wehub-resource-sync
91 lines
2.9 KiB
Python
91 lines
2.9 KiB
Python
# Copyright (c) 2024 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 sys
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import unittest
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from os.path import dirname
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import numpy as np
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import paddle
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import paddle.nn.functional as F
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from paddle import nn
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from paddle.static import InputSpec
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sys.path.append(dirname(dirname(__file__)))
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import utils
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class LlamaWhile(nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, logits, input_ids):
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batch_size, cur_len = paddle.shape(input_ids)
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unfinished_flag = paddle.full([batch_size, 1], True, dtype="float32")
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max_new_tokens = paddle.full([1], 16, dtype="int64")
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while cur_len < max_new_tokens and paddle.any(unfinished_flag):
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# [batch_size, vocab_size]
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probs = F.softmax(logits[:, -1, :])
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# compute next_tokens
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top_ps_tensor = paddle.full(
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shape=[paddle.shape(probs)[0], 1],
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fill_value=0,
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dtype=probs.dtype,
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)
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_, next_tokens = paddle.tensor.top_p_sampling(probs, top_ps_tensor)
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input_ids = paddle.concat([input_ids, next_tokens], axis=1)
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cur_len += 1
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return input_ids
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class TestLlamaPostProcess(unittest.TestCase):
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def setUp(self):
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paddle.seed(2024)
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self.prepare_data()
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def prepare_data(self):
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self.logits = paddle.randn([1, 256, 3200], dtype="float32")
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self.input_ids = paddle.randint(0, 512, [1, 8], dtype="int64")
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def check_jit_kernel_info(self, static_fn):
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utils.check_jit_kernel_number(static_fn, 1)
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utils.check_jit_kernel_structure(static_fn, {utils.JIT_KERNEL_NAME: 1})
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def eval(self, use_cinn):
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paddle.seed(2024)
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net = LlamaWhile()
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input_spec = [
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InputSpec(shape=[None, None, 3200], dtype='float32'), # logits
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InputSpec(shape=[None, None], dtype='int64'), # input_ids
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]
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net = utils.apply_to_static(net, use_cinn, input_spec)
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net.eval()
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out = net(self.logits, self.input_ids)
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return out
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@unittest.skip("TODO: xiongkun")
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def test_eval(self):
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dy_out = self.eval(use_cinn=False)
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cinn_out = self.eval(use_cinn=True)
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np.testing.assert_allclose(
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cinn_out.numpy(), dy_out.numpy(), atol=1e-6, rtol=1e-6
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)
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if __name__ == '__main__':
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unittest.main()
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