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Organize code (rename, movement) (#953)

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This commit is contained in:
Liangsheng Yin
2024-08-06 20:50:32 -07:00
committed by GitHub
parent ad56e68495
commit 87e8c090e9
29 changed files with 304 additions and 289 deletions
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5
python/sglang/bench_latency.py
View File

@@ -50,8 +50,9 @@ import torch
import torch.distributed as dist
from sglang.srt.hf_transformers_utils import get_tokenizer
from sglang.srt.managers.schedule_batch import Batch, ForwardMode, Req
from sglang.srt.managers.schedule_batch import Req, ScheduleBatch
from sglang.srt.model_config import ModelConfig
from sglang.srt.model_executor.forward_batch_info import ForwardMode
from sglang.srt.model_executor.model_runner import ModelRunner
from sglang.srt.sampling_params import SamplingParams
from sglang.srt.server_args import ServerArgs
@@ -188,7 +189,7 @@ def prepare_synthetic_inputs_for_latency_test(batch_size, input_len):
def extend(reqs, model_runner):
batch = Batch.init_new(
batch = ScheduleBatch.init_new(
reqs=reqs,
req_to_token_pool=model_runner.req_to_token_pool,
token_to_kv_pool=model_runner.token_to_kv_pool,

2
python/sglang/srt/layers/logits_processor.py
View File

@@ -25,7 +25,7 @@ from vllm.distributed import (
tensor_model_parallel_all_gather,
)
from sglang.srt.model_executor.model_runner import ForwardMode, InputMetadata
from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
@dataclasses.dataclass

7
python/sglang/srt/layers/radix_attention.py
View File

@@ -22,11 +22,8 @@ from torch import nn
from sglang.global_config import global_config
from sglang.srt.layers.extend_attention import extend_attention_fwd
from sglang.srt.layers.token_attention import token_attention_fwd
from sglang.srt.model_executor.model_runner import (
ForwardMode,
InputMetadata,
global_server_args_dict,
)
from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
from sglang.srt.model_executor.model_runner import global_server_args_dict
class RadixAttention(nn.Module):

237
python/sglang/srt/managers/schedule_batch.py
View File

@@ -18,7 +18,6 @@ limitations under the License.
import logging
import warnings
from dataclasses import dataclass
from enum import IntEnum, auto
from typing import List, Union
import numpy as np
@@ -46,15 +45,6 @@ global_server_args_dict = {
logger = logging.getLogger(__name__)
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()
class BaseFinishReason:
def __init__(self, is_error: bool = False):
self.is_error = is_error
@@ -284,7 +274,7 @@ class Req:
@dataclass
class Batch:
class ScheduleBatch:
"""Store all inforamtion of a batch."""
# Request, memory pool, and cache
@@ -673,7 +663,7 @@ class Batch:
if self_val is not None: # logit_bias can be None
setattr(self, item, self_val[new_indices])
def merge(self, other: "Batch"):
def merge(self, other: "ScheduleBatch"):
self.reqs.extend(other.reqs)
self.req_pool_indices = torch.concat(
@@ -770,229 +760,6 @@ class Batch:
return batch_next_token_ids
@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
def top_k_top_p_sampling_from_probs_torch(
probs: torch.Tensor, top_ks: torch.Tensor, top_ps: torch.Tensor
):

20
python/sglang/srt/managers/tp_worker.py
View File

@@ -39,13 +39,13 @@ from sglang.srt.managers.policy_scheduler import PolicyScheduler
from sglang.srt.managers.schedule_batch import (
FINISH_ABORT,
BaseFinishReason,
Batch,
ForwardMode,
Req,
ScheduleBatch,
)
from sglang.srt.mem_cache.chunk_cache import ChunkCache
from sglang.srt.mem_cache.radix_cache import RadixCache
from sglang.srt.model_config import ModelConfig
from sglang.srt.model_executor.forward_batch_info import ForwardMode
from sglang.srt.model_executor.model_runner import ModelRunner
from sglang.srt.server_args import ServerArgs
from sglang.srt.utils import (
@@ -172,7 +172,7 @@ class ModelTpServer:
# Init running status
self.waiting_queue: List[Req] = []
self.running_batch: Batch = None
self.running_batch: ScheduleBatch = None
self.out_pyobjs = []
self.decode_forward_ct = 0
self.stream_interval = server_args.stream_interval
@@ -353,7 +353,7 @@ class ModelTpServer:
)
self.waiting_queue.append(req)
def get_new_prefill_batch(self) -> Optional[Batch]:
def get_new_prefill_batch(self) -> Optional[ScheduleBatch]:
# TODO(lsyin): organize this function
running_bs = (
len(self.running_batch.reqs) if self.running_batch is not None else 0
@@ -526,7 +526,7 @@ class ModelTpServer:
)
# Return the new batch
new_batch = Batch.init_new(
new_batch = ScheduleBatch.init_new(
can_run_list,
self.req_to_token_pool,
self.token_to_kv_pool,
@@ -535,7 +535,7 @@ class ModelTpServer:
self.waiting_queue = [x for x in self.waiting_queue if x not in can_run_list]
return new_batch
def forward_prefill_batch(self, batch: Batch):
def forward_prefill_batch(self, batch: ScheduleBatch):
# Build batch tensors
batch.prepare_for_extend(
self.model_config.vocab_size, self.int_token_logit_bias
@@ -624,7 +624,7 @@ class ModelTpServer:
)
req.output_top_logprobs.append(output.output_top_logprobs[i])
def cache_filled_batch(self, batch: Batch):
def cache_filled_batch(self, batch: ScheduleBatch):
req_pool_indices_cpu = batch.req_pool_indices.cpu().numpy()
for i, req in enumerate(batch.reqs):
new_prefix_indices, new_last_node = self.tree_cache.cache_req(
@@ -641,7 +641,7 @@ class ModelTpServer:
# inflight request would get a new req idx
self.req_to_token_pool.free(int(req_pool_indices_cpu[i]))
def forward_decode_batch(self, batch: Batch):
def forward_decode_batch(self, batch: ScheduleBatch):
# Check if decode out of memory
if not batch.check_decode_mem():
old_ratio = self.new_token_ratio
@@ -700,7 +700,7 @@ class ModelTpServer:
self.handle_finished_requests(batch)
def handle_finished_requests(self, batch: Batch):
def handle_finished_requests(self, batch: ScheduleBatch):
output_rids = []
output_vids = []
decoded_texts = []
@@ -800,7 +800,7 @@ class ModelTpServer:
else:
batch.reqs = []
def filter_out_inflight(self, batch: Batch):
def filter_out_inflight(self, batch: ScheduleBatch):
# TODO(lsyin): reduce the overhead, make a special version for this
if self.current_inflight_req is None:
return

6
python/sglang/srt/model_executor/cuda_graph_runner.py
View File

@@ -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)

256
python/sglang/srt/model_executor/forward_batch_info.py Normal file
View File

@@ -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

16
python/sglang/srt/model_executor/model_runner.py
View File

@@ -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:

2
python/sglang/srt/models/chatglm.py
View File

@@ -45,7 +45,7 @@ from vllm.transformers_utils.configs import ChatGLMConfig
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
LoraConfig = None

2
python/sglang/srt/models/commandr.py
View File

@@ -64,7 +64,7 @@ from vllm.model_executor.utils import set_weight_attrs
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
@torch.compile

2
python/sglang/srt/models/dbrx.py
View File

@@ -45,7 +45,7 @@ from vllm.transformers_utils.configs.dbrx import DbrxConfig
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class DbrxRouter(nn.Module):

2
python/sglang/srt/models/deepseek.py
View File

@@ -46,7 +46,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.managers.schedule_batch import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class DeepseekMLP(nn.Module):

2
python/sglang/srt/models/deepseek_v2.py
View File

@@ -46,7 +46,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.managers.schedule_batch import global_server_args_dict
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class DeepseekV2MLP(nn.Module):

2
python/sglang/srt/models/gemma.py
View File

@@ -37,7 +37,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class GemmaMLP(nn.Module):

2
python/sglang/srt/models/gemma2.py
View File

@@ -42,7 +42,7 @@ from vllm.model_executor.sampling_metadata import SamplingMetadata
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class GemmaRMSNorm(CustomOp):

2
python/sglang/srt/models/gpt_bigcode.py
View File

@@ -35,7 +35,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.managers.schedule_batch import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class GPTBigCodeAttention(nn.Module):

2
python/sglang/srt/models/grok.py
View File

@@ -52,7 +52,7 @@ from vllm.utils import print_warning_once
from sglang.srt.layers.fused_moe import fused_moe
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
use_fused = True

2
python/sglang/srt/models/internlm2.py
View File

@@ -40,7 +40,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class InternLM2MLP(nn.Module):

2
python/sglang/srt/models/llama2.py
View File

@@ -41,7 +41,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class LlamaMLP(nn.Module):

2
python/sglang/srt/models/llama_classification.py
View File

@@ -25,7 +25,7 @@ from vllm.model_executor.layers.quantization.base_config import QuantizationConf
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitProcessorOutput
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
from sglang.srt.models.llama2 import LlamaModel

3
python/sglang/srt/models/llava.py
View File

@@ -32,13 +32,12 @@ from vllm.config import CacheConfig
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.managers.schedule_batch import ForwardMode
from sglang.srt.mm_utils import (
get_anyres_image_grid_shape,
unpad_image,
unpad_image_shape,
)
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
from sglang.srt.models.llama2 import LlamaForCausalLM
from sglang.srt.models.mistral import MistralForCausalLM
from sglang.srt.models.qwen2 import Qwen2ForCausalLM

3
python/sglang/srt/models/llavavid.py
View File

@@ -26,13 +26,12 @@ from vllm.config import CacheConfig
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.managers.schedule_batch import ForwardMode
from sglang.srt.mm_utils import (
get_anyres_image_grid_shape,
unpad_image,
unpad_image_shape,
)
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import ForwardMode, InputMetadata
from sglang.srt.models.llama2 import LlamaForCausalLM

2
python/sglang/srt/models/minicpm.py
View File

@@ -39,7 +39,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class MiniCPMMLP(nn.Module):

2
python/sglang/srt/models/mixtral.py
View File

@@ -50,7 +50,7 @@ from vllm.utils import print_warning_once
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class MixtralMoE(nn.Module):

2
python/sglang/srt/models/mixtral_quant.py
View File

@@ -45,7 +45,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class MixtralMLP(nn.Module):

2
python/sglang/srt/models/qwen.py
View File

@@ -39,7 +39,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class QWenMLP(nn.Module):

2
python/sglang/srt/models/qwen2.py
View File

@@ -39,7 +39,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
Qwen2Config = None

2
python/sglang/srt/models/qwen2_moe.py
View File

@@ -51,7 +51,7 @@ from vllm.sequence import IntermediateTensors, SamplerOutput
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class Qwen2MoeMLP(nn.Module):

2
python/sglang/srt/models/stablelm.py
View File

@@ -40,7 +40,7 @@ from vllm.model_executor.model_loader.weight_utils import default_weight_loader
from sglang.srt.layers.logits_processor import LogitsProcessor
from sglang.srt.layers.radix_attention import RadixAttention
from sglang.srt.model_executor.model_runner import InputMetadata
from sglang.srt.model_executor.forward_batch_info import InputMetadata
class StablelmMLP(nn.Module):