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Fix the bugs about operator registration by PyTorch Dispatcher (#2786)

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**Background:**

There are two principles about operator registration in PyTorch
- The same namespace can be only registered once by `TORCH_LIBRARY`
- The operator signatures can be only registered once by `def`

Considering that all custom operators defined in the current repo are
only used by Ascend, instead of defining a common operator schema by
vLLM, all accelerators then follow this operator schema and complete the
implementation based on their respective hardware, which is conducive to
functional abstraction.

Therefore, we can rename the operator registration namespace to an
Ascend-specific namespace(**_C_ascend**).

Related ISSUE: https://github.com/vllm-project/vllm-ascend/issues/2742


- vLLM version: main
- vLLM main:
f592b3174b

Signed-off-by: FFFrog <ljw1101.vip@gmail.com>
This commit is contained in:
Jiawei Li
2025-09-13 11:58:52 +08:00
committed by GitHub
parent 138e932630
commit e57cca971c
16 changed files with 97 additions and 65 deletions
Download Patch File Download Diff File Expand all files Collapse all files

2
benchmarks/ops/ben_vocabparallelembedding.py
View File

@ -112,7 +112,7 @@ def test_get_masked_input_and_mask(
# Define custom function
def custom_fn():
return torch.ops._C.get_masked_input_and_mask(
return torch.ops._C_ascend.get_masked_input_and_mask(
input_tensor,
test_case["org_start"],
test_case["org_end"],

28
csrc/torch_binding.cpp
View File

@ -141,7 +141,7 @@ std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
TP2, rank 1:
|< -----------BASE----------- >|< -BASE PADDING- >|< -----------LORA PADDING----------- >|
corresponding token_id: | 512 | 513 | 514 | ... | 1009 | -1 | ... | -1 | -1 | ... | -1 | -1 | ... | -1 |
index: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 512 | ... | 519 | 520 | ... | 543 |
index: | 0 | 1 | 2 | ... | 497 | 498 | ... | 511 | 512 | ... | 519 | 520 | ... | 543 |
Parameters:
org_vocab_start_index //base embeddings start
org_vocab_end_index //base embeddings end
@ -164,22 +164,22 @@ std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
// Create output tensors
at::Tensor masked_input = at::empty_like(input);
at::Tensor mask = at::empty_like(input).to(at::kBool);
// Get data pointers
void *input_ptr = input.data_ptr();
void *masked_input_ptr = masked_input.data_ptr();
void *mask_ptr = mask.data_ptr();
// Get current stream
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
// Get scalar type
at::ScalarType scalar_type = input.scalar_type();
// Create and configure OpCommand
at_npu::native::OpCommand cmd;
cmd.Name("get_masked_input_and_mask");
cmd.SetCustomHandler([scalar_type, size, stream,
cmd.SetCustomHandler([scalar_type, size, stream,
input_ptr, masked_input_ptr, mask_ptr,
org_vocab_start_index, org_vocab_end_index,
num_org_vocab_padding, added_vocab_start_index,
@ -193,7 +193,7 @@ std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
get_masked_input_and_mask_impl(
stream,
input_ptr,
masked_input_ptr,
masked_input_ptr,
mask_ptr,
org_vocab_start_index,
org_vocab_end_index,
@ -203,7 +203,7 @@ std::tuple<at::Tensor, at::Tensor> get_masked_input_and_mask(
size,
loop_cnt,
aiv_num);
return 0;
});
cmd.Run();
@ -320,8 +320,8 @@ void sgmv_shrink(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indices, at
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("sgmv_shrink");
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size,
seq_len_ptr, seq_len_size, y_ptr,
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size,
seq_len_ptr, seq_len_size, y_ptr,
batch_size, input_hidden_token, lora_rank, scale_f]() -> int {
auto dtype = get_dtype_from_torch(scalar_type);
int device_id = 0;
@ -330,7 +330,7 @@ void sgmv_shrink(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indices, at
int num_tokens_per_core = (batch_size + aiv_num - 1) / aiv_num;
TORCH_CHECK("num_tokens_per_core != 0", "num_tokens_per_core should not be 0");
sgmv_shrink_impl(dtype, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size,
y_ptr, batch_size,
y_ptr, batch_size,
num_tokens_per_core, input_hidden_token, lora_rank, scale_f);
return 0;
});
@ -367,7 +367,7 @@ at::Tensor sgmv_expand(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indic
aclrtStream stream = c10_npu::getCurrentNPUStream().stream();
at_npu::native::OpCommand cmd;
cmd.Name("sgmv_expand");
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
cmd.SetCustomHandler([scalar_type, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
batch_size, lora_rank, slice_offset, slice_size, output_full_dim]() -> int {
auto dtype = get_dtype_from_torch(scalar_type);
int device_id = 0;
@ -375,7 +375,7 @@ at::Tensor sgmv_expand(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indic
TORCH_CHECK(aclGetDeviceCapability(device_id, ACL_DEVICE_INFO_VECTOR_CORE_NUM, &aiv_num) == ACL_SUCCESS);
int num_tokens_per_core = (batch_size + aiv_num - 1) / aiv_num;
TORCH_CHECK("num_tokens_per_core != 0", "num_tokens_per_core should not be 0");
sgmv_expand_impl(dtype, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
sgmv_expand_impl(dtype, stream, x_ptr, weight_ptr, lora_indices_ptr, lora_indices_size, seq_len_ptr, seq_len_size, y_ptr, y_out_ptr,
batch_size, num_tokens_per_core, lora_rank, slice_size, slice_offset, output_full_dim);
return 0;
});
@ -384,7 +384,7 @@ at::Tensor sgmv_expand(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_indic
}
} // namespace vllm_ascend
TORCH_LIBRARY_EXPAND(_C, ops)
TORCH_LIBRARY_EXPAND(CONCAT(_C, _ascend), ops)
{
// vLLM-Ascend custom ops
ops.def("weak_ref_tensor(Tensor input) -> Tensor");

8
csrc/torch_binding_meta.cpp
View File

@ -40,7 +40,7 @@ std::tuple<at::Tensor, at::Tensor> rotary_embedding_meta(
at::Tensor &positions,
at::Tensor &query,
at::Tensor &key,
int64_t head_size,
int64_t head_size,
at::Tensor &cos_sin_cache,
bool is_neox) {
auto num_tokens = positions.sym_numel();
@ -86,9 +86,9 @@ at::Tensor sgmv_expand_meta(at::Tensor &x, at::Tensor &weight, at::Tensor &lora_
} // namespace vllm_ascend
namespace {
// Register the meta implementations of the custom kernels for symbolic tracing, this will also
// Register the meta implementations of the custom kernels for symbolic tracing, this will also
// the custom kernel been captured into aclgraph
TORCH_LIBRARY_IMPL_EXPAND(_C, Meta, ops) {
TORCH_LIBRARY_IMPL_EXPAND(CONCAT(_C, _ascend), Meta, ops) {
// Rotary embedding meta implementation
ops.impl("rotary_embedding", &vllm_ascend::meta::rotary_embedding_meta);
// Masked input and mask meta implementation
@ -99,4 +99,4 @@ namespace {
ops.impl("sgmv_expand", &vllm_ascend::meta::sgmv_expand_meta);
}
}
}

4
tests/e2e/singlecard/ops/test_bgmv_expand.py
View File

@ -33,8 +33,8 @@ def test_bgmv_expand():
y_npu = y.npu()
y_out = bgmv_expand_cpu_impl(x, w, indices, y, 0, 128)
y_out_npu = torch.ops._C.bgmv_expand(x_npu, w_npu, indices_npu, y_npu, 0,
128)
y_out_npu = torch.ops._C_ascend.bgmv_expand(x_npu, w_npu, indices_npu,
y_npu, 0, 128)
# Compare the results.
torch.testing.assert_close(y_out_npu.cpu(),

2
tests/e2e/singlecard/ops/test_bgmv_shrink.py
View File

@ -33,7 +33,7 @@ def test_bgmv_shrink():
y_npu = y.npu()
y = bgmv_shrink_cpu_impl(x, w, indices, y, 0.5)
torch.ops._C.bgmv_shrink(x_npu, w_npu, indices_npu, y_npu, 0.5)
torch.ops._C_ascend.bgmv_shrink(x_npu, w_npu, indices_npu, y_npu, 0.5)
# Compare the results.
torch.testing.assert_close(y_npu.cpu(),

6
tests/e2e/singlecard/ops/test_rotary_embedding.py
View File

@ -182,7 +182,7 @@ def test_rotary_embedding_quant_with_leading_dim(
)
ref_query, ref_key = rope.forward_native(positions, query, key)
query, key = torch.ops._C.rotary_embedding(
query, key = torch.ops._C_ascend.rotary_embedding(
positions,
query,
key,
@ -239,7 +239,7 @@ class ModelwithRotaryEmbedding(nn.Module):
# we simulated a simple attention layer to test if it can be seamlessly captured into aclgraph
qkv = self.qkv_proj(hidden_states)
q, k, v = qkv.chunk(3, dim=-1)
query, key = torch.ops._C.rotary_embedding(
query, key = torch.ops._C_ascend.rotary_embedding(
positions,
q,
k,
@ -299,7 +299,7 @@ def test_capture_rotary_embedding_in_aclgraph(
# Validate if the rotary_embedding custom kernel is indeed inside the graph by
# string match
graph = str(gm.graph)
assert "_C.rotary_embedding" in graph
assert "_C_ascend.rotary_embedding" in graph
return gm
static_positions = torch.randint(0, max_position_embeddings,

2
tests/e2e/singlecard/ops/test_vocabparallelembedding.py
View File

@ -72,7 +72,7 @@ def test_get_masked_input_and_mask(
# Get custom op result
print("input_tensor:", input_tensor)
custom_masked_input, custom_mask = torch.ops._C.get_masked_input_and_mask(
custom_masked_input, custom_mask = torch.ops._C_ascend.get_masked_input_and_mask(
input_tensor, test_case["org_start"], test_case["org_end"],
test_case["padding"], test_case["added_start"], test_case["added_end"])

2
tests/ut/ops/test_rotary_embedding.py
View File

@ -94,7 +94,7 @@ class TestAscendRotaryEmbedding(unittest.TestCase):
self.mock_self.cos_sin_cache = self.cos_sin_cache
self.mock_self.is_neox_style = self.is_neox_style
@patch('torch.ops._C')
@patch('torch.ops._C_ascend')
@patch('vllm_ascend.ops.rotary_embedding.is_310p', return_value=False)
@patch('vllm_ascend.ops.rotary_embedding._custom_rotary_embedding_enabled',
return_value=True)

2
tests/ut/torchair/ops/test_torchair_rotary_embedding.py
View File

@ -104,7 +104,7 @@ class TestRopeForwardOot(TestBase):
self.assertTrue(torch.equal(result_q, self.query))
self.assertTrue(torch.equal(result_k, self.key))
@patch('torch.ops._C')
@patch('torch.ops._C_ascend')
@patch(
'vllm_ascend.torchair.ops.torchair_rotary_embedding.get_ascend_config')
@patch('vllm_ascend.torchair.ops.torchair_rotary_embedding.is_310p',

13
vllm_ascend/compilation/acl_graph.py
View File

@ -15,7 +15,8 @@ from vllm.config import CUDAGraphMode, VllmConfig
from vllm.forward_context import BatchDescriptor, get_forward_context
from vllm.logger import logger
from vllm.platforms import current_platform
from vllm.utils import weak_ref_tensors
from ..utils import weak_ref_tensors
@dataclasses.dataclass
@ -35,10 +36,10 @@ class ACLGraphWrapper:
The workflow of this wrapper in the aclgraph dispatching is as follows:
1. At initialization, a runtime mode is assigned to the wrapper (FULL or
PIECEWISE).
2. At runtime, the wrapper receives a runtime_mode and a
PIECEWISE).
2. At runtime, the wrapper receives a runtime_mode and a
batch_descriptor(key) from the forward context and blindly trust them
for aclgraph dispatching.
for aclgraph dispatching.
3. If runtime_mode is NONE or runtime_mode does not match the mode of the
wrapper, just call the runnable directly.
4. Otherwise, i.e., the runtime_mode matches the mode of the wrapper,
@ -47,9 +48,9 @@ class ACLGraphWrapper:
Note: ACLGraphWrapper does not store persistent buffers or copy any
runtime inputs into that buffers for replay. We assume implementing them
is done outside of the wrapper. That is because we do not make any
is done outside of the wrapper. That is because we do not make any
assumption on the dynamic shape (batch size) of the runtime inputs, as a
trade-off for staying orthogonal to compilation logic. Nevertheless,
trade-off for staying orthogonal to compilation logic. Nevertheless,
tracing and checking the input addresses to be consistent during replay is
guaranteed when VLLM_LOGGING_LEVEL == "DEBUG".
"""

25
vllm_ascend/lora/lora_ops.py
View File

@ -21,7 +21,7 @@ def bgmv_shrink(inputs: torch.Tensor,
output_tensor: torch.Tensor,
lora_indices_tensor: torch.Tensor,
scaling: float = 1.0):
return torch.ops._C.bgmv_shrink(
return torch.ops._C_ascend.bgmv_shrink(
inputs,
lora_a_weights,
lora_indices_tensor,
@ -35,7 +35,7 @@ def bgmv_expand(inputs: torch.Tensor,
output_tensor: torch.Tensor,
lora_indices_tensor: torch.Tensor,
add_inputs: bool = True):
return torch.ops._C.bgmv_expand(
return torch.ops._C_ascend.bgmv_expand(
inputs,
lora_b_weights,
lora_indices_tensor,
@ -52,9 +52,9 @@ def bgmv_expand_slice(inputs: torch.Tensor,
slice_offset: int,
slice_size: int,
add_inputs: bool = True):
return torch.ops._C.bgmv_expand(inputs, lora_b_weights,
lora_indices_tensor, output_tensor,
slice_offset, slice_size)
return torch.ops._C_ascend.bgmv_expand(inputs, lora_b_weights,
lora_indices_tensor, output_tensor,
slice_offset, slice_size)
def sgmv_shrink(
@ -69,9 +69,9 @@ def sgmv_shrink(
token_nums: int,
scaling: float,
):
return torch.ops._C.sgmv_shrink(inputs, lora_a_weights,
lora_indices_tensor, seq_len_tensor,
output_tensor, scaling)
return torch.ops._C_ascend.sgmv_shrink(inputs, lora_a_weights,
lora_indices_tensor, seq_len_tensor,
output_tensor, scaling)
def sgmv_expand(inputs: torch.Tensor,
@ -84,7 +84,7 @@ def sgmv_expand(inputs: torch.Tensor,
max_seq_length: int,
token_nums: int,
add_inputs: bool = False):
return torch.ops._C.sgmv_expand(
return torch.ops._C_ascend.sgmv_expand(
inputs,
lora_b_weights,
lora_indices_tensor,
@ -107,6 +107,7 @@ def sgmv_expand_slice(inputs: torch.Tensor,
slice_offset: int,
slice_size: int,
add_inputs: bool = False):
return torch.ops._C.sgmv_expand(inputs, lora_b_weights,
lora_indices_tensor, seq_len_tensor,
output_tensor, slice_offset, slice_size)
return torch.ops._C_ascend.sgmv_expand(inputs, lora_b_weights,
lora_indices_tensor, seq_len_tensor,
output_tensor, slice_offset,
slice_size)

13
vllm_ascend/meta_registration.py
View File

@ -23,7 +23,7 @@ from torch.library import Library
# Do NOT perform any real computation or allocate device memory.
#
# 2. Register your meta function using `register_meta_if_necessary`, providing:
# - The namespace (usually "_C" for custom ops)
# - The namespace (usually "_C_ascend" for custom ops)
# - The operator name (as registered in C++)
# - The Python meta function
# - (Optional) The overload name, if your op has overloads
@ -39,7 +39,7 @@ from torch.library import Library
#
# For more details, see: https://pytorch.org/docs/stable/notes/extending.html#meta-tensors
lib = Library("_C", "IMPL")
lib = Library("_C_ascend", "IMPL")
def register_meta_if_necessary(ns: str, op_name: str, fn, overload: str = ""):
@ -97,8 +97,9 @@ def sgmv_expand_meta(x: torch.Tensor, weight: torch.Tensor,
return y_out
register_meta_if_necessary("_C", "rotary_embedding", rotary_embedding_meta)
register_meta_if_necessary("_C", "get_masked_input_and_mask",
register_meta_if_necessary("_C_ascend", "rotary_embedding",
rotary_embedding_meta)
register_meta_if_necessary("_C_ascend", "get_masked_input_and_mask",
get_masked_input_and_mask_meta)
register_meta_if_necessary("_C", "bgmv_expand", bgmv_expand_meta)
register_meta_if_necessary("_C", "sgmv_expand", sgmv_expand_meta)
register_meta_if_necessary("_C_ascend", "bgmv_expand", bgmv_expand_meta)
register_meta_if_necessary("_C_ascend", "sgmv_expand", sgmv_expand_meta)

17
vllm_ascend/ops/__init__.py
View File

@ -35,19 +35,20 @@ class dummyFusionOp:
def register_dummy_fusion_op() -> None:
torch.ops._C.rms_norm = dummyFusionOp(name="rms_norm")
torch.ops._C.fused_add_rms_norm = dummyFusionOp(name="fused_add_rms_norm")
torch.ops._C.static_scaled_fp8_quant = dummyFusionOp(
torch.ops._C_ascend.rms_norm = dummyFusionOp(name="rms_norm")
torch.ops._C_ascend.fused_add_rms_norm = dummyFusionOp(
name="fused_add_rms_norm")
torch.ops._C_ascend.static_scaled_fp8_quant = dummyFusionOp(
name="static_scaled_fp8_quant")
torch.ops._C.dynamic_scaled_fp8_quant = dummyFusionOp(
torch.ops._C_ascend.dynamic_scaled_fp8_quant = dummyFusionOp(
name="dynamic_scaled_fp8_quant")
torch.ops._C.dynamic_per_token_scaled_fp8_quant = dummyFusionOp(
torch.ops._C_ascend.dynamic_per_token_scaled_fp8_quant = dummyFusionOp(
name="dynamic_per_token_scaled_fp8_quant")
torch.ops._C.rms_norm_static_fp8_quant = dummyFusionOp(
torch.ops._C_ascend.rms_norm_static_fp8_quant = dummyFusionOp(
name="rms_norm_static_fp8_quant")
torch.ops._C.fused_add_rms_norm_static_fp8_quant = dummyFusionOp(
torch.ops._C_ascend.fused_add_rms_norm_static_fp8_quant = dummyFusionOp(
name="fused_add_rms_norm_static_fp8_quant")
torch.ops._C.rms_norm_dynamic_per_token_quant = dummyFusionOp(
torch.ops._C_ascend.rms_norm_dynamic_per_token_quant = dummyFusionOp(
name="rms_norm_dynamic_per_token_quant")

2
vllm_ascend/ops/rotary_embedding.py
View File

@ -49,7 +49,7 @@ def _rope_forward_oot(
# adopt custom kernel path for rotary_embedding
if _custom_rotary_embedding_enabled(query, is_neox_style,
self.head_size) and not is_310p():
query, key = torch.ops._C.rotary_embedding(
query, key = torch.ops._C_ascend.rotary_embedding(
positions,
query,
key,

2
vllm_ascend/torchair/ops/torchair_rotary_embedding.py
View File

@ -62,7 +62,7 @@ def rope_forward_oot(
# adopt custom kernel path for rotary_embedding
if custom_rotary_embedding_enabled(query, neox_style,
self.head_size) and not is_310p():
query, key = torch.ops._C.rotary_embedding(
query, key = torch.ops._C_ascend.rotary_embedding(
positions,
query,
key,

34
vllm_ascend/utils.py
View File

@ -24,7 +24,7 @@ import os
from contextlib import contextmanager
from enum import Enum
from threading import Lock
from typing import TYPE_CHECKING, List, Optional, Tuple
from typing import TYPE_CHECKING, Any, List, Optional, Tuple, Union
import torch
import torch_npu # noqa: F401 # noqa: F401
@ -188,7 +188,7 @@ def try_register_lib(lib_name: str, lib_info: str = ""):
def enable_custom_op():
"""
Enable lazy init for vllm_ascend_C to avoid early initialization of CANN's RTS component.
Enable lazy init for vllm_ascend_C to avoid early initialization of CANN's RTS component.
Ensure that ASCEND_RT_VISIBLE_DEVICES can be dynamically modified before torch.npu.set_device().
"""
global _CUSTOM_OP_ENABLED
@ -486,7 +486,7 @@ def get_all_reduce_merge_state(ep_size: int, is_deepseek_v3_r1: bool):
def register_ascend_customop(vllm_config: Optional[VllmConfig] = None):
"""Register Ascend CustomOP
NOTE: if the register branch requires model type, please use `vllm.config.get_current_vllm_config`,
NOTE: if the register branch requires model type, please use `vllm.config.get_current_vllm_config`,
and ensure this will execute after model config is initilazed.
"""
global _ASCEND_CUSTOMOP_IS_REIGISTERED
@ -589,3 +589,31 @@ def dense_optim_enable() -> bool:
def is_moe_model(vllm_config: VllmConfig):
config = vllm_config.model_config.hf_config
return any('experts' in key.lower() for key in config.to_dict())
def weak_ref_tensor(tensor: Any) -> Any:
"""
Create a weak reference to a tensor.
The new tensor will share the same data as the original tensor,
but will not keep the original tensor alive.
"""
if isinstance(tensor, torch.Tensor):
return torch.ops._C_ascend.weak_ref_tensor(tensor)
else:
return tensor
def weak_ref_tensors(
tensors: Union[torch.Tensor, list[torch.Tensor], tuple[torch.Tensor]]
) -> Union[torch.Tensor, list[Any], tuple[Any], Any]:
"""
Convenience function to create weak references to tensors,
for single tensor, list of tensors or tuple of tensors.
"""
if isinstance(tensors, torch.Tensor):
return weak_ref_tensor(tensors)
if isinstance(tensors, list):
return [weak_ref_tensor(t) for t in tensors]
if isinstance(tensors, tuple):
return tuple(weak_ref_tensor(t) for t in tensors)
raise ValueError("Invalid type for tensors")