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Drop torchair (#4814)

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aclgraph is stable and fast now. Let's drop torchair graph mode now.

TODO: some logic to adapt torchair should be cleaned up as well. We'll
do it in the following PR.

- vLLM version: v0.12.0
- vLLM main:
ad32e3e19c

Signed-off-by: wangxiyuan <wangxiyuan1007@gmail.com>
Co-authored-by: Mengqing Cao <cmq0113@163.com>
This commit is contained in:
wangxiyuan
2025-12-10 09:20:40 +08:00
committed by GitHub
parent ba9cda9dfd
commit 835b4c8f1d
84 changed files with 77 additions and 16881 deletions
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138
vllm_ascend/ascend_config.py
View File

@@ -18,15 +18,6 @@ from uuid import uuid4
from vllm.logger import logger
TORCHAIR_MODEL_LIST = ["deepseek", "pangu", "kimi_k2", "qwen"]
def _check_torchair_supported(model_type: str):
for supported_model in TORCHAIR_MODEL_LIST:
if supported_model in model_type.lower():
return True
return False
def check_kv_extra_config(vllm_config):
@@ -66,11 +57,6 @@ class AscendConfig:
def __init__(self, vllm_config):
additional_config = vllm_config.additional_config if vllm_config.additional_config is not None else {}
torchair_graph_config = additional_config.get("torchair_graph_config",
{})
self.torchair_graph_config = TorchairGraphConfig(
torchair_graph_config, vllm_config, additional_config)
xlite_graph_config = additional_config.get("xlite_graph_config", {})
self.xlite_graph_config = XliteGraphConfig(xlite_graph_config,
@@ -107,8 +93,8 @@ class AscendConfig:
self.chunked_prefill_for_mla = additional_config.get(
"chunked_prefill_for_mla", False)
self.enable_shared_expert_dp = additional_config.get(
"enable_shared_expert_dp", False
) and not self.torchair_graph_config.enabled and vllm_config.parallel_config.enable_expert_parallel
"enable_shared_expert_dp",
False) and vllm_config.parallel_config.enable_expert_parallel
if self.enable_shared_expert_dp:
from vllm_ascend.utils import enable_sp
assert enable_sp(vllm_config=vllm_config,
@@ -215,86 +201,6 @@ class AscendCompilationConfig:
# Add more compilation related configs here as needed
class TorchairGraphConfig:
"""
Configuration Object for torchair_graph_config from additional_config
"""
def __init__(self, torchair_graph_config, vllm_config, additional_config):
self.enabled = torchair_graph_config.get("enabled", False)
self.mode = torchair_graph_config.get("mode", '')
self.use_cached_graph = torchair_graph_config.get(
"use_cached_graph", False)
self.use_cached_kv_cache_bytes = torchair_graph_config.get(
"use_cached_kv_cache_bytes", False)
self.graph_batch_sizes = torchair_graph_config.get(
"graph_batch_sizes", [])
self.graph_batch_sizes_init = torchair_graph_config.get(
"graph_batch_sizes_init", False)
self.enable_multistream_mla = torchair_graph_config.get(
"enable_multistream_mla", False)
self.enable_view_optimize = torchair_graph_config.get(
"enable_view_optimize", True)
self.enable_frozen_parameter = torchair_graph_config.get(
"enable_frozen_parameter", True)
self.enable_kv_nz = torchair_graph_config.get("enable_kv_nz", False)
self.enable_super_kernel = torchair_graph_config.get(
"enable_super_kernel", False)
if not isinstance(self.graph_batch_sizes, list):
raise TypeError("graph_batch_sizes must be list[int]")
if self.graph_batch_sizes_init and len(self.graph_batch_sizes) > 0:
raise ValueError(
"graph_batch_sizes_init is only valid when graph_batch_sizes is empty"
)
if not self.enabled:
if self.mode:
raise RuntimeError(
"mode is valid only when Torchair graph mode is enabled")
if self.use_cached_graph:
raise RuntimeError(
"use_cached_graph is valid only when Torchair graph mode is enabled"
)
if self.use_cached_kv_cache_bytes:
raise RuntimeError(
"use_cached_kv_cache_bytes is valid only when Torchair graph mode is enabled"
)
if self.graph_batch_sizes:
raise RuntimeError(
"graph_batch_sizes is valid only when Torchair graph mode is enabled"
)
if self.graph_batch_sizes_init:
raise RuntimeError(
"graph_batch_sizes_init is valid only when Torchair graph mode is enabled"
)
if self.enable_multistream_mla:
raise RuntimeError(
"enable_multistream_mla is valid only when Torchair graph mode is enabled"
)
if self.enable_kv_nz:
raise RuntimeError(
"enable_kv_nz is valid only when Torchair graph mode is enabled"
)
if self.enable_super_kernel:
raise RuntimeError(
"enable_super_kernel is valid only when Torchair graph mode is enabled"
)
if self.enable_super_kernel:
if vllm_config.parallel_config.tensor_parallel_size != 1:
raise RuntimeError(
"enable_super_kernel is valid only when tensor_parallel_size is 1"
)
if not additional_config.get("multistream_overlap_shared_expert",
False):
raise RuntimeError(
"enable_super_kernel is valid only when multistream_overlap_shared_expert is enabled"
)
if self.use_cached_kv_cache_bytes and not self.use_cached_graph:
raise RuntimeError(
"use_cached_kv_cache_bytes is valid only when Torchair graph mode and use_cached_graph are enabled"
)
class XliteGraphConfig:
"""
Configuration Object for xlite_graph_config from additional_config
@@ -382,39 +288,7 @@ def get_ascend_config():
def check_ascend_config(vllm_config, enforce_eager):
ascend_config = get_ascend_config()
# for eager mode
if enforce_eager:
# torchair_graph cannot be enabled with eager mode.
if ascend_config.torchair_graph_config.enabled:
raise RuntimeError(
"Can't enable graph mode and eager mode at the same time. Please set `enforce_eager=False` if you attempt to enable NPU graph mode."
)
# for graph mode
else:
# torchair_graph case
if ascend_config.torchair_graph_config.enabled:
# torchair_graph is supported for deepseek/pangu/qwen model only.
if vllm_config.model_config:
model_type = vllm_config.model_config.hf_config.model_type
if not _check_torchair_supported(model_type):
raise NotImplementedError(
"Torchair graph mode only works with following model types:"
f"{TORCHAIR_MODEL_LIST}.")
if ascend_config.enable_shared_expert_dp:
logger.warning(
"enable_shared_expert_dp is not supported for torchair graph mode currently, "
"it has been disabled automatically.")
# aclgraph case
else:
if ascend_config.ascend_compilation_config.enable_quantization_fusion:
logger.info(
"Quantization fusion enabled! op fusion on quantization are expected. "
)
if vllm_config.model_config:
model_type = vllm_config.model_config.hf_config.model_type
if "qwen" not in model_type:
logger.warning(
"ACL Graph is currently experimental. Please "
"raise an issue on https://github.com/vllm-project/vllm-ascend/issues"
" if you encourage any Error")
if ascend_config.ascend_compilation_config.enable_quantization_fusion:
logger.info(
"Quantization fusion enabled! op fusion on quantization are expected. "
)