8952fddc7e
### What this PR does / why we need it? This PR implements the `AscendW8A8DynamicLinearMethod310` quantization scheme specifically for 310P hardware. It includes the logic for weight retrieval, per-channel parameter generation, and the application of dynamic quantization using NPU-specific kernels. Additionally, it updates `ShardedStateLoader310` to handle quantization configurations more robustly when generating parameter type maps. Feedback from the review identified two critical issues in the implementation: 1. The tensor squeezing logic in the `apply` method incorrectly handles 2D inputs, which may lead to shape mismatches in subsequent layers. 2. The weight tensor in `process_weights_after_loading` is transposed after being converted to the private NZ format; the transpose operation should be performed on the ND tensor before conversion to ensure correct physical layout. cherry-pick from : #7546 #7725 ### Does this PR introduce _any_ user-facing change? No. ### How was this patch tested? New unit tests were added in `tests/ut/_310p/quantization/test_w8a8_dynamic_310.py` to verify the quantization method, and `tests/ut/_310p/test_sharded_state_loader_310p.py` was updated to test the state loader changes. --------- Signed-off-by: csoulnd <daidaicurry@foxmail.com>
81 lines
2.9 KiB
Python
81 lines
2.9 KiB
Python
#
|
|
# Copyright (c) 2026 Huawei Technologies Co., Ltd. All Rights Reserved.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# http://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
# This file is a part of the vllm-ascend project.
|
|
#
|
|
import json
|
|
import os
|
|
from pathlib import Path
|
|
|
|
import torch
|
|
from vllm.config.load import LoadConfig
|
|
from vllm.model_executor.layers.quantization.base_config import QuantizationConfig
|
|
from vllm.model_executor.model_loader import ShardedStateLoader
|
|
|
|
|
|
class ShardedStateLoader310(ShardedStateLoader):
|
|
def __init__(self, load_config: LoadConfig):
|
|
super().__init__(load_config)
|
|
|
|
@staticmethod
|
|
def save_model(
|
|
model: torch.nn.Module,
|
|
path: str,
|
|
pattern: str | None = None,
|
|
max_size: int | None = None,
|
|
) -> None:
|
|
from safetensors.torch import save_file
|
|
from vllm.distributed import get_tensor_model_parallel_rank
|
|
|
|
rank = get_tensor_model_parallel_rank()
|
|
part_idx = 0
|
|
state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
|
|
|
|
filename = ShardedStateLoader.DEFAULT_PATTERN.format(rank=rank, part=part_idx)
|
|
save_file(
|
|
state_dict,
|
|
os.path.join(path, filename),
|
|
)
|
|
|
|
@staticmethod
|
|
def generate_quant_description(
|
|
model: torch.nn.Module,
|
|
path: str,
|
|
quant_config: QuantizationConfig | None = None,
|
|
) -> None:
|
|
"""Generate a mapping of parameter names to their corresponding quantization types."""
|
|
quant_description = {}
|
|
if quant_config is None:
|
|
quantize_type = "FLOAT"
|
|
else:
|
|
try:
|
|
quantize_type = quant_config.quant_description.get("model_quant_type", "FLOAT")
|
|
except AttributeError:
|
|
quantize_type = "FLOAT"
|
|
quant_description["model_quant_type"] = quantize_type
|
|
quant_description["version"] = "1.0.0"
|
|
state_dict = ShardedStateLoader._filter_subtensors(model.state_dict())
|
|
for name, tensor in state_dict.items():
|
|
if name.endswith(".weight") or name.endswith(".bias"):
|
|
if tensor.dtype in [torch.int8, torch.int32, torch.int64]:
|
|
quant_description[name] = quantize_type
|
|
else:
|
|
quant_description[name] = "FLOAT"
|
|
else:
|
|
quant_description[name] = "FLOAT"
|
|
|
|
json_path = Path(path) / "parameters_type_map.json"
|
|
with json_path.open("w", encoding="utf-8") as f:
|
|
json.dump(quant_description, f, indent=2)
|