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values | quality_score float32 0.07 1 | comment_type stringclasses 9
values | comment_length int32 16 64.5k | before_lines int32 1 17.2k | after_lines int32 1 12.1k | is_negative bool 2
classes | pr_title stringlengths 1 308 | pr_number int32 1 299k | repo_name stringclasses 533
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values | reviewer_username stringlengths 0 39 | author_username stringlengths 2 39 |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
epochs=self.config.ppo_epochs,
seed=self.config.data_loader_seed,
dataloader_kwargs={"shuffle": self.config.shuffle},
)
def forward_backward_batch(
self,
data: DataProto,
forward_only=False,
post_process_fn=None,
calculate_entropy=... | No issues found. | epochs=self.config.ppo_epochs,
seed=self.config.data_loader_seed,
dataloader_kwargs={"shuffle": self.config.shuffle},
)
def forward_backward_batch(
self,
data: DataProto,
forward_only=False,
post_process_fn=None,
calculate_entropy=... | @@ -130,7 +130,8 @@ def __init__(
else:
self.prof = None
self.use_fused_kernels = self.config.get("use_fused_kernels", False)
- if self.use_fused_kernels:
+ if self.use_fused_kernels and not getattr(self.config, "overlap_moe_expert_parallel_comm", False):
+ # do n... | verl/workers/actor/megatron_actor.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [Megatron] feat: 1f1b overlap/moe_a2a_overlap | 3,522 | volcengine/verl | 16,907 | Python | ISEEKYAN | |
# Algorithm
temperature=1.0
top_p=1.0
top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
val_top_p=0.7
# Performance Related Parameter
use_dynamic_bsz=True
actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2))
infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3))
ref_offload=T... | No issues found. |
# Algorithm
temperature=1.0
top_p=1.0
top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
val_top_p=0.7
# Performance Related Parameter
use_dynamic_bsz=True
actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2))
infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 3))
ref_offload=T... | @@ -0,0 +1,140 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+project_name='DAPO'
+exp_name='DAPO-Qwen2.5-7b-MATH-0527a1-fsdp2-sglang-one-step-off-4-12'
+
+adv_estimator=grpo
+
+use_kl_in_reward=False
+kl_coef=0.0
+use_kl_loss=False
+kl_loss_coef=0.0
+
+clip_ratio_low=0.2
+clip_ratio_high=0.28
+
+max_prompt_length=$((1... | recipe/one_step_off_policy/dapo_7b_math_fsdp2_sglang_4_12.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [sglang, recipe] feat: add SGLang as rollout engine for one-step-off-policy | 3,531 | volcengine/verl | 16,907 | Python | KAMiPan | |
AssertionError: If equal_size is True and len(seqlen_list) is not divisible by k_partitions.
AssertionError: If any resulting partition is empty.
"""
assert len(seqlen_list) >= k_partitions, f"number of items:[{len(seqlen_list)}] < k_partitions:[{k_partitions}]"
def _check_and_sort_partitio... | No issues found. | AssertionError: If equal_size is True and len(seqlen_list) is not divisible by k_partitions.
AssertionError: If any resulting partition is empty.
"""
assert len(seqlen_list) >= k_partitions, f"number of items:[{len(seqlen_list)}] < k_partitions:[{k_partitions}]"
def _check_and_sort_partitio... | @@ -24,6 +24,16 @@
from verl.utils.device import get_device_name
+def calculate_workload(seqlen_list: list[int]):
+ """
+ Calculate the workload for a dense transformer block based on sequence length.
+ FLOPs = 12 * hidden_size^2 * seqlen + 2 * hidden_size * seqlen^2
+ Hardcodes the constants by a 7B m... | verl/utils/seqlen_balancing.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [perf, data] feat: DP workload balance | 3,605 | volcengine/verl | 16,907 | Python | conver334 | |
if not os.path.exists(file_path):
raise FileNotFoundError(f"Reward function file '{file_path}' not found.")
spec = importlib.util.spec_from_file_location("custom_module", file_path)
assert spec is not None
module = importlib.util.module_from_spec(spec)
try:
... | No issues found. | if not os.path.exists(file_path):
raise FileNotFoundError(f"Reward function file '{file_path}' not found.")
spec = importlib.util.spec_from_file_location("custom_module", file_path)
assert spec is not None
module = importlib.util.module_from_spec(spec)
try:
... | @@ -109,6 +109,11 @@ def load_reward_manager(
An instance of the specified reward manager class.
"""
+ # Try to get a custom reward function based on the configuration
+ # user defined reward manager can be registered in custom_reward_fn
+ compute_score = get_custom_reward_fn(config)
+ final... | verl/trainer/ppo/reward.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [recipe] fix: move all collabllm files into recipe directory | 3,706 | volcengine/verl | 16,907 | Python | chenhaiq | |
critic_lr=2e-6
gae_gamma=1.0
gae_lam=1.0
critic_warmup=20
train_batch_size=512
ppo_mini_batch_size=512
n_resp_per_prompt_val=30
# ================= perfomance =================
infer_tp=4 # vllm
train_sp=4 # train
offload=True
actor_max_token_len_per_gpu=$(( (max_prompt_length + max_response_length) * 2 ))
critic_... | No issues found. | critic_lr=2e-6
gae_gamma=1.0
gae_lam=1.0
critic_warmup=20
train_batch_size=512
ppo_mini_batch_size=512
n_resp_per_prompt_val=30
# ================= perfomance =================
infer_tp=4 # vllm
train_sp=4 # train
offload=True
actor_max_token_len_per_gpu=$(( (max_prompt_length + max_response_length) * 2 ))
critic_... | @@ -0,0 +1,125 @@
+set -x
+
+# ================= data/model/tool =================
+HDFS_ROOT=${HDFS_ROOT:-$PWD}
+DATA_ROOT=${DATA_ROOT:-$PWD}
+
+dapo_math_17k=$DATA_ROOT/dataset/BytedTsinghua-SIA/DAPO-Math-17k
+aime_2024=$DATA_ROOT/dataset/Maxwell-Jia/AIME_2024
+aime_2025=$DATA_ROOT/dataset/yentinglin/aime_2025
+actor... | recipe/retool/run_gpt_oss_ppo.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [recipe, rollout] feat: enable gpt-oss training for tool agent add gpt-oss for retool recipe | 3,837 | volcengine/verl | 16,907 | Python | HJSang | |
# clear kv cache
get_torch_device().empty_cache()
return output
@register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor"))
@DistProfiler.annotate(color="blue", role="actor_compute_log_prob")
def compute_log_prob(self, data: DataProto):
# when is_lora i... | No issues found. | # clear kv cache
get_torch_device().empty_cache()
return output
@register(dispatch_mode=make_nd_compute_dataproto_dispatch_fn(mesh_name="actor"))
@DistProfiler.annotate(color="blue", role="actor_compute_log_prob")
def compute_log_prob(self, data: DataProto):
# when is_lora i... | @@ -15,7 +15,6 @@
The main entry point to run the PPO algorithm
"""
-import asyncio
import datetime
import json
import logging
@@ -85,6 +84,7 @@
from verl.utils.profiler import DistProfiler, DistProfilerExtension, ProfilerConfig, log_gpu_memory_usage, simple_timer
from verl.utils.profiler.performance import re... | verl/workers/fsdp_workers.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [worker] fix: create a new event loop if none exists | 3,839 | volcengine/verl | 16,907 | Python | baymax591 | |
train_path=$HOME/data/geo3k/train.parquet
test_path=$HOME/data/geo3k/test.parquet
python3 -m verl.trainer.main_ppo --config-path=config \
--config-name='ppo_megatron_trainer.yaml'\
algorithm.adv_estimator=grpo \
data.train_files="$train_path" \
data.val_files="$test_path" \
data.train_batch_size=51... | No issues found. | train_path=$HOME/data/geo3k/train.parquet
test_path=$HOME/data/geo3k/test.parquet
python3 -m verl.trainer.main_ppo --config-path=config \
--config-name='ppo_megatron_trainer.yaml'\
algorithm.adv_estimator=grpo \
data.train_files="$train_path" \
data.val_files="$test_path" \
data.train_batch_size=51... | @@ -2,15 +2,6 @@ set -x
ENGINE=${1:-vllm}
export CUDA_DEVICE_MAX_CONNECTIONS=1 # For megatron communication/computation overlapping
-
-# dependency: vllm>=0.11.0, megatron-lm>=0.13, mbridge with qwen3vl_cp branch
-# environment option1: use a stable container later than docker://verlai/verl:vllm011.dev6
- # and... | examples/grpo_trainer/run_qwen3_vl-30b-megatron.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [env] feat: update docker file building schema, from VLLM base images | 3,937 | volcengine/verl | 16,907 | Python | ISEEKYAN | |
output_lst = []
ctx = torch.no_grad() if forward_only else nullcontext()
for micro_batch in micro_batches:
with ctx:
loss, meta_info = self.forward_step(micro_batch, loss_function=loss_function, forward_only=forward_only)
if not forward_only:
... | No issues found. |
output_lst = []
ctx = torch.no_grad() if forward_only else nullcontext()
for micro_batch in micro_batches:
with ctx:
loss, meta_info = self.forward_step(micro_batch, loss_function=loss_function, forward_only=forward_only)
if not forward_only:
... | @@ -476,6 +476,14 @@ def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forw
# note that the global_batch_size should include data on all the dp
tu.assign_non_tensor(data, sp_size=self.ulysses_sequence_parallel_size)
+ # compute num_tokens in global batch for loss nor... | verl/workers/engine/fsdp/transformer_impl.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [trainer] fix: normalize sft loss by num_tokens in global batch | 3,994 | volcengine/verl | 16,907 | Python | wuxibin89 | |
2. Try current working directory
3. Try relative to verl package installation (project root)
Args:
config_path: Configuration file path (relative or absolute)
Returns:
Absolute path to the configuration file
Raises:
FileNotFoundError: If the configuration file cannot be fo... | No issues found. | 2. Try current working directory
3. Try relative to verl package installation (project root)
Args:
config_path: Configuration file path (relative or absolute)
Returns:
Absolute path to the configuration file
Raises:
FileNotFoundError: If the configuration file cannot be fo... | @@ -12,6 +12,65 @@
# See the License for the specific language governing permissions and
# limitations under the License.
+import os
+
+
+def resolve_config_path(config_path: str) -> str:
+ """Resolve agent loop configuration file path.
+
+ In multi-node Ray training, relative paths may not resolve correctly
... | verl/experimental/agent_loop/utils.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [rollout] fix: resolve agent loop config path in multi-node Ray training | 4,029 | volcengine/verl | 16,907 | Python | CzsGit | |
@GPUMemoryLogger(role="dp actor", logger=logger)
def update_policy(self, data: DataProto):
# make sure we are in training mode
self.actor_module.train()
temperature = data.meta_info["temperature"] # temperature must be in the data.meta_info to avoid silent error
select_keys = ... | No issues found. | @GPUMemoryLogger(role="dp actor", logger=logger)
def update_policy(self, data: DataProto):
# make sure we are in training mode
self.actor_module.train()
temperature = data.meta_info["temperature"] # temperature must be in the data.meta_info to avoid silent error
select_keys = ... | @@ -295,13 +295,13 @@ def update_policy(self, data: DataProto):
]
if self.config.use_kl_loss:
select_keys.append("ref_log_prob")
- # if self.config.tis_imp_ratio_cap > 0:
- # assert "rollout_log_probs" in data.batch.keys(), (
- # "Truncated Importance Samp... | recipe/flowrl/flowrl_actor.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [worker, trainer, recipe] feat: add FP16 training and inference support | 4,036 | volcengine/verl | 16,907 | Python | Xuekai-Zhu | |
TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
# Algorithm
temperature=1.0
top_p=1.0
top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
val_top_p=0.7
# Performance Related Parameter
use_dynamic_bsz=True
actor_ppo_max_token_len=$(... | No issues found. | TRAIN_FILE=${TRAIN_FILE:-"${RAY_DATA_HOME}/data/dapo-math-17k.parquet"}
TEST_FILE=${TEST_FILE:-"${RAY_DATA_HOME}/data/aime-2024.parquet"}
# Algorithm
temperature=1.0
top_p=1.0
top_k=-1 # 0 for HF rollout, -1 for vLLM rollout
val_top_p=0.7
# Performance Related Parameter
use_dynamic_bsz=True
actor_ppo_max_token_len=$(... | @@ -0,0 +1,142 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+
+rollout_mode="async"
+rollout_name="vllm" # sglang or vllm
+if [ "$rollout_mode" = "async" ]; then
+ export VLLM_USE_V1=1
+ return_raw_chat="True"
+fi
+dtype="float16" # ["bfloat16", "float16"]
+
+project_name='DAPO-fp16'
+exp_name='fp16'
+
+adv_esti... | recipe/dapo/test_dapo_8b_megatron_fp16.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [megatron] feat: fp16 training (dense and MoE supported) | 4,086 | volcengine/verl | 16,907 | Python | ISEEKYAN | |
Update Prometheus configuration file with server addresses and reload on first node.
server_addresses: vllm or sglang server addresses
"""
if not server_addresses:
logger.warning("No server addresses available to update Prometheus config")
return
try:
# Get Prometheus conf... | No issues found. | Update Prometheus configuration file with server addresses and reload on first node.
server_addresses: vllm or sglang server addresses
"""
if not server_addresses:
logger.warning("No server addresses available to update Prometheus config")
return
try:
# Get Prometheus conf... | @@ -0,0 +1,107 @@
+# Copyright 2025 Meituan Ltd. and/or its affiliates
+#
+# 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 re... | verl/experimental/agent_loop/prometheus_utils.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [sglang,vllm] feat: use prometheus and grafana to show rollout message | 4,088 | volcengine/verl | 16,907 | Python | ArronHZG | |
@ray.remote(num_cpus=1) # please make sure main_task is not scheduled on head
class TaskRunner:
def run(self, config):
# print initial config
from pprint import pprint
from omegaconf import OmegaConf
from verl.utils.fs import copy_to_local
print(f"TaskRunner hostname: {so... | No issues found. | @ray.remote(num_cpus=1) # please make sure main_task is not scheduled on head
class TaskRunner:
def run(self, config):
# print initial config
from pprint import pprint
from omegaconf import OmegaConf
from verl.utils.fs import copy_to_local
print(f"TaskRunner hostname: {so... | @@ -97,13 +97,13 @@ def run(self, config):
if config.actor_rollout_ref.actor.strategy in {"fsdp", "fsdp2"}:
assert config.critic.strategy in {"fsdp", "fsdp2"}
- from verl.workers.fsdp_workers import ActorRolloutRefWorker, CriticWorker
+ from verl.workers.fsdp_workers import... | recipe/dapo/main_dapo.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [BREAKING][rollout] feat: change rollout to server mode by default | 4,106 | volcengine/verl | 16,907 | Python | wuxibin89 | |
raise NotImplementedError(f"{sequence_length=} is larger than {max_length=}")
else:
raise NotImplementedError(f"Unknown truncation method {truncation}")
return input_ids, attention_mask
def tokenize_and_postprocess_data(
prompt: str, tokenizer: PreTrainedTokenizer, max_length:... | No issues found. | raise NotImplementedError(f"{sequence_length=} is larger than {max_length=}")
else:
raise NotImplementedError(f"Unknown truncation method {truncation}")
return input_ids, attention_mask
def tokenize_and_postprocess_data(
prompt: str, tokenizer: PreTrainedTokenizer, max_length:... | @@ -775,3 +775,27 @@ def distributed_masked_mean(local_tensor, local_mask):
global_mean = local_sum / local_num
return global_mean
+
+
+@contextmanager
+def use_original_torch_compile():
+ """torch.compile might be replaced by mindspeed on NPU, this contextmanager
+ can revert torch.compile temporaril... | verl/utils/torch_functional.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [vllm] fix: Fix issues that occur during the ACLGraph initialization process in the NPU. | 4,209 | volcengine/verl | 16,907 | Python | chengminhua | |
def _initialize_llm_servers(self):
rollout_world_size = self.config.rollout.tensor_model_parallel_size
world_size = (
self.resource_pool.world_size
if self.resource_pool # colocate mode
else self.config.n_gpus_per_node * self.config.nnodes # standalone mode
... | No issues found. | def _initialize_llm_servers(self):
rollout_world_size = self.config.rollout.tensor_model_parallel_size
world_size = (
self.resource_pool.world_size
if self.resource_pool # colocate mode
else self.config.n_gpus_per_node * self.config.nnodes # standalone mode
... | @@ -75,7 +75,14 @@ def _initialize_llm_servers(self):
for replica_rank in range(num_replicas)
]
if self.resource_pool:
- self._run_all([server.init_colocated(self.resource_pool) for server in self.rollout_replicas])
+ split_resource_pool = self.resource_pool.split(sp... | verl/experimental/reward/reward_model.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [single_controller] feat: support resource pool split method | 4,251 | volcengine/verl | 16,907 | Python | yyDing1 | |
load_megatron_optimizer(self.actor_optimizer)
log_gpu_memory_usage("After load actor optimizer during update_actor", logger=logger)
micro_batch_size = self.config.actor.ppo_micro_batch_size_per_gpu
data.meta_info["micro_batch_size"] = micro_batch_size
dataloader = self.a... | No issues found. | load_megatron_optimizer(self.actor_optimizer)
log_gpu_memory_usage("After load actor optimizer during update_actor", logger=logger)
micro_batch_size = self.config.actor.ppo_micro_batch_size_per_gpu
data.meta_info["micro_batch_size"] = micro_batch_size
dataloader = self.a... | @@ -806,6 +806,12 @@ def save_checkpoint(self, checkpoint_path, hdfs_path=None, global_step=0, max_ck
if self._is_offload_param:
offload_megatron_model_to_cpu(self.actor_module)
+ @register(dispatch_mode=Dispatch.ONE_TO_ALL)
+ def async_calls_finalize_fn_exec(self, blocking=False):
+ ... | verl/workers/megatron_workers.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] fix: megatron async save ckpt fix | 4,253 | volcengine/verl | 16,907 | Python | Leem-Li | |
assert unpadd_data["info"] == "test_info"
padded_data, pad_size = tu.pad_to_divisor(data, size_divisor=7)
assert pad_size == 4
expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6], [1, 2]])
expected_labels = ["a", "b", "c", "a", "b", "c", "a"]
assert torch.all(torch.eq(p... | No issues found. | assert unpadd_data["info"] == "test_info"
padded_data, pad_size = tu.pad_to_divisor(data, size_divisor=7)
assert pad_size == 4
expected_obs = torch.tensor([[1, 2], [3, 4], [5, 6], [1, 2], [3, 4], [5, 6], [1, 2]])
expected_labels = ["a", "b", "c", "a", "b", "c", "a"]
assert torch.all(torch.eq(p... | @@ -22,6 +22,7 @@
import numpy as np
import pytest
import torch
+from tensordict.tensorclass import NonTensorData, NonTensorStack
from verl.utils import tensordict_utils as tu
@@ -45,10 +46,10 @@ def test_union_tensor_dict():
# conflict in tensor values
tu.union_tensor_dict(data1, data_with_co... | tests/test_protocol_v2_on_cpu.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [misc] fix: fix list conversion in get_tensordict | 4,304 | volcengine/verl | 16,907 | Python | PeterSH6 | |
inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
if pixel_values_videos is not None:
pixel_values_videos = pixel_values_videos.type(model.visual.dtype)
video_embeds, deepstack_video_embeds = model.visual(pixel_values_videos, grid_thw=video_grid_thw)
n_video_tok... | No issues found. | inputs_embeds = inputs_embeds.masked_scatter(image_mask, image_embeds)
if pixel_values_videos is not None:
pixel_values_videos = pixel_values_videos.type(model.visual.dtype)
video_embeds, deepstack_video_embeds = model.visual(pixel_values_videos, grid_thw=video_grid_thw)
n_video_tok... | @@ -12,6 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
+import functools
import logging
import os
from dataclasses import dataclass
@@ -334,3 +335,41 @@ def forward_with_triton_backend(
entropy=entropy,
hidden_states=outputs.hidd... | verl/models/transformers/qwen3_vl.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [fsdp] fix: qwen3vlmoe with Monkey patch to fix a bug in transformers 4.57.x | 4,402 | volcengine/verl | 16,907 | Python | pengyanai | |
2. Worker groups for each role (actor, critic, etc.)
"""
self.resource_pool_manager.create_resource_pool()
self.resource_pool_to_cls = {pool: {} for pool in self.resource_pool_manager.resource_pool_dict.values()}
# create actor and rollout
if self.hybrid_engine:
... | No issues found. | 2. Worker groups for each role (actor, critic, etc.)
"""
self.resource_pool_manager.create_resource_pool()
self.resource_pool_to_cls = {pool: {} for pool in self.resource_pool_manager.resource_pool_dict.values()}
# create actor and rollout
if self.hybrid_engine:
... | @@ -1315,15 +1315,10 @@ def fit(self):
batch_dict, repeat_times=self.config.actor_rollout_ref.rollout.n, interleave=True
)
batch: TensorDict = self.dict_to_tensordict(repeated_batch_dict)
- asyncio.run(self.tq_client.async_put(data=batch, partition_i... | recipe/transfer_queue/ray_trainer.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [data] feat: TransferQueue - Add zero-copy serialization support & usage improvement | 4,429 | volcengine/verl | 16,907 | Python | 0oshowero0 | |
return dx, dw, None, None
def qwen3_moe_sparse_moe_block_forward_npu(self, hidden_states: torch.Tensor) -> torch.Tensor:
"""NPU optimized implementation for `forward` in Qwen3MoeSparseMoeBlock."""
# hidden_states: (batch_size, sequence_length, hidden_size)
hidden_dim = hidden_states.shape[... | No issues found. |
return dx, dw, None, None
def qwen3_moe_sparse_moe_block_forward_npu(self, hidden_states: torch.Tensor) -> torch.Tensor:
"""NPU optimized implementation for `forward` in Qwen3MoeSparseMoeBlock."""
# hidden_states: (batch_size, sequence_length, hidden_size)
hidden_dim = hidden_states.shape[... | @@ -239,6 +239,7 @@ def forward(self, hidden_states: torch.Tensor) -> torch.Tensor:
# Patches for Qwen3 Model
modeling_qwen3.Qwen3RMSNorm.forward = rms_norm_forward_npu
modeling_qwen3.Qwen3MLP.forward = silu_forward_npu
+modeling_qwen3.apply_rotary_pos_emb = apply_rotary_pos_emb_npu
# Patches for Qwen3 MoE M... | verl/models/transformers/npu_patch.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [model] feat: add qwen3-4b grpo script on ASCEND NPU A3 | 4,432 | volcengine/verl | 16,907 | Python | 5082459 | |
rollout_config = config.actor_rollout_ref.rollout
model_config = config.actor_rollout_ref.model
rollout_server_class = get_rollout_replica_class(ROLLOUT_NAME)
server = rollout_server_class(
replica_rank=0,
config=rollout_config,
model_config=model_co... | No issues found. |
rollout_config = config.actor_rollout_ref.rollout
model_config = config.actor_rollout_ref.model
rollout_server_class = get_rollout_replica_class(ROLLOUT_NAME)
server = rollout_server_class(
replica_rank=0,
config=rollout_config,
model_config=model_co... | @@ -0,0 +1,217 @@
+# Copyright 2024 Bytedance Ltd. and/or its affiliates
+#
+# 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 ... | tests/workers/rollout/rollout_vllm/test_vllm_abort.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [vllm] feat: support abort generating requests in vllm server | 4,453 | volcengine/verl | 16,907 | Python | PeterSH6 | |
wg.init_model()
batch_size = 8
seqlen = 32
response_length = seqlen // 2
torch.manual_seed(1)
np.random.seed(1)
input_ids = torch.randint(0, model_config.hf_config.vocab_size, (batch_size, seqlen))
attention_mask = create_random_mask(
input_ids=input_ids, max_ratio_of_valid_t... | No issues found. | wg.init_model()
batch_size = 8
seqlen = 32
response_length = seqlen // 2
torch.manual_seed(1)
np.random.seed(1)
input_ids = torch.randint(0, model_config.hf_config.vocab_size, (batch_size, seqlen))
attention_mask = create_random_mask(
input_ids=input_ids, max_ratio_of_valid_t... | @@ -36,6 +36,7 @@
from verl import DataProto
from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayWorkerGroup
from verl.trainer.config import CheckpointConfig
+from verl.utils import tensordict_utils as tu
from verl.utils.model import compute_position_id_with_mask, create_random_mask
fr... | tests/models/test_engine.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [model] feat: enhances TrainingWorker | 4,461 | volcengine/verl | 16,907 | Python | vermouth1992 | |
LEVEL="level0"
CONTENTS=['npu','cpu']
ANALYSIS=True
python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=grpo \
data.train_files=$HOME/data/gsm8k/train.parquet \
data.val_files=$HOME/data/gsm8k/test.parquet \
data.train_batch_size=32 \
data.max_prompt_length=1024 \
data.max_response_lengt... | No issues found. | LEVEL="level0"
CONTENTS=['npu','cpu']
ANALYSIS=True
python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=grpo \
data.train_files=$HOME/data/gsm8k/train.parquet \
data.val_files=$HOME/data/gsm8k/test.parquet \
data.train_batch_size=32 \
data.max_prompt_length=1024 \
data.max_response_lengt... | @@ -8,7 +8,7 @@ PROFILE_RANKS="[1,2]"
# profiling NPU options
SAVE_PATH="$HOME/profile_data"
-LEVEL="level1"
+LEVEL="level0"
CONTENTS=['npu','cpu']
ANALYSIS=True
| examples/grpo_trainer/run_qwen2_5_7b_grpo_discrete_prof_npu.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [perf] fix: modify the NPU profiler default configuration | 4,475 | volcengine/verl | 16,907 | Python | tardis-key | |
is_npu_available = is_torch_npu_available()
def get_visible_devices_keyword() -> str:
"""Function that gets visible devices keyword name.
Returns:
'CUDA_VISIBLE_DEVICES' or `ASCEND_RT_VISIBLE_DEVICES`
"""
return "CUDA_VISIBLE_DEVICES" if is_cuda_available else "ASCEND_RT_VISIBLE_DEVICES"
def... | No issues found. | is_npu_available = is_torch_npu_available()
def get_visible_devices_keyword() -> str:
"""Function that gets visible devices keyword name.
Returns:
'CUDA_VISIBLE_DEVICES' or `ASCEND_RT_VISIBLE_DEVICES`
"""
return "CUDA_VISIBLE_DEVICES" if is_cuda_available else "ASCEND_RT_VISIBLE_DEVICES"
def... | @@ -92,3 +92,15 @@ def set_expandable_segments(enable: bool) -> None:
"""
if is_cuda_available:
torch.cuda.memory._set_allocator_settings(f"expandable_segments:{enable}")
+
+
+def auto_set_ascend_device_name(config):
+ if config and config.trainer and config.trainer.device:
+ if is_torch_np... | verl/utils/device.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [hardware] feat: Auto set device_name to npu for Ascend NPU | 4,489 | volcengine/verl | 16,907 | Python | FightingZhen | |
train_prompt_mini_bsz=32
total_rollout_steps=$(((train_bsz * 400)))
test_freq=25
staleness_threshold=0.6 # 0 0.3 1
require_batches=1
total_train_gpus=$((n_gpus_training * n_nodes_train))
total_rollout_gpus=$((n_gpus_rollout * n_nodes_rollout))
trigger_parameter_sync_step=$((train_bsz / ( train_prompt_mini_bsz * require... | No issues found. | train_prompt_mini_bsz=32
total_rollout_steps=$(((train_bsz * 400)))
test_freq=25
staleness_threshold=0.6 # 0 0.3 1
require_batches=1
total_train_gpus=$((n_gpus_training * n_nodes_train))
total_rollout_gpus=$((n_gpus_rollout * n_nodes_rollout))
trigger_parameter_sync_step=$((train_bsz / ( train_prompt_mini_bsz * require... | @@ -0,0 +1,191 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+project_name='DAPO-Qwen3-30B-A3B-Base-Async'
+exp_name='Fsdp2-tp4sp4'
+
+# Ray
+RAY_ADDRESS=${RAY_ADDRESS:-"http://localhost:8265"}
+WORKING_DIR=${WORKING_DIR:-"${PWD}"}
+RUNTIME_ENV=${RUNTIME_ENV:-"${WORKING_DIR}/verl/trainer/runtime_env.yaml"}
+# Paths
+DA... | recipe/fully_async_policy/shell/dapo_30b_a3b_base_math_fsdp.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [rollout,vllm] feat: disable sleep mode in fully-async mode | 4,521 | volcengine/verl | 16,907 | Python | chenjiaoAngel | |
"""
Context manager entry for switching the engine and model into evaluation mode.
Usage:
with engine.eval_mode():
# runs in evaluation mode
"""
return EngineEvalModeCtx(self, **kwargs)
def optimizer_zero_grad(self):
"""
Zero out ... | No issues found. | """
Context manager entry for switching the engine and model into evaluation mode.
Usage:
with engine.eval_mode():
# runs in evaluation mode
"""
return EngineEvalModeCtx(self, **kwargs)
def optimizer_zero_grad(self):
"""
Zero out ... | @@ -41,13 +41,11 @@
load_megatron_optimizer,
offload_megatron_model_to_cpu,
offload_megatron_optimizer,
- per_tensor_generator,
register_megatron_training_hooks,
)
from verl.utils.model import (
extract_multi_modal_inputs_tensordict,
load_mcore_dist_weights,
- load_megatron_gptmode... | verl/workers/engine/megatron/transformer_impl.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] chore: clean legacy code path part 1, make engine use mbridge by default | 4,528 | volcengine/verl | 16,907 | Python | ISEEKYAN | |
- "pkg://verl.utils.dataset.rl_dataset"
- "pkg://verl/utils/dataset/rl_dataset"
- or `file_path` (absolute or relative), e.g.,
- "file://verl/utils/dataset/rl_dataset.py"
- "/path/to/verl/utils/dataset/rl_dataset.py"
... | No issues found. | - "pkg://verl.utils.dataset.rl_dataset"
- "pkg://verl/utils/dataset/rl_dataset"
- or `file_path` (absolute or relative), e.g.,
- "file://verl/utils/dataset/rl_dataset.py"
- "/path/to/verl/utils/dataset/rl_dataset.py"
... | @@ -197,6 +197,39 @@ def load_extern_object(module_path: str, object_name: str) -> object:
return getattr(module, object_name)
+def load_class_from_fqn(fqn: str, description: str = "class") -> type:
+ """Load a class from its fully qualified name.
+
+ Args:
+ fqn: Fully qualified class name (e.g.,... | verl/utils/import_utils.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [rollout, trainer] feat: extend agent loop for custom implementations | 4,548 | volcengine/verl | 16,907 | Python | JoyboyBrian | |
# 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.
from abc import ABC, abstractmethod
from typing import Any, Callable
imp... | No issues found. | # 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.
from abc import ABC, abstractmethod
from typing import Any, Callable
imp... | @@ -43,3 +43,30 @@ def __call__(
return_dict: bool = False,
) -> torch.Tensor | dict[str, Any]:
pass
+
+ def _extract_reward_from_rm_scores(
+ self, data: DataProto, return_dict: bool = False
+ ) -> torch.Tensor | dict[str, Any] | None:
+ """
+ Extract reward from alrea... | verl/workers/reward_manager/abstract.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [reward] feat: Optimize reward computation when use_reward_loop=True | 4,581 | volcengine/verl | 16,907 | Python | none0663 | |
@dataclass
class ProfileConfig(BaseConfig):
"""Configuration for profiling.
The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config.
Args:
profile_ranks (Optional[list[int]]): List of ranks to profile. None means all ranks.
step_start (int): Sta... | No issues found. |
@dataclass
class ProfileConfig(BaseConfig):
"""Configuration for profiling.
The inheritance from BaseConfig provides omegaconf.DictConfig-like interface for a dataclass config.
Args:
profile_ranks (Optional[list[int]]): List of ranks to profile. None means all ranks.
step_start (int): Sta... | @@ -36,6 +36,7 @@ class CheckpointConfig(BaseConfig):
save_contents: list[str] = field(default_factory=lambda: ["model", "optimizer", "extra"])
load_contents: list[str] = field(default_factory=lambda: ["model", "optimizer", "extra"])
async_save: bool = False
+ mbridge_config: dict[str, Any] = field(de... | verl/trainer/config/config.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] feat: use yaml to manage mbridge args | 4,584 | volcengine/verl | 16,907 | Python | Kite0011 | |
A dictionary mapping mesh names to whether they are used for collect.
"""
assert mesh_name not in self.__dispatch_dp_rank, (
f"{mesh_name} is already registered, {self.__dispatch_dp_rank.keys()}"
)
assert mesh_name not in self.__collect_dp_rank, (
... | No issues found. | A dictionary mapping mesh names to whether they are used for collect.
"""
assert mesh_name not in self.__dispatch_dp_rank, (
f"{mesh_name} is already registered, {self.__dispatch_dp_rank.keys()}"
)
assert mesh_name not in self.__collect_dp_rank, (
... | @@ -117,6 +117,9 @@ def _query_dispatch_info(self, mesh_name: str):
@register(dispatch_mode=Dispatch.ONE_TO_ALL)
def _query_collect_info(self, mesh_name: str):
+ return self.query_collect_info(mesh_name)
+
+ def query_collect_info(self, mesh_name: str):
"""Query the collect info for a giv... | verl/single_controller/base/worker.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [data] feat: TransferQueue - remove redundant data collect for both TQ and DataProto | 4,618 | volcengine/verl | 16,907 | Python | 0oshowero0 | |
assert actor_worker_2.world_size == 4
data = DataProto.from_dict({"a": torch.zeros(8)})
actor_output_1 = actor_worker_1.add(data)
actor_output_2 = actor_worker_2.add(data)
assert actor_output_1.batch["a"].tolist() == [0, 0, 1, 1, 2, 2, 3, 3]
assert actor_output_2.batch["a"].tolist() == [100, 10... | No issues found. | assert actor_worker_2.world_size == 4
data = DataProto.from_dict({"a": torch.zeros(8)})
actor_output_1 = actor_worker_1.add(data)
actor_output_2 = actor_worker_2.add(data)
assert actor_output_1.batch["a"].tolist() == [0, 0, 1, 1, 2, 2, 3, 3]
assert actor_output_2.batch["a"].tolist() == [100, 10... | @@ -26,19 +26,20 @@
RayWorkerGroup,
split_resource_pool,
)
+from verl.utils.device import get_device_name, get_nccl_backend
@ray.remote
class Actor(Worker):
def __init__(self, worker_id) -> None:
super().__init__()
self.worker_id = worker_id
- self.temp_tensor = torch.rand... | tests/single_controller/test_split_resource_pool.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [ci] feat: add npu unit test | 4,626 | volcengine/verl | 16,907 | Python | yyyy2000 | |
# for training, we only care about loss and metrics
delta_time = timer.last
update_lr_scheduler = tu.get(data, key="update_lr_scheduler", default=False)
# update lr scheduler
if update_lr_scheduler:
lr = self.engine.lr_scheduler_step()
else:
l... | No issues found. | # for training, we only care about loss and metrics
delta_time = timer.last
update_lr_scheduler = tu.get(data, key="update_lr_scheduler", default=False)
# update lr scheduler
if update_lr_scheduler:
lr = self.engine.lr_scheduler_step()
else:
l... | @@ -27,11 +27,7 @@
from verl.single_controller.base.decorator import Dispatch, make_nd_compute_dataproto_dispatch_fn, register
from verl.utils import tensordict_utils as tu
from verl.utils.config import omega_conf_to_dataclass
-from verl.utils.device import (
- get_device_name,
- get_torch_device,
- set_exp... | verl/workers/engine_workers.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] feat: LoRA adapter only refit (TensorLoRARequest) | 4,632 | volcengine/verl | 16,907 | Python | HollowMan6 | |
log_gpu_memory_usage("After rollout offload", logger=logger)
for model in self.actor.actor_module:
model.train()
# add empty cache after each compute
aggressive_empty_cache(force_sync=True)
# FIXME(@wuxibin): megatron+sglang failed with `expandable_segments:True... | No issues found. | log_gpu_memory_usage("After rollout offload", logger=logger)
for model in self.actor.actor_module:
model.train()
# add empty cache after each compute
aggressive_empty_cache(force_sync=True)
# FIXME(@wuxibin): megatron+sglang failed with `expandable_segments:True... | @@ -906,12 +906,16 @@ def load_pretrained_model(self, checkpoint_path, del_local_after_load=True):
def save_checkpoint(self, checkpoint_path, hdfs_path=None, global_step=0, max_ckpt_to_keep=None):
if self._is_offload_param:
load_megatron_model_to_gpu(self.actor_module)
+ if self.checkp... | verl/workers/megatron_workers.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] fix: megatron async save ckpt fix | 4,638 | volcengine/verl | 16,907 | Python | Leem-Li | |
install_requires = [
"accelerate",
"codetiming",
"datasets",
"dill",
"hydra-core",
"numpy<2.0.0",
"pandas",
"peft",
"pyarrow>=19.0.0",
"pybind11",
"pylatexenc",
"ray[default]>=2.41.0",
"torchdata",
"tensordict>=0.8.0,<=0.10.0,!=0.9.0",
"transformers",
"wa... | No issues found. |
install_requires = [
"accelerate",
"codetiming",
"datasets",
"dill",
"hydra-core",
"numpy<2.0.0",
"pandas",
"peft",
"pyarrow>=19.0.0",
"pybind11",
"pylatexenc",
"ray[default]>=2.41.0",
"torchdata",
"tensordict>=0.8.0,<=0.10.0,!=0.9.0",
"transformers",
"wa... | @@ -57,7 +57,7 @@
]
TRL_REQUIRES = ["trl<=0.9.6"]
MCORE_REQUIRES = ["mbridge"]
-TRANSFERQUEUE_REQUIRES = ["TransferQueue==0.1.4.dev1"]
+TRANSFERQUEUE_REQUIRES = ["TransferQueue==0.1.5.dev3"]
extras_require = {
"test": TEST_REQUIRES, | setup.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [data] feat: TransferQueue - Support sync TransferQueue client & optimize clear interface and validation procedure | 4,660 | volcengine/verl | 16,907 | Python | 0oshowero0 | |
critic_cls = RayClassWithInitArgs(cls=self.role_worker_mapping[Role.Critic], config=critic_cfg)
self.resource_pool_to_cls[resource_pool]["critic"] = critic_cls
# create reference policy if needed
if self.use_reference_policy:
resource_pool = self.resource_pool_manage... | No issues found. | critic_cls = RayClassWithInitArgs(cls=self.role_worker_mapping[Role.Critic], config=critic_cfg)
self.resource_pool_to_cls[resource_pool]["critic"] = critic_cls
# create reference policy if needed
if self.use_reference_policy:
resource_pool = self.resource_pool_manage... | @@ -48,11 +48,7 @@
from verl import DataProto
from verl.experimental.dataset.sampler import AbstractCurriculumSampler
-from verl.single_controller.ray import (
- RayClassWithInitArgs,
- RayResourcePool,
- RayWorkerGroup,
-)
+from verl.single_controller.ray import RayClassWithInitArgs, RayResourcePool, RayW... | verl/experimental/transfer_queue/ray_trainer.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] feat: Share actor and ref in LoRA | 4,673 | volcengine/verl | 16,907 | Python | HollowMan6 | |
Detects the available accelerator and returns the corresponding PyTorch
device type string. Currently supports CUDA, Ascend NPU, and CPU.
Returns:
str: Device type string ('cuda', 'npu', or 'cpu').
"""
if is_cuda_available:
device = "cuda"
elif is_npu_available:
device =... | No issues found. | Detects the available accelerator and returns the corresponding PyTorch
device type string. Currently supports CUDA, Ascend NPU, and CPU.
Returns:
str: Device type string ('cuda', 'npu', or 'cpu').
"""
if is_cuda_available:
device = "cuda"
elif is_npu_available:
device =... | @@ -15,19 +15,26 @@
logger = logging.getLogger(__name__)
-def is_torch_npu_available() -> bool:
+def is_torch_npu_available(check_device=True) -> bool:
"""Check if Ascend NPU is available for PyTorch operations.
Attempts to detect NPU availability by checking for the torch.npu module
and its is_av... | verl/utils/device.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [sglang] fix: sglang TP+DP support / port bug | 4,715 | volcengine/verl | 16,907 | Python | hustmf | |
"num_attention_heads": 16,
"num_key_value_heads": 8,
"head_dim": 256,
"sliding_window": 1024,
"layer_types": None,
# Will be auto-generated based on sliding_window_pattern
"sliding_window_pattern": 6,
# Every 6th layer is fu... | No issues found. | "num_attention_heads": 16,
"num_key_value_heads": 8,
"head_dim": 256,
"sliding_window": 1024,
"layer_types": None,
# Will be auto-generated based on sliding_window_pattern
"sliding_window_pattern": 6,
# Every 6th layer is fu... | @@ -16,14 +16,16 @@
import pytest
-from verl.utils.flops_counter import _DEVICE_FLOPS, FlopsCounter, get_device_flops
+from verl.utils.flops_counter import FlopsCounter
VALID_CONFIG_TYPE = {"llama", "qwen2", "qwen3", "qwen3_moe", "deepseek_v3", "mistral", "gemma3_text", "apertus"}
class Config:
def __... | tests/utils/test_flops_counter.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [perf] feat: Add MFU for Qwen3-VL dense | 4,753 | volcengine/verl | 16,907 | Python | zhihaofang1017 | |
COMMAND="torchrun --standalone --nnodes=${NNODES:-1} --nproc-per-node=${NUM_GPUS:-1} ${ENTRYPOINT}"
else
ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer_ray"}
COMMAND="python ${ENTRYPOINT} trainer.nnodes=${NNODES:-1} trainer.n_gpus_per_node=${NUM_GPUS:-1}"
fi
RESUME_MODE=disable
ckpts_home=${ckpts_home:-~... | No issues found. | COMMAND="torchrun --standalone --nnodes=${NNODES:-1} --nproc-per-node=${NUM_GPUS:-1} ${ENTRYPOINT}"
else
ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer_ray"}
COMMAND="python ${ENTRYPOINT} trainer.nnodes=${NNODES:-1} trainer.n_gpus_per_node=${NUM_GPUS:-1}"
fi
RESUME_MODE=disable
ckpts_home=${ckpts_home:-~... | @@ -1,5 +1,17 @@
set -x
+NUM_GPUS=${NUM_GPUS:-4}
+
+mode=${mode:-spmd}
+
+if [ "$mode" = "spmd" ]; then
+ ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.sft_trainer"}
+ COMMAND="torchrun --standalone --nnodes=${NNODES:-1} --nproc-per-node=${NUM_GPUS:-1} ${ENTRYPOINT}"
+else
+ ENTRYPOINT=${ENTRYPOINT:-"-m verl.trainer.... | tests/special_npu/run_qwen2_5_05b_sft_peft_sp2.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [trainer] feat: enable ray-based sft trainer on ascend npu | 4,764 | volcengine/verl | 16,907 | Python | ji-huazhong | |
"""Test reduce_metrics correctly handles Metric objects."""
metric = Metric(aggregation="mean")
metric.extend([1.0, 2.0, 3.0])
metrics = {
"custom_metric": metric,
"list_metric": [4.0, 5.0, 6.0],
}
result = reduce_metrics(metrics)
self.as... | No issues found. | """Test reduce_metrics correctly handles Metric objects."""
metric = Metric(aggregation="mean")
metric.extend([1.0, 2.0, 3.0])
metrics = {
"custom_metric": metric,
"list_metric": [4.0, 5.0, 6.0],
}
result = reduce_metrics(metrics)
self.as... | @@ -32,6 +32,10 @@
from verl.utils.metric import (
reduce_metrics,
)
+from verl.utils.metric.utils import (
+ AggregationType,
+ Metric,
+)
class TestReduceMetrics(unittest.TestCase):
@@ -67,6 +71,167 @@ def test_reduce_metrics_single_value(self):
self.assertEqual(result["single"], 5.0)
+... | tests/trainer/ppo/test_metric_utils_on_cpu.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [worker, training_utils] fix: Engine Metric Aggregation | 4,778 | volcengine/verl | 16,907 | Python | JacobHelwig | |
rank,
world_size,
get_torch_device().current_device(),
)
@register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False)
def sync_rollout_weights(self):
assert (self._is_actor or self._is_rollout) and not self.config.hybrid_engine
assert hasattr(self, "_... | No issues found. | rank,
world_size,
get_torch_device().current_device(),
)
@register(dispatch_mode=Dispatch.ONE_TO_ALL, blocking=False)
def sync_rollout_weights(self):
assert (self._is_actor or self._is_rollout) and not self.config.hybrid_engine
assert hasattr(self, "_... | @@ -22,7 +22,7 @@
from ray.util.collective import collective
from torch.distributed.fsdp import FullyShardedDataParallel as FSDP
-from recipe.one_step_off_policy.distributed_util import vllm_stateless_init_process_group
+from recipe.one_step_off_policy.distributed_utils import vllm_stateless_init_process_group
fro... | recipe/one_step_off_policy/fsdp_workers.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [recipe] fix: workaround for making the one-step off-policy recipe compatible with IPv6 environments on Ascend NPU | 4,782 | volcengine/verl | 16,907 | Python | ji-huazhong | |
set -x
MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct}
MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}}
python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=grpo \
data.train_files=$HOME/data/gsm8k/train.parquet \
data.val_files=$HOME/data/gsm8k/test.parquet \
data.train_batch_size=1... | No issues found. | set -x
MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct}
MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}}
python3 -m verl.trainer.main_ppo \
algorithm.adv_estimator=grpo \
data.train_files=$HOME/data/gsm8k/train.parquet \
data.val_files=$HOME/data/gsm8k/test.parquet \
data.train_batch_size=1... | @@ -1,5 +1,4 @@
set -x
-export VLLM_ASCEND_ENABLE_NZ=0
MODEL_ID=${MODEL_ID:-Qwen/Qwen2.5-0.5B-Instruct}
MODEL_PATH=${MODEL_PATH:-${HOME}/.cache/models/${MODEL_ID}} | tests/special_npu/run_qwen2_5_05b_grpo.sh | 0 | Shell | 1 | none | 16 | 47 | 47 | true | [rollout, vllm] fix: accuracy issue in verl serve mode + vllm-ascend + dp + ep + tp scenarios | 4,783 | volcengine/verl | 16,907 | Python | leo-pony | |
#!/usr/bin/env bash
set -xeuo pipefail
rm -rf ~/verl/test/log
mkdir -p ~/verl/test/log
export VERL_FILE_LOGGER_ROOT=~/verl/test/log
VPP_SIZE=${VPP_SIZE:-2}
# test with single gpu as golden
echo "run with single gpu as golden"
BACKEND=fsdp SP_SIZE=1 FSDP_SIZE=1 NUM_GPUS=1 FSDP_STRATEGY=fsdp VERL_FILE_LOGGER_PATH=~/ve... | No issues found. | #!/usr/bin/env bash
set -xeuo pipefail
rm -rf ~/verl/test/log
mkdir -p ~/verl/test/log
export VERL_FILE_LOGGER_ROOT=~/verl/test/log
VPP_SIZE=${VPP_SIZE:-2}
# test with single gpu as golden
echo "run with single gpu as golden"
BACKEND=fsdp SP_SIZE=1 FSDP_SIZE=1 NUM_GPUS=1 FSDP_STRATEGY=fsdp VERL_FILE_LOGGER_PATH=~/ve... | @@ -25,9 +25,8 @@ echo "run with sp2 fsdp_size2 num_gpus8 fsdp_strategy fsdp2"
BACKEND=fsdp SP_SIZE=2 FSDP_SIZE=2 NUM_GPUS=8 FSDP_STRATEGY=fsdp2 bash tests/special_e2e/sft/run_sft_engine.sh
# test with veomni
-# FIXME(ji-huazhong): set SP=1 cause qwen_vl do not support SP right now
-echo "run with sp1 fsdp_size4 nu... | tests/special_e2e/sft/test_sft_engine_all.sh | 0 | Shell | 1 | none | 16 | 43 | 43 | true | [trainer] feat: VeOmniEngine supports qwen3_vl ulysses | 4,806 | volcengine/verl | 16,907 | Python | A1waysBeenHere | |
def test_torch_save_data_proto():
obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
labels = ["a", "b", "c"]
data = tu.get_tensordict({"obs": obs, "labels": labels}, non_tensor_dict={"info": "test_info"})
filename = "test_data.pt"
torch.save(data, filename)
loaded_data = torch.load(filename, weight... | No issues found. |
def test_torch_save_data_proto():
obs = torch.tensor([[1, 2], [3, 4], [5, 6]])
labels = ["a", "b", "c"]
data = tu.get_tensordict({"obs": obs, "labels": labels}, non_tensor_dict={"info": "test_info"})
filename = "test_data.pt"
torch.save(data, filename)
loaded_data = torch.load(filename, weight... | @@ -988,3 +988,34 @@ def test_get_tensordict_agent_loop_scenario():
# Verify metadata
assert td["global_steps"] == 42
+
+
+def test_contiguous():
+ # create a tensordict that contains normal tensor, nested tensor,
+ # nontensorstack with numpy, nontensorstack with tensor, NonTensorData with numpy and ... | tests/test_protocol_v2_on_cpu.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [misc] feat: consolidate tensordict before dispatch | 4,830 | volcengine/verl | 16,907 | Python | vermouth1992 | |
# runs in evaluation mode
"""
return EngineEvalModeCtx(self, **kwargs)
def optimizer_zero_grad(self):
"""
Zero out gradients of all parameters before starting a new backward pass.
"""
self.optimizer.zero_grad()
# use use_contiguous_buffers_in_... | No issues found. | # runs in evaluation mode
"""
return EngineEvalModeCtx(self, **kwargs)
def optimizer_zero_grad(self):
"""
Zero out gradients of all parameters before starting a new backward pass.
"""
self.optimizer.zero_grad()
# use use_contiguous_buffers_in_... | @@ -536,9 +536,12 @@ def forward_backward_batch(self, data: TensorDict, loss_function: Callable, forw
else:
return {}
- def get_per_tensor_param(self):
+ def get_per_tensor_param(self, **kwargs):
load_megatron_model_to_gpu(self.module, load_grad=False)
- per_tensor_param = ... | verl/workers/engine/megatron/transformer_impl.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [fsdp, megatron] fix: Engine Rollout Worker LoRA Parameter Update | 4,836 | volcengine/verl | 16,907 | Python | JacobHelwig | |
max_response_length=512
ppo_mini_batch_size=3
actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2))
infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2))
exper_name=Node${NODES}_bs${bs}_${PP}${TP}${EP}${ETP}_${SGLANG_INFER_TP}_minbs${ppo_mini_batch_size}_micro_bs${micro_bs}
... | No issues found. | max_response_length=512
ppo_mini_batch_size=3
actor_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2))
infer_ppo_max_token_len=$(((max_prompt_length + max_response_length) * 2))
exper_name=Node${NODES}_bs${bs}_${PP}${TP}${EP}${ETP}_${SGLANG_INFER_TP}_minbs${ppo_mini_batch_size}_micro_bs${micro_bs}
... | @@ -0,0 +1,110 @@
+
+set -x
+
+NODES=6
+
+# R2: enable routing replay
+# R3: enable rollout routing replay
+# If enabling R3, please set actor_rollout_ref.rollout.enable_rollout_routing_replay=True
+# R3 example is based on SGLang related commit https://github.com/sgl-project/sglang/commit/bed301a5acaa9577c9aa706468bdf... | examples/router_replay/run_qwen30_a3b_megatron_sglang.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [algo, rollout, sglang] feat: Support router replay with sglang | 4,840 | volcengine/verl | 16,907 | Python | moehanabi | |
with engine.eval_mode():
# runs in evaluation mode
"""
return EngineEvalModeCtx(self, **kwargs)
def optimizer_zero_grad(self):
"""
Zero out gradients of all parameters before starting a new backward pass.
"""
self.optimizer.zero_grad()
... | No issues found. | with engine.eval_mode():
# runs in evaluation mode
"""
return EngineEvalModeCtx(self, **kwargs)
def optimizer_zero_grad(self):
"""
Zero out gradients of all parameters before starting a new backward pass.
"""
self.optimizer.zero_grad()
... | @@ -38,6 +38,7 @@
vocab_parallel_log_probs_from_logits,
)
from verl.utils.megatron_utils import (
+ get_megatron_module_device,
load_megatron_model_to_gpu,
load_megatron_optimizer,
offload_megatron_model_to_cpu,
@@ -437,7 +438,8 @@ def save_checkpoint(
global_step: Integer training... | verl/workers/engine/megatron/transformer_impl.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [ckpt] fix: Megatron save ckpt after validation | 4,841 | volcengine/verl | 16,907 | Python | wdl339 | |
dp_rank = 0
dp_size = 1
self.train_sampler = DistributedSampler(
self.train_dataset, shuffle=True, num_replicas=dp_size, rank=dp_rank, drop_last=True
)
self.global_batch_size = config.data.train_batch_size
self.train_batch_size_per_dp = self.global_batch_si... | No issues found. |
dp_rank = 0
dp_size = 1
self.train_sampler = DistributedSampler(
self.train_dataset, shuffle=True, num_replicas=dp_size, rank=dp_rank, drop_last=True
)
self.global_batch_size = config.data.train_batch_size
self.train_batch_size_per_dp = self.global_batch_si... | @@ -291,11 +291,11 @@ def fit(self):
global_step += 1
# construct tensordict
data = tu.get_tensordict(tensor_dict=data, non_tensor_dict=meta_info)
- batch_seqlens = self._get_batch_seqlens(data=data)
+ batch_seqlens = self._get_batch_seqle... | verl/trainer/sft_trainer_ray.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | fix(sft_trainer): Fix global_tokens and total_tokens metrics always showing 0.0 | 4,854 | volcengine/verl | 16,907 | Python | khazic | |
"""
# see comments from train_batch
maybe_fix_3d_position_ids(data)
with torch.no_grad():
outputs = self.forward_backward_batch(data, loss_function, forward_only=True)
return outputs
def get_per_tensor_param(self) -> tuple[Generator[tuple[str, torch.Tensor], Non... | No issues found. | """
# see comments from train_batch
maybe_fix_3d_position_ids(data)
with torch.no_grad():
outputs = self.forward_backward_batch(data, loss_function, forward_only=True)
return outputs
def get_per_tensor_param(self) -> tuple[Generator[tuple[str, torch.Tensor], Non... | @@ -22,14 +22,7 @@
from tensordict import TensorDict
from verl.utils.device import get_device_name
-
-
-def _maybe_fix_3d_position_ids(data: TensorDict):
- # note for tensordict with pickle/unpickle. nested tensor in tensordict after consolidate and pickle/unpickle
- # will incur indexing error for ragged ten... | verl/workers/engine/base.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [misc] fix: fix 3d position_ids for train_mini_batch | 4,860 | volcengine/verl | 16,907 | Python | wdl339 | |
print("[FullyAsyncTrainer] async_rollout_manager sleep")
await self.async_rollout_manager.sleep()
else:
print("[FullyAsyncTrainer] Skip async rollout manager (use_trainer_do_validate=False)")
async def fit(self):
"""
The training loop of PPO.
The ... | No issues found. | print("[FullyAsyncTrainer] async_rollout_manager sleep")
await self.async_rollout_manager.sleep()
else:
print("[FullyAsyncTrainer] Skip async rollout manager (use_trainer_do_validate=False)")
async def fit(self):
"""
The training loop of PPO.
The ... | @@ -269,12 +269,12 @@ async def init_workers(self):
async def _init_async_rollout_manager(self):
# use async rollout do validate
+ print(f"[FullyAsyncTrainer] use_trainer_do_validate: {self.config.async_training.use_trainer_do_validate}")
if self.config.async_training.use_trainer_do_vali... | verl/experimental/fully_async_policy/fully_async_trainer.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [rollout] feat: use rollout and validate parallel process | 4,863 | volcengine/verl | 16,907 | Python | chenjiaoAngel | |
self.resource_pool_to_cls[resource_pool][str(Role.Critic)] = critic_cls
# create reference policy if needed
if self.use_reference_policy and Role.RefPolicy in self.role_worker_mapping:
resource_pool = self.resource_pool_manager.get_resource_pool(Role.RefPolicy)
ref_p... | No issues found. | self.resource_pool_to_cls[resource_pool][str(Role.Critic)] = critic_cls
# create reference policy if needed
if self.use_reference_policy and Role.RefPolicy in self.role_worker_mapping:
resource_pool = self.resource_pool_manager.get_resource_pool(Role.RefPolicy)
ref_p... | @@ -339,7 +339,7 @@ def __init__(
self.role_worker_mapping = role_worker_mapping
self.resource_pool_manager = resource_pool_manager
- self.use_reference_policy = need_reference_policy(self.role_worker_mapping)
+ self.use_reference_policy = need_reference_policy(self.config)
# ... | verl/trainer/ppo/ray_trainer.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [worker] feat: New engine share actor and ref for LoRA | 4,867 | volcengine/verl | 16,907 | Python | HollowMan6 | |
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
# Get PR title from environment
pr_title = os.environ.get("PR_TITLE", "").strip()
# Define rules
allowed_modules = ["fsdp", "megatron", "veomni", "sglang", "vllm", "rollout", "trainer"]
allowed_... | No issues found. | # See the License for the specific language governing permissions and
# limitations under the License.
import os
import re
# Get PR title from environment
pr_title = os.environ.get("PR_TITLE", "").strip()
# Define rules
allowed_modules = ["fsdp", "megatron", "veomni", "sglang", "vllm", "rollout", "trainer"]
allowed_... | @@ -19,7 +19,7 @@
pr_title = os.environ.get("PR_TITLE", "").strip()
# Define rules
-allowed_modules = ["fsdp", "megatron", "sglang", "vllm", "rollout", "trainer"]
+allowed_modules = ["fsdp", "megatron", "veomni", "sglang", "vllm", "rollout", "trainer"]
allowed_modules += ["tests", "training_utils", "recipe", "hard... | tests/special_sanity/check_pr_title.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [veomni] refactor: minor refactoring to ensure veomni engine compatibility with forward_only mode | 4,889 | volcengine/verl | 16,907 | Python | ji-huazhong | |
import torch
from safetensors.torch import safe_open
from tqdm import tqdm
from transformers import AutoConfig
from veomni.models import build_tokenizer, save_model_weights
@dataclass
class StateDictIterator:
filepath: str
def __iter__(self) -> Generator[tuple[str, "torch.Tensor"], None, None]:
if s... | No issues found. |
import torch
from safetensors.torch import safe_open
from tqdm import tqdm
from transformers import AutoConfig
from veomni.models import build_tokenizer, save_model_weights
@dataclass
class StateDictIterator:
filepath: str
def __iter__(self) -> Generator[tuple[str, "torch.Tensor"], None, None]:
if s... | @@ -0,0 +1,121 @@
+# Copyright 2025 Bytedance Ltd. and/or its affiliates
+
+# 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 r... | scripts/veomni/moe_merge.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [veomni] feat: support offloading/loading the veomni model/optimizer | 4,916 | volcengine/verl | 16,907 | Python | ji-huazhong | |
add_bias_linear=False,
add_qkv_bias=qkv_bias,
qk_layernorm=qk_layernorm,
)
# override_transformer_config_kwargs as kwargs shall never be none
args.update(override_transformer_config_kwargs)
return check_and_construct_configs(args, TransformerConfig)
def hf_to_mcore_config_qwen2... | No issues found. | add_bias_linear=False,
add_qkv_bias=qkv_bias,
qk_layernorm=qk_layernorm,
)
# override_transformer_config_kwargs as kwargs shall never be none
args.update(override_transformer_config_kwargs)
return check_and_construct_configs(args, TransformerConfig)
def hf_to_mcore_config_qwen2... | @@ -290,7 +290,7 @@ def hf_to_mcore_config_dpskv3(
set_experimental_flag(True)
- from .patch_v012 import apply_patch
+ from .patch import apply_patch
apply_patch()
| verl/models/mcore/config_converter.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] fix: patch mcore for MLA support with flash_attn | 4,931 | volcengine/verl | 16,907 | Python | HollowMan6 | |
def forward(
self,
input_,
weight=None,
runtime_gather_output=None,
):
"""
Forward pass for the linear layer.
This method computes the linear transformation and handles sequence parallelism
if enabled, gathering outputs from different sequence pa... | No issues found. |
def forward(
self,
input_,
weight=None,
runtime_gather_output=None,
):
"""
Forward pass for the linear layer.
This method computes the linear transformation and handles sequence parallelism
if enabled, gathering outputs from different sequence pa... | @@ -153,9 +153,15 @@ def freeze_moe_router(model):
if hasattr(layer.mlp.router, "bias"):
layer.mlp.router.bias.requires_grad = False
if hasattr(layer.mlp, "shared_experts"):
- if hasattr(layer.mlp.shared_experts, "gate_weight"):
+ ... | verl/models/mcore/bridge.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [rollout,mbridge] feat: add metrics for rollout num preempted and fix mbrideg freeze moe | 4,956 | volcengine/verl | 16,907 | Python | RobotGF | |
# Note the default MultiturnSFT Dataset requires all the sys/user/assistant in 'data.message_key'
DATASET_DIR=${DATASET_DIR:-~/dataset/rl/gsm8k}
TRAIN_FILES=${DATASET_DIR}/train.parquet
VAL_FILES=${DATASET_DIR}/eval.parquet
project_name=verl_sft_test
RESUME_MODE=disable
MODEL_PATH="XiaomiMiMo/MiMo-7B-RL"
ckpts_home... | No issues found. |
# Note the default MultiturnSFT Dataset requires all the sys/user/assistant in 'data.message_key'
DATASET_DIR=${DATASET_DIR:-~/dataset/rl/gsm8k}
TRAIN_FILES=${DATASET_DIR}/train.parquet
VAL_FILES=${DATASET_DIR}/eval.parquet
project_name=verl_sft_test
RESUME_MODE=disable
MODEL_PATH="XiaomiMiMo/MiMo-7B-RL"
ckpts_home... | @@ -0,0 +1,102 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+NUM_GPUS=${NUM_GPUS:-8}
+SP_SIZE=${SP_SIZE:-1}
+TP_SIZE=${TP_SIZE:-1}
+PP_SIZE=${PP_SIZE:-1}
+VPP_SIZE=${VPP_SIZE:-null}
+CP_SIZE=${CP_SIZE:-1}
+PAD_MODE=${PAD_MODE:-no_padding}
+USE_REMOVE_PADDING=${USE_REMOVE_PADDING:-False}
+LR="1e-5"
+MINLR="1e-6"
+
+exp... | examples/sft/gsm8k/run_mimo_megatron_mtp.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [megatron] feat: Support MTP training in SFT | 4,981 | volcengine/verl | 16,907 | Python | arvyanh | |
)
self.resource_pool_to_cls[resource_pool][str(Role.RefPolicy)] = ref_policy_cls
# create a reward model if reward_fn is None
# for legacy discriminative reward model, we create a reward model worker here
# for reward loop discriminative reward model, we create a reward ... | No issues found. | )
self.resource_pool_to_cls[resource_pool][str(Role.RefPolicy)] = ref_policy_cls
# create a reward model if reward_fn is None
# for legacy discriminative reward model, we create a reward model worker here
# for reward loop discriminative reward model, we create a reward ... | @@ -1438,7 +1438,7 @@ def fit(self):
gen_batch_output = self.actor_rollout_wg.generate_sequences(gen_batch_output)
else:
if curr_step_profile:
- self.async_rollout_manager.start_profile()
+ ... | verl/trainer/ppo/ray_trainer.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [rollout, perf, cfg] fix: Add global step info and support more profile control params for rollout profiling (sglang backend) | 5,025 | volcengine/verl | 16,907 | Python | bithighrr | |
probs, top_indices = _compute_topk(scores, topk, num_groups=num_groups, group_topk=group_topk)
if router_replay is not None:
router_replay.record_indices(top_indices)
return probs, top_indices
elif routing_action == RouterReplayAction.REPLAY_FORWARD:
... | No issues found. | probs, top_indices = _compute_topk(scores, topk, num_groups=num_groups, group_topk=group_topk)
if router_replay is not None:
router_replay.record_indices(top_indices)
return probs, top_indices
elif routing_action == RouterReplayAction.REPLAY_FORWARD:
... | @@ -84,6 +84,7 @@ def __init__(self):
self.recorded_topk_idx = None # For recording
self.router_replay_action = None # Router replay action for this layer
self.replay_backward_list = [] # List of tensors for backward pass replay
+ self.layer_number = None # Global layer index if av... | verl/utils/megatron/router_replay_patch.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron, training_utils] fix: router replay R3 align router replay data with global layer indices | 5,037 | volcengine/verl | 16,907 | Python | HollowMan6 | |
async def _run_agent_loop(
self,
sampling_params: dict[str, Any],
trajectory: dict[str, Any],
*,
agent_name: str,
trace: bool = True,
**kwargs,
) -> _InternalAgentLoopOutput:
with rollout_trace_attr(
step=trajectory["step"],
... | No issues found. |
async def _run_agent_loop(
self,
sampling_params: dict[str, Any],
trajectory: dict[str, Any],
*,
agent_name: str,
trace: bool = True,
**kwargs,
) -> _InternalAgentLoopOutput:
with rollout_trace_attr(
step=trajectory["step"],
... | @@ -33,7 +33,6 @@
from verl.experimental.agent_loop.prometheus_utils import update_prometheus_config
from verl.experimental.agent_loop.utils import resolve_config_path
-from verl.experimental.reward_loop import RewardLoopWorker
from verl.protocol import DataProto
from verl.single_controller.ray.base import RayRes... | verl/experimental/agent_loop/agent_loop.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [reward] feat: split reward loop manager and agent loop manager | 5,134 | volcengine/verl | 16,907 | Python | yyDing1 | |
for key, val in default_keys.items():
if key not in data.keys():
tu.assign_non_tensor(data, **{key: val})
with (
self.engine.train_mode(disable_auto_offload=disable_auto_offload),
Timer(name="train_batch", logger=None) as timer,
):
... | No issues found. | for key, val in default_keys.items():
if key not in data.keys():
tu.assign_non_tensor(data, **{key: val})
with (
self.engine.train_mode(disable_auto_offload=disable_auto_offload),
Timer(name="train_batch", logger=None) as timer,
):
... | @@ -615,7 +615,9 @@ async def update_weights(self):
# main memory can trade sync time to avoid OOM
self.rollout.sleep_level = 1
- do_lora_base_sync = not self.base_sync_done or self.rollout.sleep_level != 1
+ do_lora_base_sync = (not self.base_sync_done) or (
+ ... | verl/workers/engine_workers.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [worker] feat: Avoid redundant base weight sync when engine doesn't sleep | 5,147 | volcengine/verl | 16,907 | Python | JohnConnor123 | |
# use use_contiguous_buffers_in_local_ddp and no overlap_dp_param_comm
for chunk in self.module:
# if use distributed optimizer, zero grad buffer will be handled by optimizer
chunk.zero_grad_buffer()
def optimizer_step(self):
"""
Perform an optimization step ... | No issues found. | # use use_contiguous_buffers_in_local_ddp and no overlap_dp_param_comm
for chunk in self.module:
# if use distributed optimizer, zero grad buffer will be handled by optimizer
chunk.zero_grad_buffer()
def optimizer_step(self):
"""
Perform an optimization step ... | @@ -286,7 +286,18 @@ def initialize(self):
self.optimizer = self._build_optimizer()
self.lr_scheduler = self._build_lr_scheduler()
- tmp_config = OmegaConf.create({"model": {"path": self.model_config.local_path}})
+ full_reshardable = self.engine_config.dist_ckpt_optim_fully_reshardabl... | verl/workers/engine/megatron/transformer_impl.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] fix: checkpoints uses `fully_reshardable` by default when supported | 5,154 | volcengine/verl | 16,907 | Python | HollowMan6 | |
images = multi_modal_data.get("images")
videos = multi_modal_data.get("videos")
param_version = kwargs.get("param_version", 0)
metrics = {}
request_id = uuid4().hex
param_version_start = param_version
param_version_end = param_version
if not output:
... | No issues found. | images = multi_modal_data.get("images")
videos = multi_modal_data.get("videos")
param_version = kwargs.get("param_version", 0)
metrics = {}
request_id = uuid4().hex
param_version_start = param_version
param_version_end = param_version
if not output:
... | @@ -37,11 +37,14 @@ def __init__(self, *args, **kwargs):
async def run(self, sampling_params: dict[str, Any], **kwargs) -> AgentLoopOutput:
output: Optional[AgentLoopOutput] = kwargs.get("output", None)
messages = list(kwargs["raw_prompt"])
+ multi_modal_data = await self.process_vision_in... | verl/experimental/fully_async_policy/agent_loop/partial_single_turn_agent_loop.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [vllm] fix: handle multimodal inputs correctly in full async mode | 5,160 | volcengine/verl | 16,907 | Python | Silas-11 | |
if update_successful:
# allgather already execute in optimizer.step in new megatron
pass
else:
raise NotImplementedError("Megatron optimizer step failed. This should not happen")
return grad_norm
def lr_scheduler_step(self):
"""
Advance t... | No issues found. | if update_successful:
# allgather already execute in optimizer.step in new megatron
pass
else:
raise NotImplementedError("Megatron optimizer step failed. This should not happen")
return grad_norm
def lr_scheduler_step(self):
"""
Advance t... | @@ -11,7 +11,6 @@
# 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.
-
import logging
import os
from functools import partial
@@ -33,6 +32,11 @@
from verl.utils.debug import log_gpu_memo... | verl/workers/engine/megatron/transformer_impl.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [algo] feat: support rollout router replay in MegatronEngine | 5,185 | volcengine/verl | 16,907 | Python | xhx1022 | |
# TODO Here you can use the new registration method to support dynamic registration of roles
if config.reward_model.enable_resource_pool:
if config.reward_model.n_gpus_per_node <= 0:
raise ValueError("config.reward_model.n_gpus_per_node must be greater than 0")
if... | No issues found. | # TODO Here you can use the new registration method to support dynamic registration of roles
if config.reward_model.enable_resource_pool:
if config.reward_model.n_gpus_per_node <= 0:
raise ValueError("config.reward_model.n_gpus_per_node must be greater than 0")
if... | @@ -314,13 +314,24 @@ def run(self, config):
# Used for multimodal LLM, could be None
processor = hf_processor(local_path, trust_remote_code=trust_remote_code, use_fast=True)
- # Load the reward manager for training and validation.
- reward_fn = load_reward_manager(
- config... | verl/trainer/main_ppo.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [reward] refactor: migrate all reward managers to the new asynchronous reward manager | 5,189 | volcengine/verl | 16,907 | Python | yyDing1 | |
post_process=post_process,
)
else:
output = output_orig
return output
return fused_forward_model
def fused_forward_no_padding_gen(vision_model: bool = False):
def fused_forward_no_padding(
model,
input_ids: Tensor,
labels: Tensor... | No issues found. | post_process=post_process,
)
else:
output = output_orig
return output
return fused_forward_model
def fused_forward_no_padding_gen(vision_model: bool = False):
def fused_forward_no_padding(
model,
input_ids: Tensor,
labels: Tensor... | @@ -29,12 +29,12 @@
from packaging import version
from torch import Tensor
-from verl.models.mcore.util import preprocess_packed_seqs
+from verl.models.mcore.util import preprocess_packed_seqs, preprocess_thd_no_padding
from verl.utils.kernel.linear_cross_entropy import linear_cross_entropy
from verl.utils.megatr... | verl/models/mcore/model_forward_fused.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [megatron] feat: fused kernel suppport for new model engine | 5,191 | volcengine/verl | 16,907 | Python | HollowMan6 | |
get_torch_device().set_rng_state(self.gen_random_states)
if fsdp_version(self.actor_module_fsdp) == 1:
fsdp_unshard_exit_stack = contextlib.ExitStack()
optional_state = _get_module_fsdp_state(self.actor_module_fsdp)
if optional_state is None:
self.fsd... | No issues found. | get_torch_device().set_rng_state(self.gen_random_states)
if fsdp_version(self.actor_module_fsdp) == 1:
fsdp_unshard_exit_stack = contextlib.ExitStack()
optional_state = _get_module_fsdp_state(self.actor_module_fsdp)
if optional_state is None:
self.fsd... | @@ -305,6 +305,7 @@ def init_model(self):
lr_scheduler=self.actor_lr_scheduler,
processing_class=self.processor if self.processor is not None else self.tokenizer,
checkpoint_config=self.config.actor.checkpoint,
+ trust_remote_code=self.config.model.trust... | verl/experimental/vla/fsdp_workers.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [fsdp] fix: Support trust_remote_code during FSDP HugginFace checkpoint save | 5,200 | volcengine/verl | 16,907 | Python | thvasilo | |
detached,
worker_env=None,
):
"""Initialize the worker group by creating new workers from a resource pool.
Args:
resource_pool: Resource pool for worker allocation
ray_cls_with_init: Class with initialization arguments for workers
bin_pack: Whethe... | No issues found. | detached,
worker_env=None,
):
"""Initialize the worker group by creating new workers from a resource pool.
Args:
resource_pool: Resource pool for worker allocation
ray_cls_with_init: Class with initialization arguments for workers
bin_pack: Whethe... | @@ -87,11 +87,25 @@ def sort_placement_group_by_node_ip(pgs: list[PlacementGroup]) -> list[Placement
@ray.remote
-def get_master_addr_port() -> tuple[str, str]:
+def get_master_addr_port(master_port_range: Optional[list[int]] = None) -> tuple[str, str]:
addr = ray.util.get_node_ip_address().strip("[]")
- w... | verl/single_controller/ray/base.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [ray,trainer] feat: add master port range configuration for port range | 5,201 | volcengine/verl | 16,907 | Python | RobotGF | |
# fsdp
actor_rollout_ref.actor.fsdp_config.use_orig_params=True
actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16
actor_rollout_ref.actor.fsdp_config.param_offload=${offload}
actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload}
actor_rollout_ref.actor.fsdp_config.forward_prefe... | No issues found. | # fsdp
actor_rollout_ref.actor.fsdp_config.use_orig_params=True
actor_rollout_ref.actor.fsdp_config.model_dtype=bfloat16
actor_rollout_ref.actor.fsdp_config.param_offload=${offload}
actor_rollout_ref.actor.fsdp_config.optimizer_offload=${offload}
actor_rollout_ref.actor.fsdp_config.forward_prefe... | @@ -0,0 +1,175 @@
+#!/usr/bin/env bash
+set -xeuo pipefail
+
+project_name="verl_grpo_qwen3-next-80b"
+experiment_name="Qwen3_Next_80B_Instruct"
+
+# Paths
+WORK_DIR=${WORK_DIR:-"${HOME}/verl"}
+MODEL_PATH=${WORK_DIR}/Qwen3-Next-80B-A3B-Instruct
+TRAIN_FILE=${WORK_DIR}/datasets/dapo-math-17k/dapo-math-17k.parquet
+TEST... | examples/grpo_trainer/run_qwen3next_80b_fsdp_npu.sh | 0 | Shell | 1 | none | 16 | 51 | 51 | true | [fsdp] feat: add script for qwen3next training on npu platform | 5,236 | volcengine/verl | 16,907 | Python | zjchenn | |
last_exception = None
for attempt in range(max_retries):
try:
# It's safer to have a timeout instead of None, which can hang indefinitely.
timeout = aiohttp.ClientTimeout(total=None)
async with aiohttp.ClientSession(timeout=timeout) as session:... | No issues found. | last_exception = None
for attempt in range(max_retries):
try:
# It's safer to have a timeout instead of None, which can hang indefinitely.
timeout = aiohttp.ClientTimeout(total=None)
async with aiohttp.ClientSession(timeout=timeout) as session:... | @@ -20,7 +20,7 @@
import numpy as np
import ray
import torch
-from omegaconf import DictConfig
+from omegaconf import DictConfig, open_dict
from tensordict import TensorDict
from verl.protocol import DataProto
@@ -35,6 +35,51 @@
logger.setLevel(os.getenv("VERL_LOGGING_LEVEL", "WARN"))
+def migrate_legacy_re... | verl/experimental/reward_loop/reward_loop.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [reward] fix: backward compatibility with old reward config | 5,287 | volcengine/verl | 16,907 | Python | yyDing1 | |
method="monkey_patch_model", kwargs={"vocab_size": len(self.model_config.tokenizer)}
)
build_app_sig = inspect.signature(build_app)
supported_tasks: tuple[Any, ...] = ()
if "supported_tasks" in build_app_sig.parameters:
supported_tasks = await engine_client.get_s... | No issues found. | method="monkey_patch_model", kwargs={"vocab_size": len(self.model_config.tokenizer)}
)
build_app_sig = inspect.signature(build_app)
supported_tasks: tuple[Any, ...] = ()
if "supported_tasks" in build_app_sig.parameters:
supported_tasks = await engine_client.get_s... | @@ -153,10 +153,10 @@ def __init__(
if self.node_rank == 0:
self._master_address = self._server_address
# used for torch.distributed.init_process_group
- self._master_port, self._master_sock = get_free_port(self._server_address)
+ self._master_port, self._master_... | verl/workers/rollout/vllm_rollout/vllm_async_server.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [ci] fix: occasional CI failures caused by sglang server port conflicts | 5,310 | volcengine/verl | 16,907 | Python | pengwu22 | |
else:
index = np.arange(len(batch))
trajectory_info = await get_trajectory_info(
batch.meta_info.get("global_steps", -1), index, batch.meta_info.get("validate", False)
)
if not partial_output_list:
partial_output_list = [None] * len(batch)
tr... | No issues found. | else:
index = np.arange(len(batch))
trajectory_info = await get_trajectory_info(
batch.meta_info.get("global_steps", -1), index, batch.meta_info.get("validate", False)
)
if not partial_output_list:
partial_output_list = [None] * len(batch)
tr... | @@ -28,11 +28,11 @@
AsyncLLMServerManager,
DictConfigWrap,
_agent_loop_registry,
+ _get_rollout_and_model_config,
get_trajectory_info,
)
-from verl.experimental.agent_loop.prometheus_utils import update_prometheus_config
from verl.protocol import DataProto
-from verl.single_controller.ray impor... | verl/experimental/fully_async_policy/agent_loop/agent_loop.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [BREAKING][rollout,cfg] refactor: get rid of actor_rollout_ref config from rollout | 5,418 | volcengine/verl | 16,907 | Python | wuxibin89 | |
adapter = AsyncTRTLLMHttpAdapter(host="localhost", port=8000)
post_response = AsyncMock()
post_response.status = 200
post_response.headers = {"Content-Type": "application/json"}
post_response.raise_for_status = Mock()
post_response.json = AsyncMock(return_value={"status"... | No issues found. | adapter = AsyncTRTLLMHttpAdapter(host="localhost", port=8000)
post_response = AsyncMock()
post_response.status = 200
post_response.headers = {"Content-Type": "application/json"}
post_response.raise_for_status = Mock()
post_response.json = AsyncMock(return_value={"status"... | @@ -175,9 +175,9 @@ def test_init_without_device_mesh(self):
worker0 = replica.workers[0]
worker1 = replica.workers[1]
- replica_rank = ray.get(worker0._get_attribute.remote("replica_rank"))
- is_leader_rank_0 = ray.get(worker0._get_attribute.remote("is_leader_rank"))
-... | tests/workers/rollout/rollout_trtllm/test_adapter.py | 0 | Python | 1 | none | 16 | 51 | 51 | true | [ci] fix: failed trtllm_unit_tests with attribute error | 5,446 | volcengine/verl | 16,907 | Python | HollowMan6 | |
deindent()
push(`})`)
}
// JavaScript
function genCallExpression(
node: CallExpression,
context: CodegenContext,
multilines = node.arguments.length > 2
) {
context.push(node.callee + `(`, node, true)
multilines && context.indent()
genNodeList(node.arguments, context, multilines)
multilines && context... | This cleans up object property mappings/location. 🙈 Looks little dirty. | deindent()
push(`})`)
}
// JavaScript
function genCallExpression(
node: CallExpression,
context: CodegenContext,
multilines = node.arguments.length > 2
) {
context.push(node.callee + `(`, node, true)
multilines && context.indent()
genNodeList(node.arguments, context, multilines)
multilines && context... | @@ -518,7 +518,8 @@ function genObjectExpression(node: ObjectExpression, context: CodegenContext) {
push(multilines ? `{` : `{ `)
multilines && indent()
for (let i = 0; i < properties.length; i++) {
- const { key, value } = properties[i]
+ const { key, value, loc } = properties[i]
+ push('', { loc } a... | packages/compiler-core/src/codegen.ts | 26 | TypeScript | 0.214 | suggestion | 71 | 44 | 45 | false | fix(compiler): generate correct mappings for v-for and object properties | 69 | vuejs/vue-next | 25,296 | TypeScript | znck | znck |
if (node) {
let name
if (
node.type === NodeTypes.EXPRESSION &&
!node.children &&
!node.isStatic
) {
const content = node.content.replace(/^_ctx\./, '')
if (content !== node.content && isSimpleIdentifier(content)) {
... | I have added this for L522. Otherwise, I guess it would always be non-empty. | if (node) {
let name
if (
node.type === NodeTypes.EXPRESSION &&
!node.children &&
!node.isStatic
) {
const content = node.content.replace(/^_ctx\./, '')
if (content !== node.content && isSimpleIdentifier(content)) {
... | @@ -130,7 +130,7 @@ function createCodegenContext(
}
})
}
- advancePositionWithMutation(context, code)
+ if (code) advancePositionWithMutation(context, code) | packages/compiler-core/src/codegen.ts | 26 | TypeScript | 0.214 | suggestion | 76 | 51 | 51 | false | fix(compiler): generate correct mappings for v-for and object properties | 69 | vuejs/vue-next | 25,296 | TypeScript | znck | znck |
}
}
type PropsExpression = ObjectExpression | CallExpression | ExpressionNode
function buildProps(
{ loc: elementLoc, props }: ElementNode,
context: TransformContext
): {
props: PropsExpression
directives: DirectiveNode[]
} {
let isStatic = true
let properties: ObjectExpression['properties'] = []
cons... | This gives the correct location for an object property name. | // node.tagType can also be TEMPLATE, in which case nothing needs to be done
}
}
type PropsExpression = ObjectExpression | CallExpression | ExpressionNode
function buildProps(
{ loc: elementLoc, props }: ElementNode,
context: TransformContext
): {
props: PropsExpression
directives: DirectiveNode[]
} {
... | @@ -121,7 +122,7 @@ function buildProps(
const { loc, name, value } = prop
properties.push(
createObjectProperty(
- createExpression(name, true, loc),
+ createExpression(name, true, getInnerRange(loc, 0, name.length)), | packages/compiler-core/src/transforms/transformElement.ts | 26 | TypeScript | 0.214 | suggestion | 60 | 51 | 51 | false | fix(compiler): generate correct mappings for v-for and object properties | 69 | vuejs/vue-next | 25,296 | TypeScript | znck | znck |
TransformContext
} from '../transform'
import {
NodeTypes,
ExpressionNode,
createExpression,
SourceLocation
} from '../ast'
import { createCompilerError, ErrorCodes } from '../errors'
import { getInnerRange } from '../utils'
import { RENDER_LIST } from '../runtimeConstants'
import { processExpression } from '... | This gives a more accurate location for `v-for` and even allows setting breakpoint separately for v-for and the element it's applied to. | TransformContext
} from '../transform'
import {
NodeTypes,
ExpressionNode,
createExpression,
SourceLocation
} from '../ast'
import { createCompilerError, ErrorCodes } from '../errors'
import { getInnerRange } from '../utils'
import { RENDER_LIST } from '../runtimeConstants'
import { processExpression } from '... | @@ -25,7 +25,7 @@ export const transformFor = createStructuralDirectiveTransform(
context.replaceNode({
type: NodeTypes.FOR,
- loc: node.loc,
+ loc: dir.loc, | packages/compiler-core/src/transforms/vFor.ts | 26 | TypeScript | 0.5 | suggestion | 136 | 51 | 51 | false | fix(compiler): generate correct mappings for v-for and object properties | 69 | vuejs/vue-next | 25,296 | TypeScript | znck | znck |
[['x', {}], 'foo x'],
[undefined, 'foo']
])
})
test('class merge between parent and child', () => {
const root = document.createElement('div')
type ClassItem = {
value: string | object | string[]
}
const childClass: ClassItem = { value: 'd' }
const child = () => h('div', ... | The only difference between the 2 cases is that `child2` has an empty `props` declaration, therefore it successfully triggered class merging while the other one didn't.
I didn't see anything about this behavior in the `inheritAttrs` RFS.
Seems a bug in the current implementation?
@yyx990803 | [['x', { y: true, z: true }], 'foo x y z'],
[['x', { y: true, z: false }], 'foo x y'],
[['f', { z: true }], 'foo f z'],
[['l', 'f', { n: true, z: true }], 'foo l f n z'],
[['x', {}], 'foo x'],
[undefined, 'foo']
])
})
test('class merge between parent and child', () => {
... | @@ -0,0 +1,235 @@
+// https://github.com/vuejs/vue/blob/dev/test/unit/features/directives/class.spec.js
+
+import { h, render, ComponentOptions } from '../../src'
+
+function assertClass(assertions: Array<Array<any>>) {
+ const root = document.createElement('div')
+ const dynamic = { value: '' }
+ const wrapper = ()... | packages/runtime-dom/__tests__/modules/class.spec.ts | 26 | TypeScript | 0.714 | bug | 295 | 51 | 51 | false | test: add test for runtime-dom/modules/class | 75 | vuejs/vue-next | 25,296 | TypeScript | haoqunjiang | haoqunjiang |
return target.update
case '$nextTick':
return nextTick
case '$watch':
return instanceWatch.bind(target)
}
}
return target.user[key]
}
}
},
// this trap is only called in browser-compiled render func... | ```suggestion
`Attempting to mutate public property "${key}". ` +
`Properties starting with $ are reserved and readonly.`,
``` | case '$forceUpdate':
return target.update
case '$nextTick':
return nextTick
case '$watch':
return instanceWatch.bind(target)
}
}
return target.user[key]
}
}
},
// this trap is only cal... | @@ -90,10 +91,15 @@ export const PublicInstanceProxyHandlers = {
} else if (hasOwn(renderContext, key)) {
renderContext[key] = value
} else if (key[0] === '$' && key.slice(1) in target) {
- // TODO warn attempt of mutating public property
+ __DEV__ &&
+ warn(
+ `Attempting to ... | packages/runtime-core/src/componentProxy.ts | 26 | TypeScript | 0.714 | suggestion | 149 | 40 | 42 | false | fix: add warnings | 82 | vuejs/vue-next | 25,296 | TypeScript | yyx990803 | TimvdEijnden |
return target.user[key]
}
}
},
// this trap is only called in browser-compiled render functions that use
// `with (this) {}`
has(_: any, key: string): boolean {
return key[0] !== '_' && !globalsWhitelist.has(key)
},
set(target: ComponentInternalInstance, key: string, value: any): boo... | ```suggestion
warn(
`Attempting to mutate prop "${key}". Props are readonly.`,
target
)
``` | }
return target.user[key]
}
}
},
// this trap is only called in browser-compiled render functions that use
// `with (this) {}`
has(_: any, key: string): boolean {
return key[0] !== '_' && !globalsWhitelist.has(key)
},
set(target: ComponentInternalInstance, key: string, valu... | @@ -90,10 +91,16 @@ export const PublicInstanceProxyHandlers = {
} else if (hasOwn(renderContext, key)) {
renderContext[key] = value
} else if (key[0] === '$' && key.slice(1) in target) {
- // TODO warn attempt of mutating public property
+ __DEV__ &&
+ warn(
+ `Attempting to ... | packages/runtime-core/src/componentProxy.ts | 26 | TypeScript | 0.714 | suggestion | 132 | 34 | 35 | false | fix: add warnings | 82 | vuejs/vue-next | 25,296 | TypeScript | yyx990803 | TimvdEijnden |
})
}
}
if (methods) {
for (const key in methods) {
renderContext[key] = (methods as MethodOptions)[key].bind(ctx)
}
}
if (watchOptions) {
for (const key in watchOptions) {
const raw = watchOptions[key]
const getter = () => ctx[key]
if (isString(raw)) {
c... | ```suggestion
warn(`Invalid watch option: "${key}"`)
``` | })
}
}
if (methods) {
for (const key in methods) {
renderContext[key] = (methods as MethodOptions)[key].bind(ctx)
}
}
if (watchOptions) {
for (const key in watchOptions) {
const raw = watchOptions[key]
const getter = () => ctx[key]
if (isString(raw)) {
c... | @@ -267,15 +267,15 @@ export function applyOptions(
if (isFunction(handler)) {
watch(getter, handler as any)
} else if (__DEV__) {
- // TODO warn invalid watch handler path
+ warn(`Expected a function as watch handler "${raw}"`)
}
} else if (isFunction(raw)... | packages/runtime-core/src/apiOptions.ts | 26 | TypeScript | 0.571 | suggestion | 66 | 51 | 51 | false | fix: add warnings | 82 | vuejs/vue-next | 25,296 | TypeScript | yyx990803 | TimvdEijnden |
if (computedOptions) {
for (const key in computedOptions) {
const opt = (computedOptions as ComputedOptions)[key]
renderContext[key] = isFunction(opt)
? computed(opt.bind(ctx))
: computed({
get: opt.get.bind(ctx),
set: opt.set.bind(ctx)
})
}
}
... | ```suggestion
warn(`Invalid watch handler specified by key "${raw}": `, handler)
``` | if (computedOptions) {
for (const key in computedOptions) {
const opt = (computedOptions as ComputedOptions)[key]
renderContext[key] = isFunction(opt)
? computed(opt.bind(ctx))
: computed({
get: opt.get.bind(ctx),
set: opt.set.bind(ctx)
})
}
}
... | @@ -267,15 +267,15 @@ export function applyOptions(
if (isFunction(handler)) {
watch(getter, handler as any)
} else if (__DEV__) {
- // TODO warn invalid watch handler path
+ warn(`Expected a function as watch handler "${raw}"`) | packages/runtime-core/src/apiOptions.ts | 26 | TypeScript | 0.714 | suggestion | 96 | 51 | 51 | false | fix: add warnings | 82 | vuejs/vue-next | 25,296 | TypeScript | yyx990803 | TimvdEijnden |
import { mockWarn } from '@vue/runtime-test'
describe('component proxy', () => {
/**
* These tests should tests the warnings for PublicInstanceProxyHandlers in componentProxy
* {@link @vue/runtime-core/src/componentProxy}
*
* The function setupStatefulComponent is the location where PublicInstanceProxyHa... | The `PublicInstanceProxy` is simply `this` inside any Vue 2.x options :) | import { createApp, nodeOps, mockWarn } from '@vue/runtime-test'
const createTestInstance = (props?: any) => {
const component = {
render() {}
}
const root = nodeOps.createElement('div')
return createApp().mount(component, root, props)
}
describe('component proxy', () => {
describe('warnings', () => {
... | @@ -0,0 +1,36 @@
+import { mockWarn } from '@vue/runtime-test'
+
+describe('component proxy', () => {
+ /**
+ * These tests should tests the warnings for PublicInstanceProxyHandlers in componentProxy
+ * {@link @vue/runtime-core/src/componentProxy}
+ *
+ * The function setupStatefulComponent is the location wh... | packages/runtime-core/__tests__/componentProxy.spec.ts | 11 | TypeScript | 0.357 | suggestion | 72 | 36 | 36 | false | fix: add warnings | 82 | vuejs/vue-next | 25,296 | TypeScript | yyx990803 | TimvdEijnden |
import { createApp, nodeOps, mockWarn } from '@vue/runtime-test'
describe('component proxy', () => {
/**
* These tests should tests the warnings for PublicInstanceProxyHandlers in componentProxy
* {@link @vue/runtime-core/src/componentProxy}
*
* The function setupStatefulComponent is the location where P... | Can we remove outdated comments here? | import { createApp, nodeOps, mockWarn } from '@vue/runtime-test'
const createTestInstance = (props?: any) => {
const component = {
render() {}
}
const root = nodeOps.createElement('div')
return createApp().mount(component, root, props)
}
describe('component proxy', () => {
describe('warnings', () => {
... | @@ -0,0 +1,49 @@
+import { createApp, nodeOps, mockWarn } from '@vue/runtime-test'
+
+describe('component proxy', () => {
+ /**
+ * These tests should tests the warnings for PublicInstanceProxyHandlers in componentProxy
+ * {@link @vue/runtime-core/src/componentProxy}
+ *
+ * The function setupStatefulComponen... | packages/runtime-core/__tests__/componentProxy.spec.ts | 11 | TypeScript | 0.143 | question | 37 | 36 | 36 | false | fix: add warnings | 82 | vuejs/vue-next | 25,296 | TypeScript | yyx990803 | TimvdEijnden |
describe('warnings', () => {
mockWarn()
let app: any, appProps: any
beforeEach(() => {
const component = {
render() {}
}
const root = nodeOps.createElement('div')
app = createApp().mount(component, root, appProps)
})
test('Attempting to mutate public property', ()... | Out of scope assignment here makes the tests a bit harder to read in isolation. I'd refactor it into something like a `createTestInstance()` function to be called in each spec. | mockWarn()
test('Attempting to mutate public property', () => {
const app = createTestInstance()
try {
app.$props = { foo: 'bar' }
} catch {
expect(
'Attempting to mutate public property "$props". ' +
'Properties starting with $ are reserved and readonly... | @@ -0,0 +1,49 @@
+import { createApp, nodeOps, mockWarn } from '@vue/runtime-test'
+
+describe('component proxy', () => {
+ /**
+ * These tests should tests the warnings for PublicInstanceProxyHandlers in componentProxy
+ * {@link @vue/runtime-core/src/componentProxy}
+ *
+ * The function setupStatefulComponen... | packages/runtime-core/__tests__/componentProxy.spec.ts | 26 | TypeScript | 0.571 | refactor | 176 | 38 | 29 | false | fix: add warnings | 82 | vuejs/vue-next | 25,296 | TypeScript | yyx990803 | TimvdEijnden |
export type MaybeRef<T = any> = T | Ref<T>
export type MaybeRefOrGetter<T = any> = MaybeRef<T> | (() => T)
/**
* Returns the inner value if the argument is a ref, otherwise return the
* argument itself. This is a sugar function for
* `val = isRef(val) ? val.value : val`.
*
* @example
* ```js
* function useFoo(... | ```suggestion
* Normalizes values / refs / getters to values.
``` | * `val = isRef(val) ? val.value : val`.
*
* @example
* ```js
* function useFoo(x: number | Ref<number>) {
* const unwrapped = unref(x)
* // unwrapped is guaranteed to be number now
* }
* ```
*
* @param ref - Ref or plain value to be converted into the plain value.
* @see {@link https://vuejs.org/api/rea... | @@ -207,10 +215,30 @@ export function triggerRef(ref: Ref) {
* @param ref - Ref or plain value to be converted into the plain value.
* @see {@link https://vuejs.org/api/reactivity-utilities.html#unref}
*/
-export function unref<T>(ref: T | Ref<T>): T {
+export function unref<T>(ref: MaybeRef<T>): T {
return is... | packages/reactivity/src/ref.ts | 26 | TypeScript | 0.571 | suggestion | 68 | 51 | 51 | false | feat(reactivity): improve support of getter usage in reactivity APIs | 7,997 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | yyx990803 |
describe('reactive in shallow ref', () => {
const x = shallowRef({
a: reactive({
b: ref(0)
})
})
expectType<number>(x.value.a.b)
})
describe('toRef <-> toValue', () => {
function foo(
a: MaybeRef<string>,
b: () => string,
c: MaybeRefOrGetter<string>,
d: ComputedRef<string>
) {... | ```suggestion
// @ts-expect-error ref created from getter should be readonly
``` |
describe('reactive in shallow ref', () => {
const x = shallowRef({
a: reactive({
b: ref(0)
})
})
expectType<number>(x.value.a.b)
})
describe('toRef <-> toValue', () => {
function foo(
a: MaybeRef<string>,
b: () => string,
c: MaybeRefOrGetter<string>,
d: ComputedRef<string>
) {... | @@ -319,3 +333,58 @@ describe('reactive in shallow ref', () => {
expectType<number>(x.value.a.b)
})
+
+describe('toRef <-> toValue', () => {
+ function foo(
+ a: MaybeRef<string>,
+ b: () => string,
+ c: MaybeRefOrGetter<string>,
+ d: ComputedRef<string>
+ ) {
+ const r = toRef(a)
+ expectType... | packages/dts-test/ref.test-d.ts | 26 | TypeScript | 0.786 | suggestion | 86 | 51 | 51 | false | feat(reactivity): improve support of getter usage in reactivity APIs | 7,997 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | yyx990803 |
private readonly _object: T,
private readonly _key: K,
private readonly _defaultValue?: T[K]
) {}
get value() {
const val = this._object[this._key]
return val === undefined ? (this._defaultValue as T[K]) : val
}
set value(newVal) {
this._object[this._key] = newVal
}
get dep(): Dep... | Should this also have `__v_isReadonly` set to `true`?
```js
isReadonly(toRef(() => {})) // should be true?
``` | return val === undefined ? (this._defaultValue as T[K]) : val
}
set value(newVal) {
this._object[this._key] = newVal
}
get dep(): Dep | undefined {
return getDepFromReactive(toRaw(this._object), this._key)
}
}
class GetterRefImpl<T> {
public readonly __v_isRef = true
public readonly __v_isR... | @@ -333,12 +361,35 @@ class ObjectRefImpl<T extends object, K extends keyof T> {
}
}
+class GetterRefImpl<T> {
+ public readonly __v_isRef = true
+ constructor(private readonly _getter: () => T) {}
+ get value() {
+ return this._getter()
+ }
+} | packages/reactivity/src/ref.ts | 26 | TypeScript | 0.786 | suggestion | 115 | 51 | 51 | false | feat(reactivity): improve support of getter usage in reactivity APIs | 7,997 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | yyx990803 |
}
}
class GetterRefImpl<T> {
public readonly __v_isRef = true
constructor(private readonly _getter: () => T) {}
get value() {
return this._getter()
}
}
export type ToRef<T> = IfAny<T, Ref<T>, [T] extends [Ref] ? T : Ref<T>>
/**
* Used to normalize values / refs / getters into refs.
*
* @example
* `... | ```suggestion
* // equivalent to ref(1)
``` | get value() {
return this._getter()
}
}
export type ToRef<T> = IfAny<T, Ref<T>, [T] extends [Ref] ? T : Ref<T>>
/**
* Used to normalize values / refs / getters into refs.
*
* @example
* ```js
* // returns existing refs as-is
* toRef(existingRef)
*
* // creates a ref that calls the getter on .value acc... | @@ -333,12 +361,35 @@ class ObjectRefImpl<T extends object, K extends keyof T> {
}
}
+class GetterRefImpl<T> {
+ public readonly __v_isRef = true
+ constructor(private readonly _getter: () => T) {}
+ get value() {
+ return this._getter()
+ }
+}
+
export type ToRef<T> = IfAny<T, Ref<T>, [T] extends [Ref] ?... | packages/reactivity/src/ref.ts | 26 | TypeScript | 0.429 | suggestion | 46 | 51 | 51 | false | feat(reactivity): improve support of getter usage in reactivity APIs | 7,997 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | yyx990803 |
export function toRef<T>(
value: T
): T extends () => infer R
? Readonly<Ref<R>>
: T extends Ref
? T
: Ref<UnwrapRef<T>>
export function toRef<T extends object, K extends keyof T>(
object: T,
key: K
): ToRef<T[K]>
export function toRef<T extends object, K extends keyof T>(
object: T,
key: K,
default... | I think this will potentially cause a breaking change for arrays:
```js
const arr = reactive(['a', 'b'])
const c = toRef(arr, 1)
```
Previously this would have worked, but now `c` will be the whole array.
`Symbol` keys for objects too. | : Ref<UnwrapRef<T>>
export function toRef<T extends object, K extends keyof T>(
object: T,
key: K
): ToRef<T[K]>
export function toRef<T extends object, K extends keyof T>(
object: T,
key: K,
defaultValue: T[K]
): ToRef<Exclude<T[K], undefined>>
export function toRef(
source: Record<string, any> | MaybeRe... | @@ -371,15 +430,31 @@ export function toRef<T extends object, K extends keyof T>(
key: K,
defaultValue: T[K]
): ToRef<Exclude<T[K], undefined>>
-export function toRef<T extends object, K extends keyof T>(
- object: T,
- key: K,
- defaultValue?: T[K]
-): ToRef<T[K]> {
- const val = object[key]
+export functio... | packages/reactivity/src/ref.ts | 26 | TypeScript | 0.857 | suggestion | 247 | 51 | 51 | false | feat(reactivity): improve support of getter usage in reactivity APIs | 7,997 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | yyx990803 |
type: Constructor<P>,
props?: (RawProps & P) | ({} extends P ? null : never),
children?: RawChildren | RawSlots,
): VNode
// fake constructor type returned by `defineComponent`
export function h(type: DefineComponent, children?: RawChildren): VNode
export function h<P>(
type: DefineComponent<P>,
props?: (Raw... | Just stumbled upon this as I was reading the changelog. Is there a reason why every call to `h` will now (afaik) create an inline version of `doCreateVNode`? Can't this be created outside of the scope of calling `h()`? | export function h<P>(
type: Constructor<P>,
props?: (RawProps & P) | ({} extends P ? null : never),
children?: RawChildren | RawSlots,
): VNode
// fake constructor type returned by `defineComponent`
export function h(type: DefineComponent, children?: RawChildren): VNode
export function h<P>(
type: DefineCompon... | @@ -201,25 +202,35 @@ export function h<P>(
// Actual implementation
export function h(type: any, propsOrChildren?: any, children?: any): VNode {
+ // #6913 disable tracking block in h function
+ const doCreateVNode = (type: any, props?: any, children?: any) => { | packages/runtime-core/src/h.ts | 26 | TypeScript | 0.643 | question | 218 | 46 | 51 | false | fix(runtime-core): disable tracking block in h function | 8,213 | vuejs/vue-next | 25,296 | TypeScript | spaceemotion | edison1105 |
) {
removedWhitespace = true
nodes[i] = null as any
} else {
// Otherwise, the whitespace is condensed into a single space
node.content = ' '
}
} else if (shouldCondense) {
// in condense mode, consecutive whitespaces in tex... | Do we still need this section of the change? Doesn't the code in `hydration.ts` cover it?
I think this part may cause other problems, e.g.:
```html
<script setup>
import { ref } from 'vue'
const msg = ref('\r')
</script>
<template>
<h1>{{ msg.startsWith('\r') }}</h1>
</template>
```
This will ren... | // - the whitespace is the first or last node, or:
// - (condense mode) the whitespace is between two comments, or:
// - (condense mode) the whitespace is between comment and element, or:
// - (condense mode) the whitespace is between two elements AND contains newline
i... | @@ -865,6 +865,13 @@ function condenseWhitespace(
node.content = node.content.replace(windowsNewlineRE, '\n')
}
}
+ // #9229 innerHTML normalize \r\n or \r into a single \n in the DOM
+ else if (
+ node.type === NodeTypes.INTERPOLATION &&
+ node.content.type === NodeTypes.SIMPLE_EXP... | packages/compiler-core/src/parser.ts | 26 | TypeScript | 0.857 | bug | 396 | 51 | 51 | false | fix(hydration): avoid mismatch during hydrate text with newlines in interpolation | 9,232 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | edison1105 |
// Instead of being a DirectiveTransform, v-slot processing is called during
// transformElement to build the slots object for a component.
export function buildSlots(
node: ElementNode,
context: TransformContext,
buildSlotFn: SlotFnBuilder = buildClientSlotFn
): {
slots: SlotsExpression
hasDynamicSlots: boo... | I think this also needs to account for identifiers being used within the `v-slot` value itself, e.g.:
```html
<MyChild v-for="i in items" v-slot="{ value = i.value }">
```
- [Playground - This PR](https://deploy-preview-9427--vue-sfc-playground.netlify.app/#__DEV__eNqFUk1P4zAQ/SsjX8pKNBHb3UsVVgsVB5D4EHCrOYRk2ho... | export function buildSlots(
node: ElementNode,
context: TransformContext,
buildSlotFn: SlotFnBuilder = buildClientSlotFn,
): {
slots: SlotsExpression
hasDynamicSlots: boolean
} {
context.helper(WITH_CTX)
const { children, loc } = node
const slotsProperties: Property[] = []
const dynamicSlots: (Condit... | @@ -134,9 +134,11 @@ export function buildSlots(
// since it likely uses a scope variable.
let hasDynamicSlots = context.scopes.vSlot > 0 || context.scopes.vFor > 0
// with `prefixIdentifiers: true`, this can be further optimized to make
- // it dynamic only when the slot actually uses the scope variables.
+ ... | packages/compiler-core/src/transforms/vSlot.ts | 26 | TypeScript | 0.857 | suggestion | 1,619 | 51 | 51 | false | fix(compiler-core/v-slot): force dynamic slots when slot referencing scope vars | 9,427 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | edison1105 |
if (
targetAnchor &&
targetAnchor.nodeType === 8 &&
(targetAnchor as Comment).data === 'teleport anchor'
) {
vnode.targetAnchor = targetAnchor
;(target as TeleportTargetElement)._lpa =
vnode.targetAnchor && nextSibling(vnode.t... | ```suggestion
else if (isTeleportDisabled(vnode.props)) {
if (vnode.shapeFlag & ShapeFlags.ARRAY_CHILDREN) {
vnode.anchor = hydrateChildren(
nextSibling(node),
vnode,
parentNode(node)!,
parentComponent,
parentSuspense,
slotScopeIds,
optimized... | interface TeleportTargetElement extends Element {
// last teleport target
_lpa?: Node | null
}
function hydrateTeleport(
node: Node,
vnode: TeleportVNode,
parentComponent: ComponentInternalInstance | null,
parentSuspense: SuspenseBoundary | null,
slotScopeIds: string[] | null,
optimized: boolean,
{
... | @@ -406,6 +406,21 @@ function hydrateTeleport(
}
updateCssVars(vnode)
}
+ if (!target && isTeleportDisabled(vnode.props)) { | packages/runtime-core/src/components/Teleport.ts | 26 | TypeScript | 0.786 | performance | 425 | 51 | 51 | false | fix(Teleport): hydrate disabled Teleport with undefined target | 11,235 | vuejs/vue-next | 25,296 | TypeScript | edison1105 | linzhe141 |
if (!root) return
if (styles) {
styles.forEach(css => {
const s = document.createElement('style')
s.textContent = css
root.appendChild(s)
// record for HMR
if (__DEV__) {
;(this._styles || (this._styles = [])).push(s)
}
})
}
}
/**
... | @yyx990803 Can we do the append? As my global styles are in the last tag and override child styles. In case of append we would have child styles come after the global. Maybe an option to specify this? | if (parent instanceof VueElement) {
instance.parent = parent._instance
instance.provides = parent._instance!.provides
break
}
}
}
}
return vnode
}
private _applyStyles(
styles: string[] | undefined,
owner?: ConcreteComponent,
)... | @@ -486,19 +499,36 @@ export class VueElement extends BaseClass {
return vnode
}
- private _applyStyles(styles: string[] | undefined) {
- const root = this.shadowRoot
- if (!root) return
- if (styles) {
- styles.forEach(css => {
- const s = document.createElement('style')
- s.text... | packages/runtime-dom/src/apiCustomElement.ts | 26 | TypeScript | 0.429 | question | 202 | 51 | 51 | false | feat(custom-element): inject child components styles to custom element shadow root | 11,517 | vuejs/vue-next | 25,296 | TypeScript | boga001 | yyx990803 |
warn(`useCssVars is called without current active component instance.`)
return
}
const updateTeleports = (instance.ut = (vars = getter(instance.proxy)) => {
Array.from(
document.querySelectorAll(`[data-v-owner="${instance.uid}"]`),
).forEach(node => setVarsOnNode(node, vars))
})
if (__... | Should just call `setVars` and not create another watcher on each update. | warn(`useCssVars is called without current active component instance.`)
return
}
/* v8 ignore stop */
const updateTeleports = (instance.ut = (vars = getter(instance.proxy)) => {
Array.from(
document.querySelectorAll(`[data-v-owner="${instance.uid}"]`),
).forEach(node => setVarsOnNode(node... | @@ -47,6 +48,10 @@ export function useCssVars(getter: (ctx: any) => Record<string, string>) {
watchPostEffect(setVars)
})
+ onBeforeUpdate(() => {
+ watchPostEffect(setVars) | packages/runtime-dom/src/helpers/useCssVars.ts | 26 | TypeScript | 0.429 | suggestion | 73 | 51 | 51 | false | fix(runtime-dom): before update should also set css vars | 11,561 | vuejs/vue-next | 25,296 | TypeScript | yyx990803 | linzhe141 |
instance.isMounted = true
if (__DEV__ || __FEATURE_PROD_DEVTOOLS__) {
devtoolsComponentAdded(instance)
}
// #2458: deference mount-only object parameters to prevent memleaks
initialVNode = container = anchor = null as any
} else {
let { next, bu, u, pare... | ```suggestion
update()
``` | queuePostRenderEffect(
() => instance.emit('hook:mounted'),
parentSuspense,
)
}
// activated hook for keep-alive roots.
// #1742 activated hook must be accessed after first render
// since the hook may be injected by a child keep-alive
... | @@ -1438,7 +1438,7 @@ function baseCreateRenderer(
nonHydratedAsyncRoot.asyncDep!.then(() => {
// the instance may be destroyed during the time period
if (!instance.isUnmounted) {
- componentUpdateFn()
+ instance.update() | packages/runtime-core/src/renderer.ts | 26 | TypeScript | 0.429 | suggestion | 44 | 51 | 51 | false | fix(runtime-core): properly handle async component update before resolve | 11,619 | vuejs/vue-next | 25,296 | TypeScript | edison1105 | linzhe141 |
instance.isMounted = true
if (__DEV__ || __FEATURE_PROD_DEVTOOLS__) {
devtoolsComponentAdded(instance)
}
// #2458: deference mount-only object parameters to prevent memleaks
initialVNode = container = anchor = null as any
} else {
let { next, bu, u, pare... | There is another problem here. The `update()` is executed 2 times.
This behavior is not related to this PR. But it needs to be fixed.
The flow is as follows:
1. When `locateNonHydratedAsyncRoot` is called, `subComponent.asyncResolved` is `false`, `nonHydratedAsyncRoot` has a value, and a `then` callback is registe... | queuePostRenderEffect(
() => instance.emit('hook:mounted'),
parentSuspense,
)
}
// activated hook for keep-alive roots.
// #1742 activated hook must be accessed after first render
// since the hook may be injected by a child keep-alive
... | @@ -1438,7 +1438,7 @@ function baseCreateRenderer(
nonHydratedAsyncRoot.asyncDep!.then(() => {
// the instance may be destroyed during the time period
if (!instance.isUnmounted) {
- componentUpdateFn()
+ update() | packages/runtime-core/src/renderer.ts | 26 | TypeScript | 1 | suggestion | 1,250 | 51 | 51 | false | fix(runtime-core): properly handle async component update before resolve | 11,619 | vuejs/vue-next | 25,296 | TypeScript | edison1105 | linzhe141 |
this.$emit('one', 1)
},
render,
}
const app = createApp({
render() {
return [h(CompA), ', ', h(CompB)]
},
})
app.mixin({
emits: {
one: (arg: number) => arg === 0,
two: null,
},
})
const root = nodeOps.createElement('div')
app.mount(root)
expect(`event v... | These tests are outside the `describe` block. All the other tests in this file are inside the `describe`. Is there a reason to have these outside? | created(this) {
this.$emit('one', 1)
},
render,
})
const app = createApp({
render() {
return [h(CompA), h(CompB)]
},
})
app.mixin({
emits: {
one: validateByGlobalMixin,
two: null,
},
})
const root = nodeOps.createElemen... | @@ -599,3 +600,76 @@ describe('component: emit', () => {
expect(renderFn).toHaveBeenCalledTimes(1)
})
})
+
+test('merging emits for a component that is also used as a mixin', () => { | packages/runtime-core/__tests__/componentEmits.spec.ts | 26 | TypeScript | 0.5 | question | 146 | 51 | 26 | false | fix(runtime-core): use separate emits caches for components and mixins | 11,661 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | gcaaa31928 |
// Note: emits and listener fallthrough is tested in
// ./rendererAttrsFallthrough.spec.ts.
import {
type ComponentPublicInstance,
createApp,
defineComponent,
h,
nextTick,
nodeOps,
render,
toHandlers,
} from '@vue/runtime-test'
import { isEmitListener } from '../src/componentEmits'
describe('component... | ```suggestion
test('merging emits from global mixins and extends', () => {
``` | // Note: emits and listener fallthrough is tested in
// ./rendererAttrsFallthrough.spec.ts.
import {
type ComponentPublicInstance,
createApp,
defineComponent,
h,
nextTick,
nodeOps,
render,
toHandlers,
} from '@vue/runtime-test'
import { isEmitListener } from '../src/componentEmits'
describe('component... | @@ -599,3 +600,76 @@ describe('component: emit', () => {
expect(renderFn).toHaveBeenCalledTimes(1)
})
})
+
+test('merging emits for a component that is also used as a mixin', () => {
+ const render = () => h('div')
+ const CompA = {
+ render,
+ }
+
+ const mixin = {
+ emits: {
+ one: (arg: numbe... | packages/runtime-core/__tests__/componentEmits.spec.ts | 26 | TypeScript | 0.571 | suggestion | 80 | 676 | 644 | false | fix(runtime-core): use separate emits caches for components and mixins | 11,661 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | gcaaa31928 |
two: null,
},
})
const root = nodeOps.createElement('div')
app.mount(root)
expect(`event validation failed for event "one"`).not.toHaveBeenWarned()
})
test('merging props from global mixins and extends', () => {
let renderProxy: any
let extendedRenderProxy: any
const render = () => h('div')
... | Couldn't this use `defineComponent`, rather than explicitly typing the `this` value? That seems to be how the other tests in this file are handling it.
There's some similar code in the other new test where the typings could just be removed. | // Note: emits and listener fallthrough is tested in
// ./rendererAttrsFallthrough.spec.ts.
import {
type ComponentPublicInstance,
createApp,
defineComponent,
h,
nextTick,
nodeOps,
render,
toHandlers,
} from '@vue/runtime-test'
import { isEmitListener } from '../src/componentEmits'
describe('component... | @@ -599,3 +600,76 @@ describe('component: emit', () => {
expect(renderFn).toHaveBeenCalledTimes(1)
})
})
+
+test('merging emits for a component that is also used as a mixin', () => {
+ const render = () => h('div')
+ const CompA = {
+ render,
+ }
+
+ const mixin = {
+ emits: {
+ one: (arg: numbe... | packages/runtime-core/__tests__/componentEmits.spec.ts | 26 | TypeScript | 0.643 | suggestion | 243 | 44 | 644 | false | fix(runtime-core): use separate emits caches for components and mixins | 11,661 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | gcaaa31928 |
// Note: emits and listener fallthrough is tested in
// ./rendererAttrsFallthrough.spec.ts.
import {
type ComponentPublicInstance,
createApp,
defineComponent,
h,
nextTick,
nodeOps,
render,
toHandlers,
} from '@vue/runtime-test'
import { isEmitListener } from '../src/componentEmits'
describe('component... | Is there a reason to grab things from internal properties here? Can't this be tested via the public API, like in the other test? | // Note: emits and listener fallthrough is tested in
// ./rendererAttrsFallthrough.spec.ts.
import {
type ComponentPublicInstance,
createApp,
defineComponent,
h,
nextTick,
nodeOps,
render,
toHandlers,
} from '@vue/runtime-test'
import { isEmitListener } from '../src/componentEmits'
describe('component... | @@ -599,3 +600,76 @@ describe('component: emit', () => {
expect(renderFn).toHaveBeenCalledTimes(1)
})
})
+
+test('merging emits for a component that is also used as a mixin', () => {
+ const render = () => h('div')
+ const CompA = {
+ render,
+ }
+
+ const mixin = {
+ emits: {
+ one: (arg: numbe... | packages/runtime-core/__tests__/componentEmits.spec.ts | 26 | TypeScript | 0.357 | question | 128 | 676 | 644 | false | fix(runtime-core): use separate emits caches for components and mixins | 11,661 | vuejs/vue-next | 25,296 | TypeScript | skirtles-code | gcaaa31928 |
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