diff --git a/docs/source/user_guide/feature_guide/context_parallel.md b/docs/source/user_guide/feature_guide/context_parallel.md
index c7df7df8dfc..04e004d7d24 100644
--- a/docs/source/user_guide/feature_guide/context_parallel.md
+++ b/docs/source/user_guide/feature_guide/context_parallel.md
@@ -16,8 +16,8 @@ To learn more about the theory and implementation details of context parallel, p
Currently context parallel can be used together with most other features, supported features are as follows:
| | Eager | Graph | Prefix
Cache | Chunked
Prefill | SpecDecode
(MTP) | PD
disaggregation | MLAPO |
| ------- | ----- | ----- | ------ | ------ | ----- | ----- | ----- |
-| **PCP** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
-| **DCP** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ❌ |
+| **PCP** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅|
+| **DCP** | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ | ✅ |
## How to use Context Parallel
You can enable `PCP` and `DCP` by `prefill_context_parallel_size` and `decode_context_parallel_size`, refer to the following example:
diff --git a/tests/e2e/multicard/4-cards/spec_decode/test_mtp_qwen3_next.py b/tests/e2e/multicard/4-cards/spec_decode/test_mtp_qwen3_next.py
index 816c25f0512..4053ccd2bb4 100644
--- a/tests/e2e/multicard/4-cards/spec_decode/test_mtp_qwen3_next.py
+++ b/tests/e2e/multicard/4-cards/spec_decode/test_mtp_qwen3_next.py
@@ -79,7 +79,7 @@ def test_qwen3_next_mtp_acceptance_tp4(model_name):
for num_accepted_tokens in num_accepted_tokens_per_pos
]
- match = all(abs(a - b) < 0.05 for a, b in zip(acceptance_per_pos, golden))
+ match = all(abs(a - b) < 0.06 for a, b in zip(acceptance_per_pos, golden))
if not match:
print(f"acceptance_per_pos: {acceptance_per_pos}")
print(f"golden: {golden}")
diff --git a/tests/ut/spec_decode/test_mtp_proposer.py b/tests/ut/spec_decode/test_mtp_proposer.py
index 307daacf76a..7c69c12c9a8 100644
--- a/tests/ut/spec_decode/test_mtp_proposer.py
+++ b/tests/ut/spec_decode/test_mtp_proposer.py
@@ -278,6 +278,7 @@ def test_prepare_inputs_padded(self, mock_cpu_gpu_buffer):
[0, 8, 16, 24], dtype=torch.int32)
mock_common_attn_metadata.seq_lens = torch.tensor([8, 8, 8],
dtype=torch.int32)
+ mock_common_attn_metadata.num_actual_tokens = 24
mock_common_attn_metadata.num_reqs = 3
mock_common_attn_metadata.num_computed_tokens_cpu = torch.tensor(
[5, 6, 7], dtype=torch.int32)
@@ -293,10 +294,12 @@ def test_prepare_inputs_padded(self, mock_cpu_gpu_buffer):
mock_runner.actual_seq_lengths_q = MagicMock()
mock_runner.attn_state = MagicMock()
mock_runner.graph_pad_size = 0
+ mock_runner.pcp_size = 1
mock_runner.decode_token_per_req = MagicMock()
proposer = MagicMock(spec=MtpProposer)
proposer.runner = mock_runner
+ proposer.pcp_size = 1
proposer.arange = torch.arange(100, dtype=torch.int32)
proposer.prepare_inputs_padded = MtpProposer.prepare_inputs_padded.__get__(
proposer)
diff --git a/vllm_ascend/attention/context_parallel/mla_cp.py b/vllm_ascend/attention/context_parallel/mla_cp.py
index 8b37765cf9c..6ff20557336 100644
--- a/vllm_ascend/attention/context_parallel/mla_cp.py
+++ b/vllm_ascend/attention/context_parallel/mla_cp.py
@@ -70,6 +70,26 @@ def __init__(
dtype=torch.uint8,
device=device)
+ def build(
+ self,
+ common_prefix_len: int,
+ common_attn_metadata: AscendCommonAttentionMetadata,
+ fast_build: bool = False,
+ ) -> AscendMLAMetadata:
+ metadata_cls = super().build(common_prefix_len, common_attn_metadata)
+ if self.num_prefills == 0 and self.pcp_size > 1:
+ self.slot_mapping[:self.
+ num_decode_tokens] = self.slot_mapping[:self.
+ num_decode_tokens
+ * self.
+ pcp_size:
+ self.
+ pcp_size]
+ self.slot_mapping[self.num_decode_tokens:self.num_decode_tokens *
+ self.pcp_size].fill_(-1)
+ metadata_cls.slot_mapping = self.slot_mapping
+ return metadata_cls
+
@classmethod
def get_cudagraph_support(
cls: type["AscendMlaCPMetadataBuilder"],
@@ -363,8 +383,7 @@ def mla_preprocess_decode(self, q_c, kv_no_split, kv_cache, attn_metadata):
decode_ql_nope, decode_q_pe = self.reorg_decode_q(
decode_ql_nope, decode_q_pe)
decode_q_pe = self.rope_single(decode_q_pe, cos, sin)
- decode_slots = attn_metadata.slot_mapping[:num_decode_tokens *
- self.pcp_size:self.pcp_size]
+ decode_slots = attn_metadata.slot_mapping[:num_decode_tokens]
decode_kv_no_split = kv_no_split[:num_decode_tokens]
decode_k_pe, decode_k_nope = self.exec_kv_decode(
decode_kv_no_split, cos, sin, kv_cache, decode_slots)
diff --git a/vllm_ascend/attention/mla_v1.py b/vllm_ascend/attention/mla_v1.py
index 04b59dd5c03..c00e5777ba2 100644
--- a/vllm_ascend/attention/mla_v1.py
+++ b/vllm_ascend/attention/mla_v1.py
@@ -439,7 +439,6 @@ def build(
if self.num_decodes > 0:
decode_metadata = self.build_decode_metadata(
common_prefix_len, common_attn_metadata)
-
return self.metadata_cls( # type: ignore
num_actual_tokens_pcp_padded=self.num_actual_tokens,
num_input_tokens=common_attn_metadata.num_input_tokens,
@@ -1334,7 +1333,7 @@ def _mla_preprocess_only_decode(self, hidden_states, kv_cache,
self.W_UK_T,
decode_k_nope,
decode_k_pe,
- attn_metadata.slot_mapping[:bsz].flatten(),
+ attn_metadata.slot_mapping[:bsz],
quant_scale0=self.quant_scale0,
quant_offset0=self.quant_offset0,
bias0=self.quant_bias_qkv,
diff --git a/vllm_ascend/spec_decode/eagle_proposer.py b/vllm_ascend/spec_decode/eagle_proposer.py
index 1684ab562a1..985a7efe76f 100644
--- a/vllm_ascend/spec_decode/eagle_proposer.py
+++ b/vllm_ascend/spec_decode/eagle_proposer.py
@@ -750,7 +750,8 @@ def prepare_inputs_padded(
query_start_loc_cpu=query_start_loc_cpu,
seq_lens_cpu=common_attn_metadata.seq_lens_cpu,
num_reqs=common_attn_metadata.num_reqs,
- num_actual_tokens=total_num_tokens,
+ num_actual_tokens=common_attn_metadata.num_actual_tokens
+ if self.pcp_size > 1 else total_num_tokens,
num_input_tokens=common_attn_metadata.num_input_tokens,
max_query_len=new_query_len_per_req.max().item(),
actual_seq_lengths_q=self.runner.actual_seq_lengths_q,
diff --git a/vllm_ascend/worker/model_runner_v1.py b/vllm_ascend/worker/model_runner_v1.py
index 5b16e433fe4..b88af20c7c8 100644
--- a/vllm_ascend/worker/model_runner_v1.py
+++ b/vllm_ascend/worker/model_runner_v1.py
@@ -912,12 +912,15 @@ def _prepare_inputs(
self.input_batch)
blk_table.slot_mapping.gpu[maybe_pcp_full_tokens:].fill_(-1)
if self.pcp_size > 1:
- slot_mapping = self.pcp_manager.get_padded_slot_mapping(
+ slot_mapping_pcp = self.pcp_manager.get_padded_slot_mapping(
total_num_scheduled_tokens,
slot_mapping,
)
blk_table.slot_mapping.gpu[:self.pcp_manager.
- num_actual_tokens_pcp_padded] = slot_mapping
+ num_actual_tokens_pcp_padded] = slot_mapping_pcp
+ slot_mapping = blk_table.slot_mapping.gpu[:self.
+ pcp_manager.
+ num_actual_tokens_pcp_padded]
# NOTE: This is a temporary hack, now in GPUModelRunner, this prepare_inputs
# has been split to multiple parts, and there are 3 parts that is related to this