Merge branch 'main' into fix-cutlass-prepare-moe-input

Author: zhyncs

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

@@ -1704,14 +1704,15 @@ def init_forward_metadata_replay_cuda_graph(
 
                 # 2. The second half of metadata for draft tokens (per_batch_num_tokens = topk)
                 metadata_expand = self.target_verify_metadata_topk_expand[bs]
+
                 # metadata_expand.max_seq_len_q = 1, already set in capture
                 # metadata_expand.cu_seqlens_q already set in capture
-
                 offsets = torch.arange(
                     self.speculative_num_draft_tokens, device=device
                 ).unsqueeze(
                     0
                 )  # shape: (1, self.speculative_num_draft_tokens)
+
                 cols = offsets.expand(seq_lens.numel(), -1) + seq_lens.unsqueeze(1)
                 cum_len = torch.nn.functional.pad(
                     torch.cumsum(
@@ -1728,17 +1729,20 @@ def init_forward_metadata_replay_cuda_graph(
                 ).view(1, -1)
                 # avoid extracting padded seq indices which will be out of boundary
                 mask_extraction_indices[
-                    :, spec_info.positions.numel() * self.speculative_num_draft_tokens :
+                    :,
+                    spec_info.positions.numel() * self.speculative_num_draft_tokens :,
                 ].fill_(0)
-
                 mask = spec_info.custom_mask[mask_extraction_indices].view(
                     -1, self.speculative_num_draft_tokens
                 )  # (bsz * draft_num, draft_num)
+
                 col_indices = offsets.expand(
                     mask.shape[0], self.speculative_num_draft_tokens
                 )
                 keys = torch.where(
-                    mask, col_indices, col_indices + self.speculative_num_draft_tokens
+                    mask,
+                    col_indices,
+                    col_indices + self.speculative_num_draft_tokens,
                 )
                 _, sort_order = torch.sort(keys, dim=1)
 
@@ -1747,6 +1751,7 @@ def init_forward_metadata_replay_cuda_graph(
                     .gather(1, cols)
                     .repeat_interleave(self.speculative_num_draft_tokens, dim=0)
                 )  # (bsz, draft_num)
+
                 metadata_expand.page_table.copy_(
                     non_masked_page_table.gather(1, sort_order)
                 )
@@ -1758,6 +1763,7 @@ def init_forward_metadata_replay_cuda_graph(
                         dtype=torch.int32,
                     )
                 )
+
         elif forward_mode.is_draft_extend():
             metadata = self.draft_extend_metadata[bs]
             metadata.cache_seqlens_int32.copy_(seq_lens)
@@ -1767,7 +1773,11 @@ def init_forward_metadata_replay_cuda_graph(
                 torch.cumsum(metadata.cache_seqlens_int32, dim=0, dtype=torch.int32)
             )
             accept_length = spec_info.accept_length[:bs]
-            metadata.max_seq_len_q = max(spec_info.accept_length_cpu) + 1
+            if spec_info.accept_length_cpu:
+                metadata.max_seq_len_q = max(spec_info.accept_length_cpu) + 1
+            else:
+                metadata.max_seq_len_q = 1
+
             metadata.cu_seqlens_q[1:].copy_(
                 torch.cumsum(accept_length, dim=0, dtype=torch.int32)
             )

python/sglang/srt/managers/scheduler.py

@@ -1821,11 +1821,6 @@ def prepare_mlp_sync_batch_raw(
         else:
             can_cuda_graph = 0
 
-        if not spec_algorithm.is_none():
-            # TODO(sang): Support cuda graph when idle batch is there.
-            if local_batch is None or local_batch.forward_mode.is_idle():
-                can_cuda_graph = 0
-
         is_extend_in_batch = (
             local_batch.forward_mode.is_extend() if local_batch else False
         )

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -306,28 +306,30 @@ def __init__(self, model_runner: ModelRunner):
                 self.encoder_lens = None
 
             if self.require_gathered_buffer:
+                self.gathered_buffer = torch.zeros(
+                    (
+                        self.max_num_token,
+                        self.model_runner.model_config.hidden_size,
+                    ),
+                    dtype=self.model_runner.dtype,
+                )
                 if self.require_mlp_tp_gather:
-                    self.gathered_buffer = torch.zeros(
-                        (
-                            self.max_bs * self.dp_size * self.num_tokens_per_bs,
-                            self.model_runner.model_config.hidden_size,
-                        ),
-                        dtype=self.model_runner.dtype,
-                    )
                     self.global_num_tokens_gpu = torch.zeros(
                         (self.dp_size,), dtype=torch.int32
                     )
                 else:
                     assert self.require_attn_tp_gather
-                    self.gathered_buffer = torch.zeros(
-                        (
-                            self.max_bs * self.num_tokens_per_bs,
-                            self.model_runner.model_config.hidden_size,
-                        ),
-                        dtype=self.model_runner.dtype,
-                    )
                     self.global_num_tokens_gpu = torch.zeros((1,), dtype=torch.int32)
 
+            self.custom_mask = torch.ones(
+                (
+                    (self.seq_lens.sum().item() + self.max_num_token)
+                    * self.num_tokens_per_bs
+                ),
+                dtype=torch.bool,
+                device="cuda",
+            )
+
         # Capture
         try:
             with model_capture_mode():
@@ -674,19 +676,20 @@ def replay_prepare(
             self.num_token_non_padded.copy_(forward_batch.num_token_non_padded)
         if self.enable_two_batch_overlap:
             self.tbo_plugin.replay_prepare(
-                forward_mode=forward_batch.forward_mode,
+                forward_mode=self.capture_forward_mode,
                 bs=bs,
                 num_token_non_padded=len(forward_batch.input_ids),
             )
-
+        if forward_batch.forward_mode.is_idle() and forward_batch.spec_info is not None:
+            forward_batch.spec_info.custom_mask = self.custom_mask
         # Attention backend
         self.model_runner.attn_backend.init_forward_metadata_replay_cuda_graph(
             bs,
             self.req_pool_indices[:bs],
             self.seq_lens[:bs],
             forward_batch.seq_lens_sum + (bs - raw_bs) * self.seq_len_fill_value,
             self.encoder_lens[:bs] if self.is_encoder_decoder else None,
-            forward_batch.forward_mode,
+            self.capture_forward_mode,
             forward_batch.spec_info,
             seq_lens_cpu=self.seq_lens_cpu[:bs],
         )
@@ -736,11 +739,7 @@ def get_spec_info(self, num_tokens: int):
             else:
                 spec_info = EagleVerifyInput(
                     draft_token=None,
-                    custom_mask=torch.ones(
-                        (num_tokens * self.model_runner.model_config.context_len),
-                        dtype=torch.bool,
-                        device="cuda",
-                    ),
+                    custom_mask=self.custom_mask,
                     positions=None,
                     retrive_index=None,
                     retrive_next_token=None,

python/sglang/srt/speculative/eagle_utils.py

@@ -99,6 +99,8 @@ def create_idle_input(
             topk_p=torch.empty((0, topk), device=device, dtype=torch.float32),
             topk_index=torch.empty((0, topk), device=device, dtype=torch.int64),
             capture_hidden_mode=capture_hidden_mode,
+            accept_length=torch.empty((0,), device=device, dtype=torch.int32),
+            accept_length_cpu=[],
         )
 
     def prepare_extend_after_decode(

python/sglang/srt/speculative/eagle_worker.py

@@ -322,13 +322,11 @@ def forward_batch_speculative_generation(
             logits_output, verify_output, model_worker_batch, can_run_cuda_graph = (
                 self.verify(batch, spec_info)
             )
-            need_forward, can_run_draft_extend_cuda_graph = (
-                self.check_forward_draft_extend_after_decode(batch)
-            )
-            if need_forward:
+
+            if self.check_forward_draft_extend_after_decode(batch):
                 with self.draft_tp_context(self.draft_model_runner.tp_group):
                     self.forward_draft_extend_after_decode(
-                        batch, can_run_draft_extend_cuda_graph
+                        batch,
                     )
             return (
                 logits_output,
@@ -344,7 +342,7 @@ def check_forward_draft_extend_after_decode(self, batch: ScheduleBatch):
             and batch.spec_info.verified_id.shape[0] > 0
         )
         if not self.server_args.enable_dp_attention:
-            return local_need_forward, True
+            return local_need_forward
 
         global_need_forward = torch.tensor(
             [
@@ -357,10 +355,7 @@ def check_forward_draft_extend_after_decode(self, batch: ScheduleBatch):
         )
         global_need_forward_cnt = global_need_forward[0].item()
         need_forward = global_need_forward_cnt > 0
-        can_run_draft_extend_cuda_graph = (
-            global_need_forward_cnt == get_tensor_model_parallel_world_size()
-        )
-        return need_forward, can_run_draft_extend_cuda_graph
+        return need_forward
 
     def forward_target_extend(
         self, batch: ScheduleBatch
@@ -816,15 +811,12 @@ def forward_draft_extend(
         assert forward_batch.spec_info is batch.spec_info
         self.capture_for_decode(logits_output, forward_batch.spec_info)
 
-    def forward_draft_extend_after_decode(
-        self, batch: ScheduleBatch, can_run_draft_extend_cuda_graph: bool
-    ):
+    def forward_draft_extend_after_decode(self, batch: ScheduleBatch):
         # Backup fields that will be modified in-place
         seq_lens_backup = batch.seq_lens.clone()
         req_pool_indices_backup = batch.req_pool_indices
         accept_length_backup = batch.spec_info.accept_length
         return_logprob_backup = batch.return_logprob
-
         input_is_idle = batch.forward_mode.is_idle()
         if not input_is_idle:
             # Prepare metadata
@@ -836,14 +828,18 @@ def forward_draft_extend_after_decode(
             else:
                 batch = batch.copy()
                 batch.prepare_for_idle()
+                hidden_size = (
+                    self.model_config.hidden_size * 3
+                    if self.speculative_algorithm.is_eagle3()
+                    else self.model_config.hidden_size
+                )
                 batch.spec_info = EagleDraftInput.create_idle_input(
                     device=self.device,
-                    hidden_size=self.model_config.hidden_size,
+                    hidden_size=hidden_size,
                     dtype=self.model_config.dtype,
                     topk=self.topk,
                     capture_hidden_mode=CaptureHiddenMode.LAST,
                 )
-
         batch.return_hidden_states = False
         model_worker_batch = batch.get_model_worker_batch()
         model_worker_batch.spec_num_draft_tokens = self.speculative_num_draft_tokens
@@ -858,8 +854,7 @@ def forward_draft_extend_after_decode(
 
         # Run
         can_cuda_graph = (
-            can_run_draft_extend_cuda_graph
-            and self.cuda_graph_runner_for_draft_extend
+            self.cuda_graph_runner_for_draft_extend
             and self.cuda_graph_runner_for_draft_extend.can_run(forward_batch)
         )
         if can_cuda_graph: