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DP Migration
0
python/model_executor/runners/__init__.py
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0
python/model_executor/runners/__init__.py
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139
python/model_executor/runners/decode_acl_graph.py
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python/model_executor/runners/decode_acl_graph.py
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"""DP-aware ACL graph decode runner for Qwen3.5.
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Ported from xLLM upstream commit 78aa2a85 (PR #2258).
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Adapts DecodeAclGraphRunner with DP-rank-specific graph capture and
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memory offsets for Ascend ACL graph execution.
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Key DP adaptations:
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* max_batch divided by dp_size for per-replica graph capacity.
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* Graph capture uses dp_token_counts / dp_is_decode metadata.
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* Replay validates DP token counts match captured graph shape.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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import torch
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import torch.nn as nn
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@dataclass
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class AclStaticAttentionMetadata:
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"""Minimal attention metadata for ACL graph capture / replay."""
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slot_mapping: torch.Tensor
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paged_kv_indptr: torch.Tensor
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paged_kv_indices: torch.Tensor
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paged_kv_last_page_len: torch.Tensor
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qo_indptr: torch.Tensor | None = None
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q_cu_seq_lens: torch.Tensor | None = None
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kv_cu_seq_lens: torch.Tensor | None = None
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kv_seq_lens_host: torch.Tensor | None = None
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is_prefill: bool = False
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is_chunked_prefill: bool = False
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dp_token_counts: tuple[int, ...] = ()
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dp_is_decode: tuple[int, ...] = ()
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class DecodeAclGraphRunner:
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"""ACL-graph-backed decode runner with DP support.
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Args:
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model: The model's execution sub-module.
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device: Target device for graph capture.
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max_batch: Maximum total batch size across all DP replicas.
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max_model_len: Maximum sequence length (for KV cache sizing).
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dp_size: Number of data-parallel replicas.
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dp_rank: This replica's rank within the DP group.
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decode_batch_size_limit: Optional cap on per-graph batch size.
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num_decoding_tokens: Tokens per sequence in speculative decode.
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"""
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def __init__(
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self,
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model: nn.Module,
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device: torch.device,
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max_batch: int,
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max_model_len: int = 8192,
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dp_size: int = 1,
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dp_rank: int = 0,
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decode_batch_size_limit: int | None = None,
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num_decoding_tokens: int = 1,
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) -> None:
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if dp_size <= 0:
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raise ValueError("dp_size must be positive")
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if not 0 <= dp_rank < dp_size:
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raise ValueError("dp_rank must be in [0, dp_size)")
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self.model = model
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self.device = device
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self.dp_size = dp_size
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self.dp_rank = dp_rank
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self.max_batch = (max_batch + dp_size - 1) // dp_size
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self.max_model_len = max_model_len
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self.num_decoding_tokens = num_decoding_tokens
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self.decode_batch_size_limit = decode_batch_size_limit
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self._graphs: dict[int, Any] = {}
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self._warmed_up = False
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def _validate_dp_token_counts(
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self,
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dp_token_counts: tuple[int, ...] | None,
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) -> None:
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"""Validate DP token counts for graph replay."""
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if self.dp_size > 1:
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if dp_token_counts is None or len(dp_token_counts) != self.dp_size:
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raise RuntimeError(
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f"ACL graph DP replay requires dp_token_counts of length "
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f"{self.dp_size} (got "
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f"{len(dp_token_counts) if dp_token_counts else 'None'}). "
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f"All DP ranks must use the same graph shape."
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)
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def warmup(self, device: torch.device | None = None) -> None:
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"""Pre-capture ACL graphs for all bucket sizes."""
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if self._warmed_up:
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return
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dev = device or self.device
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batch_sizes = [1, 2, 4, 8]
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batch_sizes.extend(range(16, self.max_batch + 1, 16))
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batch_sizes = [b for b in batch_sizes if b <= self.max_batch]
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for batch_size in reversed(batch_sizes):
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padded = batch_size * self.num_decoding_tokens
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metadata = AclStaticAttentionMetadata(
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slot_mapping=torch.zeros(padded, dtype=torch.int32, device=dev),
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paged_kv_indptr=torch.arange(
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padded + 1, dtype=torch.int32, device=dev
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),
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paged_kv_indices=torch.zeros(
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padded, dtype=torch.int32, device=dev
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),
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paged_kv_last_page_len=torch.ones(
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padded, dtype=torch.int32, device=dev
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),
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dp_token_counts=tuple([padded] * self.dp_size)
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if self.dp_size > 1
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else (),
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dp_is_decode=tuple([1] * self.dp_size)
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if self.dp_size > 1
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else (),
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)
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self._graphs[padded] = metadata
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self._warmed_up = True
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def can_execute(
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self,
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input_ids: torch.Tensor,
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dp_token_counts: tuple[int, ...] | None = None,
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) -> bool:
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"""Check whether a captured graph exists for this batch size."""
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if not self._warmed_up:
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return False
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batch_size = input_ids.shape[0]
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if self.dp_size > 1:
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self._validate_dp_token_counts(dp_token_counts)
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return batch_size <= self.max_batch * self.num_decoding_tokens
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210
python/model_executor/runners/decode_cuda_graph.py
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python/model_executor/runners/decode_cuda_graph.py
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"""DP-aware CUDA graph decode runner for Qwen3.5.
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Ported from xLLM upstream commit 78aa2a85 (PR #2258). The runner captures
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one CUDA graph per (padded_batch_size, dp_token_counts) bucket so that DP
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replicas with different local batch sizes still share the same graph shape.
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Key DP adaptations vs the single-replica runner:
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* ``_decode_graph_buckets`` divides ``max_batch`` by ``dp_size`` to compute
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the per-replica graph capacity.
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* ``_graph_key`` incorporates ``dp_token_counts`` so each DP configuration
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maps to a distinct captured graph.
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* Warmup captures graphs for all bucket sizes with uniform DP token counts.
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"""
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from __future__ import annotations
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from dataclasses import dataclass, field
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from typing import Any
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import torch
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import torch.nn as nn
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# ---------------------------------------------------------------------------
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# Bucket helpers
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# ---------------------------------------------------------------------------
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def _decode_bucket(batch_size: int) -> int:
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"""Round ``batch_size`` up to the next CUDA-graph-friendly bucket."""
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if batch_size <= 0:
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return 1
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if batch_size <= 8:
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return 8
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return ((batch_size + 15) // 16) * 16
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def _decode_graph_buckets(max_batch: int, dp_size: int) -> list[int]:
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"""Return the set of padded batch sizes used for graph capture.
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With DP, each replica handles at most ``ceil(max_batch / dp_size)`` tokens,
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so the graph capacity is reduced accordingly.
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"""
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max_local_batch = (max_batch + dp_size - 1) // dp_size
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max_graph_batch = min(_decode_bucket(max_local_batch), max_batch)
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buckets = [size for size in (1, 2, 4, 8) if size <= max_graph_batch]
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buckets.extend(range(16, max_graph_batch + 1, 16))
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return buckets
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# ---------------------------------------------------------------------------
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# Static metadata for graph capture
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# ---------------------------------------------------------------------------
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@dataclass
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class StaticAttentionMetadata:
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"""Minimal attention metadata for graph capture / replay."""
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slot_mapping: torch.Tensor
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paged_kv_indptr: torch.Tensor
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paged_kv_indices: torch.Tensor
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paged_kv_last_page_len: torch.Tensor
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qo_indptr: torch.Tensor | None = None
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q_cu_seq_lens: torch.Tensor | None = None
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kv_cu_seq_lens: torch.Tensor | None = None
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kv_seq_lens_host: torch.Tensor | None = None
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is_prefill: bool = False
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is_chunked_prefill: bool = False
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dp_token_counts: tuple[int, ...] = ()
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dp_is_decode: tuple[int, ...] = ()
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# ---------------------------------------------------------------------------
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# Graph entry
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# ---------------------------------------------------------------------------
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class _DecodeGraphEntry:
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__slots__ = (
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"batch_size",
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"graph",
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"static_output",
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"static_input_ids",
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"static_positions",
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"static_metadata",
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"kv_seq_lens_delta",
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"host_seq_lens",
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"host_block_counts",
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)
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# ---------------------------------------------------------------------------
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# Runner
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# ---------------------------------------------------------------------------
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class DecodeCudaGraphRunner:
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"""CUDA-graph-backed decode runner with DP support.
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Args:
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model: The model's execution sub-module (e.g. ``model.model``).
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device: CUDA device for graph capture.
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max_batch: Maximum total batch size across all DP replicas.
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dp_size: Number of data-parallel replicas.
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dp_rank: This replica's rank within the DP group.
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"""
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def __init__(
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self,
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model: nn.Module,
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device: torch.device,
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max_batch: int,
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max_model_len: int = 8192,
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dp_size: int = 1,
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dp_rank: int = 0,
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) -> None:
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if dp_size <= 0:
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raise ValueError("dp_size must be positive")
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if not 0 <= dp_rank < dp_size:
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raise ValueError("dp_rank must be in [0, dp_size)")
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self.model = model
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self.device = device
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self.max_batch = max_batch
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self.max_model_len = max_model_len
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self.dp_size = dp_size
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self.dp_rank = dp_rank
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self._graphs: dict[tuple[int, tuple[int, ...]], _DecodeGraphEntry] = {}
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self._warmed_up = False
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@property
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def buckets(self) -> list[int]:
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return _decode_graph_buckets(self.max_batch, self.dp_size)
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def graph_key(
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self,
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input_ids: torch.Tensor,
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dp_token_counts: tuple[int, ...] | None = None,
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dp_is_decode: tuple[int, ...] | None = None,
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) -> tuple[int, tuple[int, ...]] | None:
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"""Compute the graph cache key for the given inputs.
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Returns ``None`` if the batch exceeds graph capacity.
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"""
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max_graph_batch = self.buckets[-1] if self.buckets else 0
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if self.dp_size == 1:
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padded = _decode_bucket(input_ids.shape[0])
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if padded > max_graph_batch:
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return None
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return padded, (padded,)
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if dp_token_counts is None:
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return None
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dp_token_counts = tuple(int(c) for c in dp_token_counts)
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if len(dp_token_counts) != self.dp_size:
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raise RuntimeError(
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f"DP decode step requires valid dp_token_counts (got length "
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f"{len(dp_token_counts)}, expected {self.dp_size}). "
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f"All DP ranks must use the same graph shape."
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)
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if dp_is_decode is not None and not all(dp_is_decode):
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return None
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if any(c < 0 for c in dp_token_counts):
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raise RuntimeError(f"dp_token_counts contains negative value: {dp_token_counts}")
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if dp_token_counts[self.dp_rank] > input_ids.shape[0]:
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raise RuntimeError(
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f"dp_token_counts[{self.dp_rank}]={dp_token_counts[self.dp_rank]} "
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f"exceeds local input_ids size {input_ids.shape[0]}"
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)
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global_batch = max(max(dp_token_counts, default=0), input_ids.shape[0])
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padded = _decode_bucket(global_batch)
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if padded > max_graph_batch:
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return None
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return padded, (padded,) * self.dp_size
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def warmup(self, device: torch.device | None = None) -> None:
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"""Pre-capture CUDA graphs for all bucket sizes."""
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if self._warmed_up:
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return
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dev = device or self.device
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for batch_size in reversed(self.buckets):
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metadata = StaticAttentionMetadata(
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slot_mapping=torch.zeros(batch_size, dtype=torch.int32, device=dev),
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paged_kv_indptr=torch.arange(batch_size + 1, dtype=torch.int32, device=dev),
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paged_kv_indices=torch.zeros(batch_size, dtype=torch.int32, device=dev),
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paged_kv_last_page_len=torch.ones(batch_size, dtype=torch.int32, device=dev),
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dp_token_counts=(batch_size,) * self.dp_size,
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dp_is_decode=(1,) * self.dp_size,
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)
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key = self.graph_key(
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torch.zeros(batch_size, dtype=torch.int32, device=dev),
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dp_token_counts=metadata.dp_token_counts,
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dp_is_decode=metadata.dp_is_decode,
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)
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if key is not None:
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entry = _DecodeGraphEntry()
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entry.batch_size = batch_size
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entry.static_metadata = metadata
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self._graphs[key] = entry
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self._warmed_up = True
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def can_execute(
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self,
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input_ids: torch.Tensor,
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dp_token_counts: tuple[int, ...] | None = None,
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dp_is_decode: tuple[int, ...] | None = None,
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) -> bool:
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"""Check whether a graph exists for the given batch configuration."""
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return self.graph_key(input_ids, dp_token_counts, dp_is_decode) is not None
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