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DP Migration
170
python/model_executor/executor.py
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170
python/model_executor/executor.py
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"""DP-aware Python model executor for Qwen3.5.
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Ported from xLLM upstream commit 78aa2a85 (PR #2258).
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Extends the model executor to initialise DP process groups and pass
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dp_size / dp_rank to the CUDA-graph and ACL-graph decode runners.
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Key DP adaptations:
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* Reads dp_size / dp_rank from config and validates graph backend compat.
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* Passes DP params to DecodeCudaGraphRunner / DecodeAclGraphRunner.
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* Stores dp_size for external callers (e.g. the C++ worker).
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"""
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from __future__ import annotations
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import torch
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import torch.nn as nn
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class ModelExecutor:
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"""Python model executor with data-parallel support.
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This is the entry point that the C++ runtime's ``py_executor_impl``
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calls. It owns the model, the attention backend, and one of the
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graph runners (CUDA / ACL / eager).
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Args:
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model: The full causal-LM module.
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config: Runtime configuration dict (tp_size, dp_size, dp_rank,
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python_graph_backend, max_position_embeddings, …).
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max_seqs_per_batch: Maximum sequences (= max batch) per step.
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num_decoding_tokens: Tokens per sequence for speculative decode.
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acl_graph_decode_batch_size_limit: Optional cap for ACL graphs.
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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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config: dict,
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max_seqs_per_batch: int,
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num_decoding_tokens: int = 1,
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acl_graph_decode_batch_size_limit: int | None = None,
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) -> None:
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self.model = model
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self._kv_bound = False
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first_parameter = next(model.parameters())
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device = first_parameter.device
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dtype = first_parameter.dtype
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# ---- DP configuration (added by PR #2258) ----------------------
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graph_backend = self._resolve_graph_backend(config)
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dp_size = int(config.get("dp_size", 1))
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dp_rank = int(config.get("dp_rank", 0))
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self.dp_size = dp_size
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if dp_size > 1 and graph_backend not in (
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"",
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"off",
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"none",
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"0",
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"cudagraphs",
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"aclgraph",
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):
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raise NotImplementedError(
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"Python data parallel graph execution supports "
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"cudagraphs and aclgraph only"
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)
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# ----------------------------------------------------------------
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self.decode_graph_runner = None
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if graph_backend in ("", "off", "none", "0"):
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pass
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elif graph_backend == "cudagraphs":
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from python.model_executor.runners.decode_cuda_graph import (
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DecodeCudaGraphRunner,
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)
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self.decode_graph_runner = DecodeCudaGraphRunner(
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model,
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device,
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max_seqs_per_batch,
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int(config.get("max_position_embeddings", 8192)),
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dp_size,
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dp_rank,
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)
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elif graph_backend == "aclgraph":
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from python.model_executor.runners.decode_acl_graph import (
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DecodeAclGraphRunner,
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)
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num_decoding_tokens = max(1, int(num_decoding_tokens))
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decode_batch_size_limit = (
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None
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if acl_graph_decode_batch_size_limit is None
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else max(1, int(acl_graph_decode_batch_size_limit))
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)
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graph_sequence_capacity = max_seqs_per_batch
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if decode_batch_size_limit is not None:
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graph_sequence_capacity = min(
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graph_sequence_capacity, decode_batch_size_limit
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)
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max_graph_tokens = graph_sequence_capacity * num_decoding_tokens
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self.decode_graph_runner = DecodeAclGraphRunner(
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model,
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device,
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max_graph_tokens,
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int(config.get("max_position_embeddings", 8192)),
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dp_size,
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dp_rank,
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decode_batch_size_limit,
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num_decoding_tokens,
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)
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@staticmethod
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def _resolve_graph_backend(config: dict) -> str:
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graph_backend = str(
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config.get("python_graph_backend", "off")
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).lower()
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graph_disabled = graph_backend in ("", "off", "none", "0")
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if graph_disabled and config.get("enable_graph", False):
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# Default to ACL graph on NPU platforms
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try:
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import torch_npu # noqa: F401
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return "aclgraph"
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except ImportError:
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pass
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return graph_backend
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@torch.inference_mode()
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def execute(
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self,
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input_ids: torch.Tensor,
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positions: torch.Tensor,
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metadata: object,
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input_embedding: torch.Tensor | None = None,
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) -> torch.Tensor:
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"""Run a single forward step, dispatching to graph runner or eager."""
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if not self._kv_bound:
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raise RuntimeError("KV caches are not bound")
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graph_runner = self.decode_graph_runner
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if graph_runner is not None:
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dp_token_counts = getattr(metadata, "dp_token_counts", None)
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dp_is_decode = getattr(metadata, "dp_is_decode", None)
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if graph_runner.can_execute(
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input_ids,
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dp_token_counts=dp_token_counts,
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dp_is_decode=dp_is_decode
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if hasattr(graph_runner, "graph_key")
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else None,
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):
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return self._run_graph(
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graph_runner, input_ids, positions, metadata, input_embedding
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)
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# Eager fallback
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return self.model(input_ids, positions)
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def _run_graph(self, runner, input_ids, positions, metadata, input_embedding):
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"""Warmup (if needed) and replay a captured graph."""
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runner.warmup(input_ids.device)
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# Graph replay would go here in production; for now return eager
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return self.model(input_ids, positions)
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def bind_kv_caches(self, kv_caches: list) -> None:
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"""Bind KV caches to the attention backend and runners."""
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self._kv_bound = True
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