Organize code (rename, movement) (#953)

python/sglang/srt/model_executor/cuda_graph_runner.py

@@ -29,8 +29,8 @@ from sglang.srt.layers.logits_processor import (
     LogitsMetadata,
     LogitsProcessor,
 )
-from sglang.srt.managers.schedule_batch import (
-    Batch,
+from sglang.srt.managers.schedule_batch import ScheduleBatch
+from sglang.srt.model_executor.forward_batch_info import (
     ForwardMode,
     InputMetadata,
     init_flashinfer_args,
@@ -202,7 +202,7 @@ class CudaGraphRunner:
         self.graph_memory_pool = graph.pool()
         return graph, None, out, flashinfer_decode_wrapper
 
-    def replay(self, batch: Batch):
+    def replay(self, batch: ScheduleBatch):
         assert batch.out_cache_loc is not None
         raw_bs = len(batch.reqs)
 

python/sglang/srt/model_executor/forward_batch_info.py

@@ -0,0 +1,256 @@
+"""
+Copyright 2023-2024 SGLang Team
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+    http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+"""
+
+"""ModelRunner runs the forward passes of the models."""
+from dataclasses import dataclass
+from enum import IntEnum, auto
+from typing import List
+
+import numpy as np
+import torch
+
+from sglang.srt.mem_cache.memory_pool import BaseTokenToKVPool, ReqToTokenPool
+
+
+class ForwardMode(IntEnum):
+    # Prefill a new sequence. This is deprecated now. "EXTEND" covers this case.
+    PREFILL = auto()
+    # Extend a sequence. The KV cache of the first part of the sequence is already computed (e.g., system prompt).
+    EXTEND = auto()
+    # Decode one token.
+    DECODE = auto()
+
+
+@dataclass
+class InputMetadata:
+    """Store all inforamtion of a forward pass."""
+
+    forward_mode: ForwardMode
+    batch_size: int
+    total_num_tokens: int
+    req_pool_indices: torch.Tensor
+    seq_lens: torch.Tensor
+    positions: torch.Tensor
+    req_to_token_pool: ReqToTokenPool
+    token_to_kv_pool: BaseTokenToKVPool
+
+    # For extend
+    extend_seq_lens: torch.Tensor
+    extend_start_loc: torch.Tensor
+    extend_no_prefix: bool
+
+    # Output location of the KV cache
+    out_cache_loc: torch.Tensor = None
+
+    # Output options
+    return_logprob: bool = False
+    top_logprobs_nums: List[int] = None
+
+    # Trition attention backend
+    triton_max_seq_len: int = 0
+    triton_max_extend_len: int = 0
+    triton_start_loc: torch.Tensor = None
+    triton_prefix_lens: torch.Tensor = None
+
+    # FlashInfer attention backend
+    flashinfer_prefill_wrapper_ragged: "BatchPrefillWithRaggedKVCacheWrapper" = None
+    flashinfer_prefill_wrapper_paged: "BatchPrefillWithPagedKVCacheWrapper" = None
+    flashinfer_decode_wrapper: "BatchDecodeWithPagedKVCacheWrapper" = None
+    flashinfer_use_ragged: bool = False
+
+    @classmethod
+    def create(
+        cls,
+        model_runner,
+        forward_mode,
+        req_pool_indices,
+        seq_lens,
+        prefix_lens,
+        position_ids_offsets,
+        out_cache_loc,
+        top_logprobs_nums=None,
+        return_logprob=False,
+        skip_flashinfer_init=False,
+    ):
+        flashinfer_use_ragged = False
+        if not skip_flashinfer_init and not model_runner.server_args.disable_flashinfer:
+            if forward_mode != ForwardMode.DECODE and int(torch.sum(seq_lens)) > 4096:
+                flashinfer_use_ragged = True
+            init_flashinfer_args(
+                forward_mode,
+                model_runner,
+                req_pool_indices,
+                seq_lens,
+                prefix_lens,
+                model_runner.flashinfer_decode_wrapper,
+                flashinfer_use_ragged,
+            )
+
+        batch_size = len(req_pool_indices)
+
+        if forward_mode == ForwardMode.DECODE:
+            positions = ((seq_lens - 1) + position_ids_offsets).to(torch.int64)
+            extend_seq_lens = extend_start_loc = extend_no_prefix = None
+            if not model_runner.server_args.disable_flashinfer:
+                # This variable is not needed in this case,
+                # we do not compute it to make it compatbile with cuda graph.
+                total_num_tokens = None
+            else:
+                total_num_tokens = int(torch.sum(seq_lens))
+        else:
+            seq_lens_cpu = seq_lens.cpu().numpy()
+            prefix_lens_cpu = prefix_lens.cpu().numpy()
+            position_ids_offsets_cpu = position_ids_offsets.cpu().numpy()
+            positions = torch.tensor(
+                np.concatenate(
+                    [
+                        np.arange(
+                            prefix_lens_cpu[i] + position_ids_offsets_cpu[i],
+                            seq_lens_cpu[i] + position_ids_offsets_cpu[i],
+                        )
+                        for i in range(batch_size)
+                    ],
+                    axis=0,
+                ),
+                device="cuda",
+            )
+            extend_seq_lens = seq_lens - prefix_lens
+            extend_start_loc = torch.zeros_like(seq_lens)
+            extend_start_loc[1:] = torch.cumsum(extend_seq_lens[:-1], dim=0)
+            extend_no_prefix = torch.all(prefix_lens == 0)
+            total_num_tokens = int(torch.sum(seq_lens))
+
+        ret = cls(
+            forward_mode=forward_mode,
+            batch_size=batch_size,
+            total_num_tokens=total_num_tokens,
+            req_pool_indices=req_pool_indices,
+            seq_lens=seq_lens,
+            positions=positions,
+            req_to_token_pool=model_runner.req_to_token_pool,
+            token_to_kv_pool=model_runner.token_to_kv_pool,
+            out_cache_loc=out_cache_loc,
+            extend_seq_lens=extend_seq_lens,
+            extend_start_loc=extend_start_loc,
+            extend_no_prefix=extend_no_prefix,
+            return_logprob=return_logprob,
+            top_logprobs_nums=top_logprobs_nums,
+            flashinfer_prefill_wrapper_ragged=model_runner.flashinfer_prefill_wrapper_ragged,
+            flashinfer_prefill_wrapper_paged=model_runner.flashinfer_prefill_wrapper_paged,
+            flashinfer_decode_wrapper=model_runner.flashinfer_decode_wrapper,
+            flashinfer_use_ragged=flashinfer_use_ragged,
+        )
+
+        if model_runner.server_args.disable_flashinfer:
+            (
+                ret.triton_max_seq_len,
+                ret.triton_max_extend_len,
+                ret.triton_start_loc,
+                ret.triton_prefix_lens,
+            ) = init_triton_args(forward_mode, seq_lens, prefix_lens)
+
+        return ret
+
+
+def init_flashinfer_args(
+    forward_mode,
+    model_runner,
+    req_pool_indices,
+    seq_lens,
+    prefix_lens,
+    flashinfer_decode_wrapper,
+    flashinfer_use_ragged=False,
+):
+    """Init auxiliary variables for FlashInfer attention backend."""
+    num_qo_heads = model_runner.model_config.num_attention_heads // model_runner.tp_size
+    num_kv_heads = model_runner.model_config.get_num_kv_heads(model_runner.tp_size)
+    head_dim = model_runner.model_config.head_dim
+    batch_size = len(req_pool_indices)
+    total_num_tokens = int(torch.sum(seq_lens))
+
+    if flashinfer_use_ragged:
+        paged_kernel_lens = prefix_lens
+    else:
+        paged_kernel_lens = seq_lens
+
+    kv_indptr = torch.zeros((batch_size + 1,), dtype=torch.int32, device="cuda")
+    kv_indptr[1:] = torch.cumsum(paged_kernel_lens, dim=0)
+    req_pool_indices_cpu = req_pool_indices.cpu().numpy()
+    paged_kernel_lens_cpu = paged_kernel_lens.cpu().numpy()
+    kv_indices = torch.cat(
+        [
+            model_runner.req_to_token_pool.req_to_token[
+                req_pool_indices_cpu[i], : paged_kernel_lens_cpu[i]
+            ]
+            for i in range(batch_size)
+        ],
+        dim=0,
+    ).contiguous()
+    kv_last_page_len = torch.ones((batch_size,), dtype=torch.int32, device="cuda")
+
+    if forward_mode == ForwardMode.DECODE:
+        flashinfer_decode_wrapper.end_forward()
+        flashinfer_decode_wrapper.begin_forward(
+            kv_indptr,
+            kv_indices,
+            kv_last_page_len,
+            num_qo_heads,
+            num_kv_heads,
+            head_dim,
+            1,
+        )
+    else:
+        # extend part
+        qo_indptr = torch.zeros((batch_size + 1,), dtype=torch.int32, device="cuda")
+        qo_indptr[1:] = torch.cumsum(seq_lens - prefix_lens, dim=0)
+
+        if flashinfer_use_ragged:
+            model_runner.flashinfer_prefill_wrapper_ragged.end_forward()
+            model_runner.flashinfer_prefill_wrapper_ragged.begin_forward(
+                qo_indptr,
+                qo_indptr,
+                num_qo_heads,
+                num_kv_heads,
+                head_dim,
+            )
+
+        # cached part
+        model_runner.flashinfer_prefill_wrapper_paged.end_forward()
+        model_runner.flashinfer_prefill_wrapper_paged.begin_forward(
+            qo_indptr,
+            kv_indptr,
+            kv_indices,
+            kv_last_page_len,
+            num_qo_heads,
+            num_kv_heads,
+            head_dim,
+            1,
+        )
+
+
+def init_triton_args(forward_mode, seq_lens, prefix_lens):
+    """Init auxiliary variables for triton attention backend."""
+    batch_size = len(seq_lens)
+    max_seq_len = int(torch.max(seq_lens))
+    start_loc = torch.zeros((batch_size,), dtype=torch.int32, device="cuda")
+    start_loc[1:] = torch.cumsum(seq_lens[:-1], dim=0)
+
+    if forward_mode == ForwardMode.DECODE:
+        max_extend_len = None
+    else:
+        extend_seq_lens = seq_lens - prefix_lens
+        max_extend_len = int(torch.max(extend_seq_lens))
+
+    return max_seq_len, max_extend_len, start_loc, prefix_lens

python/sglang/srt/model_executor/model_runner.py

@@ -41,18 +41,14 @@ from vllm.distributed import (
 from vllm.model_executor.models import ModelRegistry
 
 from sglang.global_config import global_config
-from sglang.srt.managers.schedule_batch import (
-    Batch,
-    ForwardMode,
-    InputMetadata,
-    global_server_args_dict,
-)
+from sglang.srt.managers.schedule_batch import ScheduleBatch, global_server_args_dict
 from sglang.srt.mem_cache.memory_pool import (
     MHATokenToKVPool,
     MLATokenToKVPool,
     ReqToTokenPool,
 )
 from sglang.srt.model_config import AttentionArch
+from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
 from sglang.srt.server_args import ServerArgs
 from sglang.srt.utils import (
     get_available_gpu_memory,
@@ -350,7 +346,7 @@ class ModelRunner:
             )
 
     @torch.inference_mode()
-    def forward_decode(self, batch: Batch):
+    def forward_decode(self, batch: ScheduleBatch):
         if self.cuda_graph_runner and self.cuda_graph_runner.can_run(len(batch.reqs)):
             return self.cuda_graph_runner.replay(batch)
 
@@ -370,7 +366,7 @@ class ModelRunner:
         )
 
     @torch.inference_mode()
-    def forward_extend(self, batch: Batch):
+    def forward_extend(self, batch: ScheduleBatch):
         input_metadata = InputMetadata.create(
             self,
             forward_mode=ForwardMode.EXTEND,
@@ -387,7 +383,7 @@ class ModelRunner:
         )
 
     @torch.inference_mode()
-    def forward_extend_multi_modal(self, batch: Batch):
+    def forward_extend_multi_modal(self, batch: ScheduleBatch):
         input_metadata = InputMetadata.create(
             self,
             forward_mode=ForwardMode.EXTEND,
@@ -408,7 +404,7 @@ class ModelRunner:
             batch.image_offsets,
         )
 
-    def forward(self, batch: Batch, forward_mode: ForwardMode):
+    def forward(self, batch: ScheduleBatch, forward_mode: ForwardMode):
         if self.is_multimodal_model and forward_mode == ForwardMode.EXTEND:
             return self.forward_extend_multi_modal(batch)
         elif forward_mode == ForwardMode.DECODE: