mirror of
https://github.com/PaddlePaddle/FastDeploy.git
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chen
29a313a402
* support FA4 sm100 * flash attn backend support mask * flash attn backend run flashmask correct * add test for flash_attn_backend and flash_attn_func * check * add test for fa4 * requirements.txt add fa4 whl * check test on sm100 * fix CI conflict * add enable_torch_proxy for flash_mask * lazy import fa4 * check * fix tests import * check test_load_mpt import
84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
"""
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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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"""
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import sys
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import unittest
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from pathlib import Path
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from unittest.mock import Mock
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import numpy as np
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import paddle
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import paddle.distributed.fleet as fleet
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from fastdeploy.model_executor.layers.embeddings import VocabParallelEmbedding
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from fastdeploy.model_executor.models.ernie4_5_mtp import Ernie4_5_MTPForCausalLM
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ROOT = Path(__file__).resolve().parents[1]
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sys.path.insert(0, str(ROOT))
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from utils import get_default_test_fd_config
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strategy = fleet.DistributedStrategy()
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fleet.init(strategy=strategy)
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class TestErnie4_5_MTPLoadWeights(unittest.TestCase):
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def setUp(self):
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self.fd_config = get_default_test_fd_config()
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self.fd_config.speculative_config = Mock()
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self.fd_config.speculative_config.sharing_model = Mock()
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self.fd_config.speculative_config.sharing_model.ernie = Mock()
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self.fd_config.parallel_config.tp_group = None
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self.fd_config.speculative_config.sharing_model.ernie.embed_tokens = VocabParallelEmbedding(
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fd_config=self.fd_config,
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num_embeddings=self.fd_config.model_config.vocab_size,
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embedding_dim=self.fd_config.model_config.hidden_size,
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params_dtype=paddle.get_default_dtype,
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prefix=("embed_tokens"),
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)
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self.fd_config.speculative_config.sharing_model.ernie.lm_head = Mock()
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self.model = Ernie4_5_MTPForCausalLM(self.fd_config)
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def test_load_weights_normal_case(self):
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weights_iterator = [
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("ernie.embed_tokens.weight", paddle.rand([32000, 768], dtype="float32")),
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("ernie.mtp_block.0.self_attn.qkv_proj.weight", paddle.rand([768, 768 * 3], dtype="float32")),
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]
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for k, v in self.model.named_parameters():
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print("{}".format(k))
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self.model.load_weights(iter(weights_iterator))
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self.assertTrue(np.allclose(self.model.ernie.embed_tokens.embeddings.weight.numpy(), weights_iterator[0][1]))
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def test_load_weights_with_unexpected_keys(self):
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weights_iterator = [
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("unknown_key", paddle.rand([10, 10], dtype="float32")),
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("ernie.embed_tokens.weight", paddle.rand([32000, 768], dtype="float32")),
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]
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self.model.load_weights(iter(weights_iterator))
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self.assertTrue(np.allclose(self.model.ernie.embed_tokens.embeddings.weight.numpy(), weights_iterator[1][1]))
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def test_load_weights_empty_iterator(self):
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weights_iterator = []
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self.model.load_weights(iter(weights_iterator))
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if __name__ == "__main__":
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unittest.main()
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