wehub-resource-sync
45 lines
1.9 KiB
Python
45 lines
1.9 KiB
Python
"""模型组网正确性验证
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【基本流程】
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定义原模型，加载权重，固定seed，基于numpy生成随机数，转换为PyTorch可以处理的tensor，送入网络，获取输出。
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定义模块化转换后modeling模型，加载权重，固定seed，基于numpy生成随机数，转换为PaddlePaddle可以处理的tensor，送入网络，获取输出。
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排查diff，小于阈值，即可完成自测。
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"""
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import numpy as np
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import paddle
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from paddleformers.transformers.qwen2 import Qwen2Config
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from paddleformers.transformers.qwen2.modeling import Qwen2ForCausalLM
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from paddleformers.transformers import Qwen2Config as Qwen2Config_hf
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from paddleformers.transformers import Qwen2ForCausalLM as Qwen2ForCausalLM_hf
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#from paddleformers.transformers.qwen2.test_model_expanded import Qwen2ForCausalLM as Qwen2ForCausalLM_hf
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def eval_model_convert():
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paddle_input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
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torch_input_ids = paddle.to_tensor([[0, 345, 232, 328, 740, 140, 1695, 69, 6078, 1588, 2]])
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# paddle model
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paddle_ckpt_path = "Qwen/Qwen2-0.5B"
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config_paddle = Qwen2Config.from_pretrained(paddle_ckpt_path)
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model_paddle = Qwen2ForCausalLM.from_pretrained(paddle_ckpt_path, config=config_paddle, dtype="float32")
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# torch model
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torch_ckpt_path = "Qwen/Qwen2-0.5B"
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config_torch = Qwen2Config_hf.from_pretrained(torch_ckpt_path)
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config_torch.dtype = "float32"
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model_torch = Qwen2ForCausalLM_hf.from_pretrained(torch_ckpt_path, config=config_torch, dtype="float32")
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model_paddle.eval()
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model_torch.eval()
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out_paddle = model_paddle(paddle_input_ids)[0]
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out_torch = model_torch(torch_input_ids, return_dict=False)[0]
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print(out_paddle)
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print(out_torch)
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assert np.allclose(out_paddle.numpy(), out_torch.detach().numpy(), rtol=1e-5, atol=1e-3)
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eval_model_convert() |