Support EAGLE draft extend CUDA graph

python/sglang/srt/layers/attention/flashattention_backend.py

@@ -1268,6 +1268,29 @@ def init_cuda_graph_state(self, max_bs: int):
                 ),
             }
 
+            self.draft_extend_metadata = {
+                "cache_seqlens": torch.zeros(
+                    max_bs, dtype=torch.int32, device=self.device
+                ),
+                "cu_seqlens_q": torch.zeros(
+                    max_bs + 1,
+                    dtype=torch.int32,
+                    device=self.device,
+                ),
+                "cu_seqlens_k": torch.zeros(
+                    max_bs + 1, dtype=torch.int32, device=self.device
+                ),
+                "page_table": torch.zeros(
+                    max_bs,
+                    (self.max_context_len + self.page_size - 1) // self.page_size,
+                    dtype=torch.int32,
+                    device=self.device,
+                ),
+                "strided_indices": torch.arange(
+                    0, self.max_context_len, self.page_size, device=self.device
+                ),
+            }
+
         if self.topk > 1:
             self.target_verify_metadata_topk_normal = {
                 "cache_seqlens": torch.zeros(
@@ -1508,6 +1531,32 @@ def init_forward_metadata_capture_cuda_graph(
 
                 self.target_verify_metadata_topk_normal[bs] = metadata
                 self.target_verify_metadata_topk_expand[bs] = metadata_expand
+        elif forward_mode.is_draft_extend():
+            metadata.cache_seqlens_int32 = self.draft_extend_metadata["cache_seqlens"][
+                :bs
+            ]
+            metadata.cache_seqlens_int32.copy_(seq_lens.to(torch.int32))
+
+            num_tokens_per_bs = num_tokens // bs
+            metadata.max_seq_len_q = num_tokens_per_bs
+            metadata.max_seq_len_k = seq_lens.max().item()
+
+            metadata.cu_seqlens_q = torch.arange(
+                0,
+                bs * num_tokens_per_bs + 1,
+                num_tokens_per_bs,
+                dtype=torch.int32,
+                device=device,
+            )
+
+            metadata.cu_seqlens_k = self.draft_extend_metadata["cu_seqlens_k"][
+                : (bs + 1)
+            ]
+            metadata.page_table = self.draft_extend_metadata["page_table"][
+                req_pool_indices, :
+            ]
+
+            self.draft_extend_metadata[bs] = metadata
 
         if encoder_lens is not None:
             encoder_bs = encoder_lens.numel()
@@ -1732,6 +1781,23 @@ def init_forward_metadata_replay_cuda_graph(
                 metadata_expand.max_seq_len_k = (
                     metadata_expand.cache_seqlens_int32.max().item()
                 )
+        elif forward_mode.is_draft_extend():
+            metadata = self.draft_extend_metadata[bs]
+            metadata.cache_seqlens_int32.copy_(seq_lens.to(torch.int32))
+
+            metadata.max_seq_len_k = seq_lens_cpu.max().item()
+            metadata.cu_seqlens_k[1:].copy_(
+                torch.cumsum(metadata.cache_seqlens_int32, dim=0, dtype=torch.int32)
+            )
+            max_seq_pages = (
+                metadata.max_seq_len_k + self.page_size - 1
+            ) // self.page_size
+            page_indices = self.req_to_token[
+                req_pool_indices[:, None],
+                self.draft_extend_metadata["strided_indices"][:max_seq_pages],
+            ]
+            page_indices //= self.page_size
+            metadata.page_table[:, :max_seq_pages].copy_(page_indices)
 
         if encoder_lens is not None:
             # Only support encoder size 1 for now

python/sglang/srt/speculative/eagle_draft_extend_cuda_graph_runner.py

@@ -0,0 +1,250 @@
+from __future__ import annotations
+
+import bisect
+from typing import TYPE_CHECKING, Callable
+
+import torch
+
+from sglang.srt.model_executor.cuda_graph_runner import (
+    CudaGraphRunner,
+    LogitsProcessorOutput,
+    get_batch_sizes_to_capture,
+    get_global_graph_memory_pool,
+    set_global_graph_memory_pool,
+    set_torch_compile_config,
+)
+from sglang.srt.model_executor.forward_batch_info import (
+    CaptureHiddenMode,
+    ForwardBatch,
+    ForwardMode,
+)
+from sglang.srt.speculative.eagle_utils import EagleDraftInput
+
+if TYPE_CHECKING:
+    from sglang.srt.speculative.eagle_worker import EAGLEWorker
+
+
+class EAGLEDraftExtendCudaGraphRunner:
+    def __init__(self, eagle_worker: EAGLEWorker):
+        # Parse args
+        self.eagle_worker = eagle_worker
+        self.model_runner = model_runner = eagle_worker.model_runner
+        self.graphs = {}
+        self.output_buffers = {}
+        self.enable_torch_compile = model_runner.server_args.enable_torch_compile
+        self.disable_padding = model_runner.server_args.disable_cuda_graph_padding
+        self.tp_size = self.model_runner.tp_size
+        self.dp_size = model_runner.server_args.dp_size
+        self.speculative_num_steps = model_runner.server_args.speculative_num_steps
+        self.capture_bs, self.compile_bs = get_batch_sizes_to_capture(model_runner)
+        self.padded_static_len = -1
+
+        # Attention backend
+        self.num_tokens_per_bs = self.speculative_num_steps + 1
+        self.max_bs = max(self.capture_bs)
+        self.max_num_token = self.max_bs * self.num_tokens_per_bs
+
+        self.eagle_worker.draft_extend_attn_backend.init_cuda_graph_state(
+            self.max_num_token
+        )
+        self.seq_len_fill_value = (
+            self.eagle_worker.draft_extend_attn_backend.get_cuda_graph_seq_len_fill_value()
+        )
+        self.seq_lens_cpu = torch.full(
+            (self.max_bs,), self.seq_len_fill_value, dtype=torch.int32
+        )
+
+        if self.enable_torch_compile:
+            set_torch_compile_config()
+
+        # Graph inputs
+        with torch.device("cuda"):
+            self.input_ids = torch.zeros((self.max_num_token,), dtype=torch.int64)
+            self.req_pool_indices = torch.zeros((self.max_bs,), dtype=torch.int32)
+            self.out_cache_loc = torch.ones((self.max_num_token,), dtype=torch.int64)
+            self.positions = torch.zeros((self.max_num_token,), dtype=torch.int64)
+
+            if self.eagle_worker.speculative_algorithm.is_eagle3():
+                self.hidden_states = torch.zeros(
+                    (
+                        self.max_num_token,
+                        self.model_runner.model_config.hidden_size * 3,
+                    ),
+                    dtype=self.model_runner.dtype,
+                )
+            else:
+                self.hidden_states = torch.zeros(
+                    (self.max_num_token, self.model_runner.model_config.hidden_size),
+                    dtype=self.model_runner.dtype,
+                )
+
+            self.seq_lens = torch.ones((self.max_bs,), dtype=torch.int32)
+            self.extend_seq_lens = torch.ones((self.max_bs,), dtype=torch.int32)
+            self.accept_length = torch.ones((self.max_bs,), dtype=torch.int32)
+
+        # Capture
+        try:
+            self.capture()
+        except RuntimeError as e:
+            raise Exception(
+                f"Capture CUDA graph failed: {e}\n"
+                "Possible solutions:\n"
+                "1. set --mem-fraction-static to a smaller value (e.g., 0.8 or 0.7)\n"
+                "2. set --cuda-graph-max-bs to a smaller value (e.g., 16)\n"
+                "3. disable torch compile by not using --enable-torch-compile\n"
+                "4. disable CUDA graph by --disable-cuda-graph. (Not recommended. Huge performance loss)\n"
+                "Open an issue on GitHub https://github.com/sgl-project/sglang/issues/new/choose \n"
+            )
+
+    def can_run(self, forward_batch: ForwardBatch):
+        batch_size = forward_batch.seq_lens.numel()
+
+        is_bs_supported = (
+            batch_size in self.graphs
+            if self.disable_padding
+            else batch_size <= self.max_bs
+        )
+
+        return is_bs_supported
+
+    def capture(self):
+        CudaGraphRunner.capture(self)
+
+    def capture_one_batch_size(self, bs: int, forward: Callable):
+        graph = torch.cuda.CUDAGraph()
+        stream = self.stream
+        num_tokens = bs * self.num_tokens_per_bs
+
+        # Graph inputs
+        input_ids = self.input_ids[:num_tokens]
+        req_pool_indices = self.req_pool_indices[:bs]
+        seq_lens = self.seq_lens[:bs]
+        extend_seq_lens = self.extend_seq_lens[:bs]
+        accept_length = self.accept_length[:bs]
+        out_cache_loc = self.out_cache_loc[:num_tokens]
+        positions = self.positions[:num_tokens]
+        hidden_states = self.hidden_states[:num_tokens]
+
+        spec_info = EagleDraftInput(
+            hidden_states=hidden_states,
+            accept_length=accept_length,
+        )
+        spec_info.positions = None
+
+        # Forward batch
+        forward_batch = ForwardBatch(
+            forward_mode=ForwardMode.DRAFT_EXTEND,
+            batch_size=bs,
+            input_ids=input_ids,
+            req_pool_indices=req_pool_indices,
+            seq_lens=seq_lens,
+            req_to_token_pool=self.model_runner.req_to_token_pool,
+            token_to_kv_pool=self.model_runner.token_to_kv_pool,
+            out_cache_loc=out_cache_loc,
+            seq_lens_sum=seq_lens.sum(),
+            return_logprob=False,
+            positions=positions,
+            spec_algorithm=self.model_runner.spec_algorithm,
+            spec_info=spec_info,
+            capture_hidden_mode=CaptureHiddenMode.LAST,
+            attn_backend=self.eagle_worker.draft_extend_attn_backend,
+            extend_seq_lens=extend_seq_lens,
+            padded_static_len=self.padded_static_len,
+        )
+
+        self.eagle_worker.draft_extend_attn_backend.init_forward_metadata_capture_cuda_graph(
+            bs=bs,
+            num_tokens=num_tokens,
+            req_pool_indices=req_pool_indices,
+            seq_lens=seq_lens,
+            encoder_lens=None,
+            forward_mode=ForwardMode.DRAFT_EXTEND,
+            spec_info=spec_info,
+        )
+
+        # Run and capture
+        def run_once():
+            # Backup two fields, which will be modified in-place in `draft_forward`.
+            output_cache_loc_backup = forward_batch.out_cache_loc
+            hidden_states_backup = forward_batch.spec_info.hidden_states
+
+            ret = self.eagle_worker.draft_model_runner.model.forward(
+                forward_batch.input_ids,
+                forward_batch.positions,
+                forward_batch,
+            )
+
+            forward_batch.out_cache_loc = output_cache_loc_backup
+            forward_batch.spec_info.hidden_states = hidden_states_backup
+            return ret
+
+        for _ in range(2):
+            torch.cuda.synchronize()
+            self.model_runner.tp_group.barrier()
+
+            run_once()
+
+        with torch.cuda.graph(
+            graph, pool=get_global_graph_memory_pool(), stream=stream
+        ):
+            out = run_once()
+
+        set_global_graph_memory_pool(graph.pool())
+        return graph, out
+
+    def replay(self, forward_batch: ForwardBatch):
+        assert forward_batch.out_cache_loc is not None
+        # batch_size and num_seqs can be different in case there are finished examples
+        # in the batch, which will not be counted as num_seqs
+        raw_bs = forward_batch.seq_lens.numel()
+        num_tokens = forward_batch.input_ids.numel()
+        assert raw_bs * self.num_tokens_per_bs == num_tokens
+
+        index = bisect.bisect_left(self.capture_bs, raw_bs)
+        bs = self.capture_bs[index]
+        if bs != raw_bs:
+            self.accept_length.fill_(1)
+            self.out_cache_loc.zero_()
+
+        # Common inputs
+        self.input_ids[:num_tokens].copy_(forward_batch.input_ids)
+        self.seq_lens[:raw_bs].copy_(forward_batch.seq_lens[:raw_bs])
+        self.extend_seq_lens[:raw_bs].copy_(forward_batch.extend_seq_lens[:raw_bs])
+        self.out_cache_loc[:num_tokens].copy_(forward_batch.out_cache_loc)
+        self.positions[:num_tokens].copy_(forward_batch.positions)
+        self.hidden_states[:num_tokens].copy_(forward_batch.spec_info.hidden_states)
+        self.accept_length[:raw_bs].copy_(
+            forward_batch.spec_info.accept_length[:raw_bs]
+        )
+        self.req_pool_indices[:raw_bs].copy_(forward_batch.req_pool_indices)
+
+        if forward_batch.seq_lens_cpu is not None:
+            if bs != raw_bs:
+                self.seq_lens_cpu.fill_(1)
+            self.seq_lens_cpu[:raw_bs].copy_(forward_batch.seq_lens_cpu)
+
+        forward_batch.spec_info.positions = None
+        if bs != raw_bs:
+            forward_batch.spec_info.accept_length = self.accept_length[:bs]
+
+        self.eagle_worker.draft_extend_attn_backend.init_forward_metadata_replay_cuda_graph(
+            bs=bs,
+            req_pool_indices=self.req_pool_indices,
+            seq_lens=self.seq_lens,
+            seq_lens_sum=sum(forward_batch.seq_lens),
+            encoder_lens=None,
+            forward_mode=ForwardMode.DRAFT_EXTEND,
+            spec_info=forward_batch.spec_info,
+            seq_lens_cpu=self.seq_lens_cpu,
+        )
+
+        # Replay
+        self.graphs[bs].replay()
+        out = self.output_buffers[bs]
+        if bs != raw_bs:
+            forward_batch.spec_info.accept_length = self.accept_length[:raw_bs]
+            out = LogitsProcessorOutput(
+                next_token_logits=out.next_token_logits[:raw_bs],
+                hidden_states=out.hidden_states[:raw_bs],
+            )
+        return out

python/sglang/srt/speculative/eagle_utils.py

@@ -84,6 +84,7 @@ def prepare_extend_after_decode(
         self,
         batch: ScheduleBatch,
         speculative_num_steps: int,
+        pad_input: bool = False,
     ):
         assert len(self.verified_id) == len(batch.out_cache_loc)
         accept_length_cpu = batch.spec_info.accept_length_cpu
@@ -111,6 +112,50 @@ def prepare_extend_after_decode(
         batch.input_ids = self.verified_id
         self.verified_id = new_verified_id
 
+        if pad_input:
+            batch_size = sum(not req.finished() for req in batch.reqs)
+            # Total constant input length after padding
+            static_len = speculative_num_steps + 1
+            # Total size after padding
+            padded_input_size = batch_size * static_len
+
+            padded_len = padded_input_size - batch.input_ids.shape[0]
+            if padded_len > 0:
+                new_input_ids = torch.nn.functional.pad(
+                    batch.input_ids, (0, padded_len), value=0
+                )
+                position_padding = torch.arange(
+                    padded_len, device=self.positions.device
+                )
+                new_positions = torch.cat([self.positions, position_padding])
+
+                # need dummy hidden states for the padded positions
+                hidden_states_dim = self.hidden_states.shape[-1]
+                new_hidden_states = torch.cat(
+                    [
+                        self.hidden_states,
+                        torch.zeros(
+                            (padded_len, hidden_states_dim),
+                            dtype=self.hidden_states.dtype,
+                            device=self.hidden_states.device,
+                        ),
+                    ],
+                    dim=0,
+                )
+
+                # allocate KV cache location for the padded tokens
+                padded_cache_loc = torch.zeros(
+                    padded_len,
+                    dtype=batch.out_cache_loc.dtype,
+                    device=batch.out_cache_loc.device,
+                )
+                new_out_cache_loc = torch.cat([batch.out_cache_loc, padded_cache_loc])
+
+                batch.input_ids = new_input_ids
+                self.hidden_states = new_hidden_states
+                self.positions = new_positions
+                batch.out_cache_loc = new_out_cache_loc
+
     def generate_attn_arg_prefill(
         self,
         req_pool_indices: torch.Tensor,