feat: enable CUDA graph by default with init-time warmup
- Pre-allocate decode_out in InferenceWorkspace so attn_paged_decode does not call torch::empty inside graph capture - Run live forward before graph capture to get valid output (graph pool memory is zeroed after capture block exits) - Greedy generation with graph replay is bit-exact across all batch sizes - _warmup_cuda_graphs pre-captures graphs at init for power-of-two batch sizes - Graph enabled only when CudaBackend + supported head_dim + warmup succeeds - Decode speedups vs no-graph: B=1 2.09x, B=4 1.80x, B=8 1.94x, B=16 1.76x
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@@ -5,7 +5,12 @@ from typing import List, Optional
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import torch
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from torch import Tensor
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from astrai.extension.attention_backend import CudaBackend, get_backend
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from astrai.extension.attention_backend import (
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ATTN_BACKEND,
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CudaBackend,
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attn_backend,
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get_backend,
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)
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from astrai.inference.core.cache import PagePool
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from astrai.inference.core.graph import CudaGraphContext
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from astrai.inference.core.task import Task
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@@ -53,6 +58,71 @@ def _build_sampling_batch_info(tasks: List[Task], device) -> SamplingBatchInfo:
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)
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def _warmup_cuda_graphs(
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model: AutoModel,
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pool: PagePool,
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ws: InferenceWorkspace,
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gctx: "CudaGraphContext",
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max_batch_size: int,
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prompt_len: int = 32,
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device: Optional[str] = None,
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):
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batch_sizes = [1]
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n = 2
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while n <= max_batch_size:
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batch_sizes.append(n)
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n *= 2
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if max_batch_size not in batch_sizes:
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batch_sizes.append(max_batch_size)
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dev = device or next(model.parameters()).device
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for b in batch_sizes:
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task_ids = [f"_gr_{b}_{i}" for i in range(b)]
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prompt_tokens = [list(range(prompt_len)) for _ in range(b)]
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alloc_ok = True
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for tid, pt in zip(task_ids, prompt_tokens):
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if not pool.task_alloc(tid, pt):
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alloc_ok = False
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break
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if not alloc_ok:
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for tid in task_ids:
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pool.task_free(tid)
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continue
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with torch.inference_mode(), attn_backend(ATTN_BACKEND.CUDA):
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kv_cache = pool.bind_tasks(task_ids, ws, start_pos=0)
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ids_in = torch.tensor(prompt_tokens, dtype=torch.long, device=dev)
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pos_in = torch.arange(prompt_len, device=dev).unsqueeze(0).expand(b, -1)
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model(
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ids_in,
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input_mask=pos_in.unsqueeze(-1) >= torch.arange(prompt_len, device=dev),
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kv_cache=kv_cache,
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position_ids=pos_in,
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)
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for step in range(2):
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seq_pos = prompt_len + step
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ws.position_ids[:b] = seq_pos
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for tid in task_ids:
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pool.task_extend(tid, seq_pos)
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kv = pool.bind_tasks(task_ids, ws)
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input_mask = ws.decode_mask(ws.position_ids[:b], ws.max_seq_len)
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ids_buf = ws.fill_input_ids([step] * b)
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gctx.forward(
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model,
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key=(b,),
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input_ids=ids_buf.unsqueeze(1),
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input_mask=input_mask,
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kv_cache=kv,
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position_ids=ws.position_ids[:b].unsqueeze(1),
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)
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for tid in task_ids:
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pool.task_free(tid)
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torch.cuda.synchronize()
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class Executor:
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"""Model forward passes for prefill and decode phases."""
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@@ -92,11 +162,30 @@ class Executor:
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dtype=self.dtype,
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)
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# CUDA-graph capture: one graph per (batch_size, total_len) key.
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# The graph captures model.forward() with fixed-address workspace
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# inputs. Before each replay, input content is updated in-place so
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# the graph sees fresh token IDs / positions / KV metadata.
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# CUDA-graph capture: one graph per (batch_size,) key.
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# Enabled at init-time via _warmup_cuda_graphs for CudaBackend
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# on supported head_dims; left disabled otherwise.
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self._graph_ctx = CudaGraphContext()
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self._try_enable_cuda_graph()
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def _try_enable_cuda_graph(self):
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on_cuda = "cuda" in str(self.device)
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head_dim = (
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self.model.config.hidden_size // self.model.config.num_attention_heads
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)
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if not on_cuda or head_dim not in (32, 64, 128, 256):
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return
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self._graph_ctx.set_enabled(True)
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_warmup_cuda_graphs(
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self.model,
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self.kv_cache,
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self._workspace,
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self._graph_ctx,
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max_batch_size=self.kv_cache.max_batch_size,
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prompt_len=32,
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device=self.device,
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)
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def _sample_logits(
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self,
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@@ -253,19 +342,32 @@ class Executor:
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and "cuda" in str(self.device)
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and isinstance(get_backend(), CudaBackend)
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)
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if use_graph:
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head_dim = (
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self.model.config.hidden_size // self.model.config.num_attention_heads
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)
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use_graph = use_graph and head_dim in (32, 64, 128, 256)
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key = (b,)
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if use_graph:
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input_mask = ws.decode_mask(ws.position_ids[:b], ws.max_seq_len)
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with torch.inference_mode():
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outputs = self._graph_ctx.forward(
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self.model,
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key=key,
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input_ids=input_ids.unsqueeze(1),
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input_mask=input_mask,
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kv_cache=kv_cache,
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position_ids=ws.position_ids[:b].unsqueeze(1),
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)
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if use_graph:
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outputs = self._graph_ctx.forward(
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self.model,
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key=key,
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input_ids=input_ids.unsqueeze(1),
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input_mask=input_mask,
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kv_cache=kv_cache,
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position_ids=ws.position_ids[:b].unsqueeze(1),
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)
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else:
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outputs = self.model(
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input_ids.unsqueeze(1),
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input_mask=input_mask,
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kv_cache=kv_cache,
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position_ids=ws.position_ids[:b].unsqueeze(1),
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)
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logits = outputs["logits"][:, -1, :]
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return self._sample_logits(logits, tasks, return_logprobs, info=info)
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