "residual")
},
)
registry = build_prefill_registry(
device=base.device,
max_bs=1,
max_num_token=len(base),
cache_loc_dtype=torch.int64,
share_pool=False,
source=buffers,
)
return buffers, registry
class TestPrefillCudaGraphRunnerHelpers(CustomTestCase):
def test_pp_proxy_stable_buffers_accept_full_and_hidden_only_contracts(self):
buffers, registry = _make_pp_buffers_and_registry()
full_proxy = PPProxyTensors(
{
"hidden_states": torch.full((3, 2), 2.0),
"residual": torch.full((3, 2), 3.0),
}
)
values = torch.arange(3)
fill = partial(
registry.fill_from,
SimpleNamespace(input_ids=values, positions=values, out_cache_loc=values),
raw_bs=1,
padded_bs=1,
raw_num_tokens=3,
padded_num_tokens=3,
)
fill(pp_proxy_tensors=full_proxy)
runner = PrefillCudaGraphRunner.__new__(PrefillCudaGraphRunner)
runner.buffers = buffers
runner.model_runner = SimpleNamespace(
pp_group=SimpleNamespace(is_first_rank=False)
)
capture_proxy = runner._capture_pp_proxy_tensors(3)
torch.testing.assert_close(capture_proxy.tensors, full_proxy.tensors)
self.assertEqual(
capture_proxy["hidden_states"].data_ptr(),
buffers.pp_proxy_tensors["hidden_states"].data_ptr(),
)
hidden_only_proxy = PPProxyTensors({"hidden_states": torch.full((3, 2), 4.0)})
fill(pp_proxy_tensors=hidden_only_proxy)
torch.testing.assert_close(
buffers.pp_proxy_tensors["hidden_states"][:3],
hidden_only_proxy["hidden_states"],
)
def test_layer_model_kwargs_bind_optional_inputs_by_signature(self):
def proxy_before_embeds(a, b, c, pp_proxy_tensors=None, inputs_embeds=None):
pass
cases = (
(_LayerModel(), {"input_embeds": "embeds"}),
(
SimpleNamespace(forward=proxy_before_embeds),
{"inputs_embeds": "embeds", "pp_proxy_tensors": "proxy"},
),
)
forward_batch = SimpleNamespace(input_embeds="embeds")
for layer_model, expected in cases:
with self.subTest(signature=layer_model.forward.__name__):
kwargs = _build_layer_model_forward_kwargs(
layer_model, forward_batch, "proxy"
)
self.assertEqual(kwargs, expected)
layer_model.forward(None, None, forward_batch, **kwargs)
def test_finalize_pp_proxy_trims_padded_token_rows(self):
runner = PrefillCudaGraphRunner.__new__(PrefillCudaGraphRunner)
runner.raw_num_tokens = 3
output = PPProxyTensors({"hidden_states": torch.arange(10).reshape(5, 2)})
trimmed = runner._finalize_execute_output(output)
self.assertIsInstance(trimmed, PPProxyTensors)
self.assertEqual(tuple(trimmed["hidden_states"].shape), (3, 2))
torch.testing.assert_close(
trimmed["hidden_states"][-1], output["hidden_states"][2]
)
def test_resolve_layer_model_from_language_model_wrapper(self):
layer_model = _LayerModel()
model = SimpleNamespace(language_model=SimpleNamespace(model=layer_model))
@@ -74,6 +168,7 @@ class TestPrefillCudaGraphRunnerHelpers(CustomTestCase):
dtype=torch.bfloat16,
enable_mamba_track=False,
pp_size=2,
is_first_pp_rank=False,
pp_proxy_residual_num_blocks=3,
)
@@ -93,6 +188,38 @@ class TestPrefillCudaGraphRunnerHelpers(CustomTestCase):
finalized = runner._finalize_execute_output(output)
self.assertEqual(finalized["hidden_states"].shape, (3, 8))
def test_bcg_eager_tail_uses_live_multimodal_embeddings(self):
live_embeds = object()
live_batch = SimpleNamespace(mm_input_embeds=live_embeds)
static_batch = SimpleNamespace(
input_ids=None,
positions=None,
mm_input_embeds=None,
)
runner = PrefillCudaGraphRunner.__new__(PrefillCudaGraphRunner)
runner._is_full_backend = False
runner._input_embeds_arg_idx = None
runner.buffer_registry = SimpleNamespace(has_slot=lambda _name: False)
runner.backend = SimpleNamespace(replay=lambda *_args, **_kwargs: None)
runner.layer_model = SimpleNamespace(forward=lambda *_args, **_kwargs: None)
runner.model_runner = SimpleNamespace(
model=SimpleNamespace(
forward=lambda _ids, _positions, batch, **_kwargs: batch.mm_input_embeds
)
)
runner._prefill_forward_context = lambda *_args, **_kwargs: nullcontext()
output = runner._execute_body_capture(
live_batch,
static_batch,
static_num_tokens=1,
raw_num_tokens=1,
shape_key=object(),
)
self.assertIs(output, live_embeds)
if __name__ == "__main__":
unittest.main()
test/registered/unit/server_args/test_server_args.py
+54
View File
@@ -15,6 +15,7 @@ from sglang.srt.arg_groups.attention_hook import (
handle_deterministic_inference,
)
from sglang.srt.arg_groups.cuda_graph_hook import (
apply_cuda_graph_compatibility,
disable_tc_piecewise_cudagraph_if_incompatible,
handle_cuda_graph_config,
)
@@ -31,6 +32,7 @@ from sglang.srt.arg_groups.kv_cache_hook import (
validate_prefill_only_disable_kv_cache_args,
)
from sglang.srt.arg_groups.mamba_hook import handle_mamba_backend
from sglang.srt.arg_groups.memory_hook import handle_gpu_memory_settings
from sglang.srt.arg_groups.model_path_hook import handle_load_format
from sglang.srt.arg_groups.moe_hook import (
handle_a2a_moe,
@@ -1852,6 +1854,58 @@ class TestCudaGraphConfigDataclassAccess(CustomTestCase):
self.assertEqual(config.compiler, "eager")
class TestPipelineParallelPrefillCudaGraphPolicy(CustomTestCase):
def test_pp_prefill_graph_is_opt_in(self):
cases = (
(set(), Backend.DISABLED),
({(Phase.PREFILL, "backend")}, Backend.BREAKABLE),
)
for locked, expected in cases:
with self.subTest(locked=locked):
args = ServerArgs(
model_path="dummy",
pp_size=4,
cuda_graph_config=CudaGraphConfig(
prefill=PhaseConfig(backend=Backend.BREAKABLE)
),
)
args._cuda_graph_config_locked = locked
apply_cuda_graph_compatibility(args)
self.assertEqual(
resolution_result(args, "cuda_graph_config").prefill.backend,
expected,
)
def test_pp_prefill_capture_limit_policy(self):
cases = (
(4096, None, 4096),
(32768, None, 8192),
(32768, 16384, 16384),
)
for chunked_prefill_size, max_bs, expected in cases:
with self.subTest(chunked_prefill_size=chunked_prefill_size, max_bs=max_bs):
args = ServerArgs(
model_path="dummy",
pp_size=4,
chunked_prefill_size=chunked_prefill_size,
mem_fraction_static=0.8,
cuda_graph_config=CudaGraphConfig(
decode=PhaseConfig(backend=Backend.DISABLED, max_bs=1, bs=[1]),
prefill=PhaseConfig(backend=Backend.BREAKABLE, max_bs=max_bs),
),
)
args._cuda_graph_config_locked = {(Phase.PREFILL, "backend")} | (
{(Phase.PREFILL, "max_bs")} if max_bs is not None else set()
)
with patch(
"sglang.srt.arg_groups.memory_hook.use_mla_backend",
return_value=False,
):
handle_gpu_memory_settings(args, gpu_mem=None)
prefill = resolution_result(args, "cuda_graph_config").prefill
self.assertEqual((prefill.max_bs, prefill.bs[-1]), (expected, expected))
class TestCudaGraphDisaggregationRoles(CustomTestCase):
def _handled_args(self, **overrides):
args = ServerArgs(model_path="dummy", **overrides)