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mfix bug when max_seqs=14 in mtp=2 scenario and raise error when cudagraph_capture_sizes can't be an integer multiple of uniform_decode_query_lentp (#3910)

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### What this PR does / why we need it?
1. Revert [bugfix for mtp in
fullgraph](0948483642)
and support it when vllm supports
2. raise error when cudagraph_capture_sizes can't be an integer multiple
of uniform_decode_query_len
3. bugfix when max_num_seqs=14 in mtp=2 scenario

### Does this PR introduce _any_ user-facing change?
no

### How was this patch tested?

- vLLM version: v0.11.0
- vLLM main:
83f478bb19

---------

Signed-off-by: zouyida2052 <zouyida2002@gmail.com>
This commit is contained in:
zouyida2052
2025-10-31 09:24:50 +08:00
committed by GitHub
parent 35a913cf1e
commit 1966885be2
4 changed files with 63 additions and 61 deletions
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39
vllm_ascend/torchair/torchair_model_runner.py
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@@ -117,12 +117,23 @@ class NPUTorchairModelRunner(NPUModelRunner):
# NOTE: To be clear, we need to make sure that during graph capture, the number of
# tokens is less than or equal to mc2_tokens_capacity. According to _set_cudagraph_sizes,
# the max number of tokens in graph is min(max_num_seqs * uniform_decode_query_len, 512).
max_num_tokens = self.max_num_reqs * self.uniform_decode_query_len
max_num_tokens = self.parallel_config.tensor_parallel_size
tp_size = self.parallel_config.tensor_parallel_size
# Use integer arithmetic for ceiling division.
num_tokens_per_tp_rank = (max_num_tokens + tp_size - 1) // tp_size
# maintain the same calculation logic as the function _align_graph_size_divisible_by_tp_size()
self.mc2_tokens_capacity = num_tokens_per_tp_rank * tp_size
max_graph_batch_size = self.calculate_new_torchair_graph_batch_size(
max_num_tokens, tp_size)
self.mc2_tokens_capacity = max_graph_batch_size
if get_ascend_soc_version(
) == AscendSocVersion.A3 and self.mc2_tokens_capacity > 512:
logger.error(
f"A3: the max number of tokens must smaller then 512, but now is {self.mc2_tokens_capacity}"
)
if get_ascend_soc_version(
) == AscendSocVersion.A2 and self.mc2_tokens_capacity > 256:
logger.error(
f"A2: the max number of tokens must smaller then 256, but now is {self.mc2_tokens_capacity}"
)
def _sync_metadata_across_dp(
self, num_tokens: int,
@@ -464,6 +475,17 @@ class NPUTorchairModelRunner(NPUModelRunner):
self.torchair_graph_batch_sizes.append(start_graph_batch_size)
start_graph_batch_size *= 2
def calculate_new_torchair_graph_batch_size(self, old_graph_batch_size,
tp_size):
cur_graph_batch_size = (old_graph_batch_size + tp_size -
1) // tp_size * tp_size
# MTP > 1: Cal LCMLeast Common Multiple with graph_batch_size and tp_size,
# Both adapter multi-dp and FIA operator
if self.speculative_config is not None and self.speculative_config.num_speculative_tokens > 1:
cur_graph_batch_size = (tp_size * old_graph_batch_size) \
// math.gcd(tp_size, old_graph_batch_size)
return cur_graph_batch_size
def select_torchair_padded_batch_size(self, batch_size: int):
for padded_batch_size in self.torchair_graph_batch_sizes:
if batch_size <= padded_batch_size:
@@ -515,13 +537,8 @@ class NPUTorchairModelRunner(NPUModelRunner):
tp_size = self.parallel_config.tensor_parallel_size
new_graph_batch_sizes = []
for graph_batch_size in self.torchair_graph_batch_sizes:
cur_graph_batch_size = (graph_batch_size + tp_size -
1) // tp_size * tp_size
# MTP > 1: Cal LCMLeast Common Multiple with graph_batch_size and tp_size,
# Both adapter multi-dp and FIA operator
if self.speculative_config is not None and self.speculative_config.num_speculative_tokens > 1:
cur_graph_batch_size = (tp_size * graph_batch_size) \
// math.gcd(tp_size, graph_batch_size)
cur_graph_batch_size = self.calculate_new_torchair_graph_batch_size(
graph_batch_size, tp_size)
if cur_graph_batch_size not in new_graph_batch_sizes and \
cur_graph_batch_size <= self.scheduler_config.max_num_batched_tokens:
new_graph_batch_sizes.append(cur_graph_batch_size)