# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import os import unittest import numpy os.environ['FLAGS_prim_all'] = 'true' os.environ['FLAGS_prim_enable_dynamic'] = 'true' os.environ['FLAGS_use_cinn'] = '1' os.environ['FLAGS_deny_cinn_ops'] = 'slice;' import paddle def generate_input_spec(rank_dtype_list): input_spec = [] for rank, dtype in rank_dtype_list: input_spec.append( paddle.static.InputSpec(shape=[None] * rank, dtype=dtype) ) return input_spec class TestTrivialFusion(unittest.TestCase): def setUp(self): pass def tearDown(self): pass def compare_result(self, dy_compute, input_spec, data_init): inputs = data_init() dy_out = dy_compute(*inputs) static_compute = paddle.jit.to_static( full_graph=True, backend="CINN", input_spec=input_spec, )(dy_compute) st_out = static_compute(*inputs) if isinstance(dy_out, paddle.Tensor): numpy.testing.assert_allclose(dy_out, st_out, atol=1e-5, rtol=1e-6) return for d, s in zip(dy_out, st_out): numpy.testing.assert_allclose(d, s, atol=1e-5, rtol=1e-6) def test_simple_trivial_fusions(self): def func(x): x = x * 2 x = x + 1 x = paddle.nn.functional.relu(x) x = paddle.transpose(x, perm=[0, 2, 1]) x = x.reshape((-1, 128)) return x def init(): x = paddle.rand((32, 32, 128)) return (x,) input_spec = generate_input_spec([(3, 'float32')]) self.compare_result(func, input_spec, init) def test_trivial_fusion_slice_and_concat(self): def func(x, y): x = x * 2 y = y * 2 x = x[:, :, :64] y = y[:, :, :64] z = paddle.concat([x, y], axis=-1) return z def init(): x = paddle.rand((32, 32, 128)) y = paddle.rand((32, 32, 128)) return (x, y) input_spec = generate_input_spec([(3, 'float32'), (3, 'float32')]) self.compare_result(func, input_spec, init) def test_trivial_fusion_gather_nd(self): def func(x, y): x = x * 2 output = paddle.gather_nd(x, y) return output def init(): x = paddle.to_tensor( [[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]]] ) index = paddle.to_tensor([[0, 1]]) return (x, index) input_spec = [ paddle.static.InputSpec(shape=[None, None, None], dtype='float32'), paddle.static.InputSpec(shape=[None, 2], dtype='int32'), ] self.compare_result(func, input_spec, init) def test_broadcast(self): def func(x, y): output = x + y return output def init(): x = paddle.rand((32, 1)) y = paddle.rand((1, 32)) return (x, y) input_spec = generate_input_spec([(2, 'float32'), (2, 'float32')]) self.compare_result(func, input_spec, init) def test_broadcast_tree(self): def init(): var_1 = paddle.rand([32], dtype="float32") var_2 = paddle.rand([32], dtype="float32") var_3 = paddle.rand([32], dtype="float32") return (var_1, var_2, var_3) def input_spec(): return [ paddle.static.InputSpec(shape=[None], dtype='float32'), # S0 paddle.static.InputSpec(shape=[None], dtype='float32'), # S1 paddle.static.InputSpec(shape=[None], dtype='float32'), # S2 ] def func(var_1, var_2, var_3): var_4 = paddle.reshape(var_1, [-1, 32]) # Div(S0, 32) var_5 = paddle.reshape(var_2, [-1, 32]) # Div(S1, 32) var_6 = paddle.reshape(var_3, [-1, 32]) # Div(S2, 32) # Broadcast(Div(S0, 32), Div(S1, 32), Div(S2, 32) var_7 = var_4 + var_5 + var_6 # Mul(Broadcast(Div(S0, 32), Div(S1, 32), Div(S2, 32)), 32) var_9 = var_7.reshape([1, -1, 1, 1]) var_752 = paddle.full([20, var_9.shape[1], 8, 24], 0.1, "float32") var_kwarg_var_10744 = var_2 var_769 = paddle.full( [20, 32, var_4.shape[0], 8, 24], 0.1, "float32" ) var_kwarg_middle_31 = paddle.rand( [20, var_9.shape[1], 8, 24], "float32" ) var_kwarg_middle_31[:] = 0.1 var_kwarg_middle_30 = paddle.full([20, 32, 1, 1, 1], 0.1, "float32") var_812 = paddle.full(shape=[], dtype='float32', fill_value=0.0) var_814 = paddle.expand(var_812, var_kwarg_middle_31.shape) var_815 = paddle.greater_than(var_kwarg_middle_31, var_814) var_816 = paddle.cast(var_815, dtype='float32') var_817 = var_816 * var_752 var_818 = paddle.reshape(var_817, [20, 32, -1, 8, 24]) var_819 = paddle.reshape(var_kwarg_var_10744, [32, -1, 1, 1]) var_820 = paddle.full(shape=[20, 32, 1, 1, 1], fill_value=1e-05) var_821 = var_kwarg_middle_30 + var_820 var_822 = paddle.full(shape=[20, 32, 1, 1, 1], fill_value=1.0) var_823 = var_822 / var_821 var_824 = paddle.sqrt(var_823) var_827 = var_818 * var_819 var_830 = var_824 * var_827 var_831 = paddle.sum(var_830, keepdim=True, axis=[2, 3, 4]) var_834 = var_831 * var_769 var_837 = var_834 * var_827 var_838 = paddle.sum(var_837, keepdim=True, axis=[2, 3, 4]) return var_818, var_824, var_838, var_831, var_830 self.compare_result(func, input_spec(), init) if __name__ == "__main__": unittest.main()