mirror of
https://github.com/PaddlePaddle/FastDeploy.git
synced 2025-10-05 00:33:03 +08:00
[Feature] support pool (#3827)
* support pool * update pooling * add pooler_config and check * update * support AutoWeightsLoader load weight * fix * update * delete print * update pre-commit * fix * fix xpu * fix ModelRegistry->model_registry * fix Copilot review * fix pooler.py * delete StepPooler * fix abstract * fix default_loader_v1 * fix Pre Commit * support torch qwen3 dense * add test and fix torch-qwen * fix * fix * adapter ci: * fix review * fix pooling_params.py * fix * fix tasks.py 2025 * fix print and logger * Modefy ModelRegistry and delete AutoWeightsLoader * fix logger * fix test_embedding * fix ci bug * ernie4_5 model_registry * fix test * support Qwen3-Embedding-0.6B tp=1 load * fix extra code * fix * delete fix vocab_size * delete prepare_params_dict * fix:
This commit is contained in:
lizexu123
182
tests/pooling/test_embedding.py
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182
tests/pooling/test_embedding.py
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# Copyright (c) 2025 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import sys
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import paddle
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import pytest
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from fastdeploy.config import (
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CacheConfig,
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FDConfig,
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GraphOptimizationConfig,
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LoadConfig,
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ModelConfig,
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ParallelConfig,
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)
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from fastdeploy.model_executor.models.model_base import ModelRegistry
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current_dir = os.path.dirname(os.path.abspath(__file__))
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project_root = os.path.abspath(os.path.join(current_dir, ".."))
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if project_root not in sys.path:
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sys.path.insert(0, project_root)
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from tests.model_loader.utils import get_torch_model_path
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class TestModelLoader:
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@pytest.fixture(scope="session", autouse=True)
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def setup_paddle(self):
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if not paddle.is_compiled_with_cuda():
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print("CUDA not available, using CPU")
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paddle.set_device("cpu")
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else:
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print("Using CUDA device")
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paddle.set_device("gpu")
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yield
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@pytest.fixture(scope="session")
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def model_path(self):
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try:
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torch_model_path = get_torch_model_path("Qwen3-0.6B")
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if os.path.exists(torch_model_path):
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return torch_model_path
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except Exception as e:
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print(f"Could not get torch model path: {e}")
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@pytest.fixture
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def model_config(self, model_path):
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model_args = {
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"model": model_path,
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"dtype": "bfloat16",
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"max_model_len": 8192,
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"tensor_parallel_size": 1,
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"runner": "auto",
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"convert": "auto",
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}
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try:
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return ModelConfig(model_args)
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except Exception as e:
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print(f"Could not create ModelConfig: {e}")
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@pytest.fixture
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def fd_config(self, model_config):
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try:
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cache_args = {
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"block_size": 64,
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"gpu_memory_utilization": 0.9,
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"cache_dtype": "bfloat16",
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"model_cfg": model_config,
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"tensor_parallel_size": 1,
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}
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cache_config = CacheConfig(cache_args)
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parallel_args = {
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"tensor_parallel_size": 1,
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"data_parallel_size": 1,
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}
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parallel_config = ParallelConfig(parallel_args)
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load_args = {}
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load_config = LoadConfig(load_args)
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graph_opt_args = {
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"enable_cudagraph": False,
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"cudagraph_capture_sizes": None,
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}
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graph_opt_config = GraphOptimizationConfig(graph_opt_args)
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return FDConfig(
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model_config=model_config,
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cache_config=cache_config,
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parallel_config=parallel_config,
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load_config=load_config,
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graph_opt_config=graph_opt_config,
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test_mode=True,
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)
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except Exception as e:
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print(f"Could not create FDConfig: {e}")
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@pytest.fixture
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def model_json_config(self, model_path):
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config_path = os.path.join(model_path, "config.json")
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if os.path.exists(config_path):
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with open(config_path, "r", encoding="utf-8") as f:
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return json.load(f)
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return None
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def test_embedding_with_none_convert_type(self, fd_config, model_json_config):
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if model_json_config is None:
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pytest.skip("Model config not available")
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if fd_config is None:
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pytest.skip("FDConfig not available")
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print("=" * 60)
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print("Testing initialize_model with convert_type='none'")
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print("=" * 60)
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architectures = model_json_config.get("architectures", [])
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if not architectures:
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pytest.skip("No architectures found in model config")
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fd_config.model_config.convert_type = "none"
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try:
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model_cls = ModelRegistry.get_class(architectures)
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if hasattr(model_cls, "__name__"):
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assert (
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"ForEmbedding" not in model_cls.__name__
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), f"Standard model should not have 'ForEmbedding' in name, but got: {model_cls.__name__}"
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print(f"Confirmed standard model type (no ForEmbedding): {model_cls.__name__}")
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standard_methods = set(dir(model_cls))
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assert "_init_pooler" not in standard_methods, "Standard model should not have _init_pooler method"
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except Exception as e:
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print(f"Error in none: {e}")
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def test_embedding_with_embed_convert_type(self, fd_config, model_json_config):
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if model_json_config is None:
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pytest.skip("Model config not available")
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if fd_config is None:
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pytest.skip("FDConfig not available")
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print("=" * 60)
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print("Testing embedding with convert_type='embed'")
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print("=" * 60)
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architectures = model_json_config.get("architectures", [])
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if not architectures:
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pytest.skip("No architectures found in model config")
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fd_config.model_config.convert_type = "embed"
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try:
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model_cls = ModelRegistry.get_class(architectures)
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if hasattr(model_cls, "__name__"):
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assert "ForEmbedding" in model_cls.__name__, "Embedding model should have 'ForEmbedding' in name"
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print(f"Confirmed embedding model type: {model_cls.__name__}")
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embedding_methods = set(dir(model_cls))
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assert "_init_pooler" in embedding_methods, "Embedding model should have _init_pooler method"
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except Exception as e:
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print(f"Error in convert embed: {e}")
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