trinity.trainer.verl.monkey_patch 源代码
import sys
import torch
from transformers.modeling_utils import PreTrainedModel
from trinity.utils.log import get_logger
# modified from verl.models.transformers.monkey_patch.apply_monkey_patch
[文档]
def apply_monkey_patch( # noqa: C901
model: PreTrainedModel,
ulysses_sp_size: int = 1,
use_remove_padding: bool = True,
use_fused_kernels: bool = False,
fused_kernels_backend: str = None,
use_tiled_mlp: bool = False,
tiled_mlp_shards: int = 4,
):
"""
Apply monkey patch to the models for ulysses sequence parallel, fused kernel, and tiled MLP.
In the end of this function forward function of the model is patched for fused kernel.
If the model is not supported with fused kernel, please return after patch.
Args:
model: The model to apply the monkey patch.
ulysses_sp_size: The size of ulysses sequence parallel.
use_remove_padding: Whether to use remove padding.
use_fused_kernels: Whether to use fused kernels.
fused_kernels_backend: The backend to use for fused kernels.
use_tiled_mlp: Whether to use TiledMLP for memory-efficient MLP computation.
tiled_mlp_shards: Number of shards for TiledMLP (higher = lower memory, slightly slower).
"""
from verl.models.transformers.monkey_patch import (
_ulysses_flash_attention_forward,
patch_forward_with_backends,
patch_vlm_for_ulysses_input_slicing,
)
from verl.utils.import_utils import is_trl_available
from verl.utils.transformers_compat import is_transformers_version_in_range
logger = get_logger(__name__)
# Apply TiledMLP monkey patch for memory-efficient MLP computation
if use_tiled_mlp:
from verl.models.transformers.tiled_mlp import apply_tiled_mlp_monkey_patch
model_type = getattr(model.config, "model_type", None)
apply_tiled_mlp_monkey_patch(num_shards=tiled_mlp_shards, model_type=model_type)
"""Replace _flash_attention_forward to _ulysses_flash_attention_forward"""
module = sys.modules[model.__module__]
try:
num_attention_heads, num_key_value_heads = (
model.config.num_attention_heads,
model.config.num_key_value_heads,
)
except AttributeError:
num_attention_heads, num_key_value_heads = (
model.config.text_config.num_attention_heads,
model.config.text_config.num_key_value_heads,
)
assert (
num_attention_heads % ulysses_sp_size == 0
), f"num_attention_heads {num_attention_heads} must be divisible by ulysses_sp_size {ulysses_sp_size}"
assert (
num_key_value_heads % ulysses_sp_size == 0 or ulysses_sp_size % num_key_value_heads == 0
), (
f"num_key_value_heads {num_key_value_heads} must be divisible by ulysses_sp_size "
f"{ulysses_sp_size}or vise versa. Upon ulysses_sp_size % num_key_value_heads == 0,"
f"kv heads are repeated to ensure correctness."
)
if is_trl_available():
from trl import AutoModelForCausalLMWithValueHead # type: ignore
def state_dict(self, *args, **kwargs):
return torch.nn.Module.state_dict(self, *args, **kwargs)
AutoModelForCausalLMWithValueHead.state_dict = state_dict
logger.info("Monkey patch state_dict in AutoModelForCausalLMWithValueHead. ")
# TODO: VLM models only, unify monkey patch to LLM models.
if model.config.model_type in ["qwen2_5_vl", "qwen2_vl"]:
# Step 1: patch model to support image-text mixed data
if is_transformers_version_in_range(min_version="4.52.0"):
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
Qwen2_5_VLTextModel,
)
from transformers.models.qwen2_vl.modeling_qwen2_vl import Qwen2VLTextModel
else:
from transformers.models.qwen2_5_vl.modeling_qwen2_5_vl import (
Qwen2_5_VLModel as Qwen2_5_VLTextModel,
)
from transformers.models.qwen2_vl.modeling_qwen2_vl import (
Qwen2VLModel as Qwen2VLTextModel,
)
if is_transformers_version_in_range(min_version="4.53.0", max_version="4.53.3"):
raise RuntimeError("Transformers 4.53.* is bugged. Use transformers 4.54.0 or later.")
# Step 2: patch input for multimodal sequence parallelism
if ulysses_sp_size > 1:
patch_vlm_for_ulysses_input_slicing(Qwen2_5_VLTextModel)
patch_vlm_for_ulysses_input_slicing(Qwen2VLTextModel)
elif model.config.model_type in ["qwen3_vl", "qwen3_vl_moe"]:
# Step 1: patch model to support image-text mixed data
from transformers.models.qwen3_vl.modeling_qwen3_vl import Qwen3VLTextModel
from transformers.models.qwen3_vl_moe.modeling_qwen3_vl_moe import (
Qwen3VLMoeTextModel,
)
from verl.models.transformers.qwen3_vl import (
patch_qwen3_vl_moe_sparse_moe_block_forward,
)
# Step 1.5: patch Qwen3VLMoeTextSparseMoeBlock to fix transformers 4.57.3 bug
if model.config.model_type == "qwen3_vl_moe" and is_transformers_version_in_range(
max_version="4.57.3"
):
patch_qwen3_vl_moe_sparse_moe_block_forward()
# Step 2: patch input for multimodal sequence parallelism
if ulysses_sp_size > 1:
patch_vlm_for_ulysses_input_slicing(Qwen3VLTextModel)
patch_vlm_for_ulysses_input_slicing(Qwen3VLMoeTextModel)
elif model.config.model_type == "glm4v":
# Step 1: patch model to support image-text mixed data
from transformers.models.glm4v.modeling_glm4v import Glm4vTextModel
from trinity.common.patch.glm4v import glm4v_text_forward
Glm4vTextModel.forward = glm4v_text_forward
# Step 2: patch input for multimodal sequence parallelism
if ulysses_sp_size > 1:
patch_vlm_for_ulysses_input_slicing(Glm4vTextModel)
elif model.config.model_type == "kimi_vl":
if ulysses_sp_size > 1:
patch_vlm_for_ulysses_input_slicing(module.DeepseekV3ForCausalLM)
if use_fused_kernels:
logger.info("Not support fused kernels for KimiVL")
if use_remove_padding or ulysses_sp_size > 1:
if hasattr(module, "_flash_attention_forward"): # transformers <= 4.47.1 or legacy models
module._flash_attention_forward = _ulysses_flash_attention_forward
logger.info(f"Monkey patch _flash_attention_forward in {model.__module__}")
else:
from transformers.integrations import flash_attention
flash_attention._flash_attention_forward = _ulysses_flash_attention_forward
logger.info(f"Monkey patch _flash_attention_forward in {flash_attention.__name__}")
patch_forward_with_backends(
model, use_fused_kernels=use_fused_kernels, fused_kernels_backend=fused_kernels_backend
)
