wehub-resource-sync
107 lines
3.3 KiB
Python
107 lines
3.3 KiB
Python
# Copyright (c) 2023 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 unittest
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import numpy as np
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import paddle
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from paddle.framework import core
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from paddle.static import InputSpec
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def apply_to_static(net, use_cinn, input_spec=None):
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backend = "CINN" if use_cinn else None
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return paddle.jit.to_static(
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net,
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input_spec=input_spec,
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backend=backend,
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full_graph=True,
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)
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def rms_norm1(hidden_states, weight):
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# From llama2, reduce dim is not equal to dynamic shape dim
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variance = hidden_states.pow(2).mean(-1, keepdim=True)
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hidden_states = paddle.rsqrt(variance + 1e-5) * hidden_states
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return hidden_states * weight
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def rms_norm2(hidden_states, weight):
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# reduce dim is not equal to dynamic shape dim
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variance = hidden_states.pow(2).mean((0, 1), keepdim=True)
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hidden_states = paddle.rsqrt(variance + 1e-5) * hidden_states
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return hidden_states * weight
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class TestPrimMode1(unittest.TestCase):
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def setUp(self):
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np.random.seed(2023)
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self.shape_x = [1, 300, 4096]
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self.shape_y = [4096]
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self.x = np.random.random(self.shape_x).astype("float32")
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self.y = np.random.random(self.shape_y).astype("float32")
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self.net = rms_norm1
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self.enable_cinn = False
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def base_net(self, flag=None):
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x = paddle.to_tensor(self.x)
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y = paddle.to_tensor(self.y)
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if flag == "prim":
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core._set_prim_all_enabled(True)
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fn = apply_to_static(
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self.net,
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use_cinn=self.enable_cinn,
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input_spec=[
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InputSpec(shape=[None, None, 4096], dtype='float32'),
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InputSpec(shape=[4096], dtype='float32'),
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],
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)
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fn.eval()
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else:
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fn = self.net
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res = fn(x, y)
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if flag == "prim":
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ops = [
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op.name()
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for op in fn.program_cache.last()[-1][-1]
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.infer_program.program.global_block()
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.ops
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]
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assert "pd_op.mean" not in ops
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core._set_prim_all_enabled(False)
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return res
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def test_prim_all_dynamic(self):
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res_ref = self.base_net()
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res = self.base_net("prim")
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for ref, actual in zip(res_ref, res):
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np.testing.assert_allclose(ref, actual, rtol=1e-6)
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class TestPrimMode2(TestPrimMode1):
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def setUp(self):
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np.random.seed(2023)
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self.shape_x = [1, 300, 4096]
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self.shape_y = [4096]
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self.x = np.random.random(self.shape_x).astype("float32")
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self.y = np.random.random(self.shape_y).astype("float32")
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self.net = rms_norm2
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self.enable_cinn = False
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if __name__ == "__main__":
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unittest.main()
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