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Add elementwise ops (PaddlePaddle#59)
* add elementwise_op_handler * fix pow_handler
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109 changes: 109 additions & 0 deletions
109
paddle/fluid/framework/ipu/popart_canonicalization/elementwise_ops.cc
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| // Copyright (c) 2021 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. | ||
|
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| #include "paddle/fluid/framework/ipu/popart_canonicalization/canonicalization_utils.h" | ||
| #include "paddle/fluid/framework/ipu/popart_canonicalization/op_builder.h" | ||
| #include "paddle/fluid/platform/enforce.h" | ||
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| namespace paddle { | ||
| namespace framework { | ||
| namespace ipu { | ||
| namespace { | ||
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| ir::Node *elementwise_op_handler(ir::Graph *graph, ir::Node *node, | ||
| const std::string &type) { | ||
| auto *op = node->Op(); | ||
| auto x_shape = op->Block()->FindVar(op->Input("X").front())->GetShape(); | ||
| int64_t x_rank = x_shape.size(); | ||
| auto y_shape = op->Block()->FindVar(op->Input("Y").front())->GetShape(); | ||
| int64_t y_rank = y_shape.size(); | ||
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| auto axis = BOOST_GET_CONST(int, op->GetAttr("axis")); | ||
| if (axis == -1 || axis == x_rank - 1 || x_rank == y_rank) { | ||
| auto new_node = CreateBaseOp( | ||
| graph, type, {GetInputNode("X", node), GetInputNode("Y", node)}, | ||
| node->outputs); | ||
| return new_node; | ||
| } else { | ||
| auto y_new_shape = std::vector<int64_t>(x_rank, 1); | ||
| for (int i = axis; i < axis + y_rank; ++i) { | ||
| y_new_shape[i] = y_shape[i - axis]; | ||
| } | ||
| auto attrs = AttributeMap{ | ||
| {"value", y_new_shape}, | ||
| {"dims", std::vector<int64_t>{x_rank}}, | ||
| {"dtype", ONNXDataType::INT64}, | ||
| }; | ||
| // constant | ||
| auto new_node_const = CreateConst(graph, {}, {}, attrs); | ||
| // reshape | ||
| auto new_node_reshape = | ||
| CreateBaseOp(graph, "Reshape", | ||
| {GetInputNode("Y", node), new_node_const->outputs[0]}, {}); | ||
| ReplaceNodeInputs(node, new_node_reshape); | ||
| // elementwise_op | ||
| auto new_node = CreateBaseOp( | ||
| graph, type, {GetInputNode("X", node), new_node_reshape->outputs[0]}, | ||
| {node->outputs[0]}); | ||
| ReplaceNodeInputs(node, new_node); | ||
| ReplaceNodeOutputs(node, new_node); | ||
| return new_node; | ||
| } | ||
| } | ||
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| ir::Node *elementwise_add_handler(ir::Graph *graph, ir::Node *node) { | ||
| return elementwise_op_handler(graph, node, "Add"); | ||
| } | ||
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| ir::Node *elementwise_sub_handler(ir::Graph *graph, ir::Node *node) { | ||
| return elementwise_op_handler(graph, node, "Sub"); | ||
| } | ||
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| ir::Node *elementwise_div_handler(ir::Graph *graph, ir::Node *node) { | ||
| return elementwise_op_handler(graph, node, "Div"); | ||
| } | ||
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| ir::Node *elementwise_mul_handler(ir::Graph *graph, ir::Node *node) { | ||
| return elementwise_op_handler(graph, node, "Mul"); | ||
| } | ||
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| ir::Node *elementwise_min_handler(ir::Graph *graph, ir::Node *node) { | ||
| return elementwise_op_handler(graph, node, "Min"); | ||
| } | ||
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| ir::Node *elementwise_max_handler(ir::Graph *graph, ir::Node *node) { | ||
| return elementwise_op_handler(graph, node, "Max"); | ||
| } | ||
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| ir::Node *elementwise_pow_handler(ir::Graph *graph, ir::Node *node) { | ||
| return elementwise_op_handler(graph, node, "Pow"); | ||
| } | ||
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| ir::Node *elementwise_mod_handler(ir::Graph *graph, ir::Node *node) { | ||
| return elementwise_op_handler(graph, node, "Mod"); | ||
| } | ||
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| REGISTER_HANDLER(elementwise_add, elementwise_add_handler); | ||
| REGISTER_HANDLER(elementwise_sub, elementwise_sub_handler); | ||
| REGISTER_HANDLER(elementwise_div, elementwise_div_handler); | ||
| REGISTER_HANDLER(elementwise_mul, elementwise_mul_handler); | ||
| REGISTER_HANDLER(elementwise_min, elementwise_min_handler); | ||
| REGISTER_HANDLER(elementwise_max, elementwise_max_handler); | ||
| REGISTER_HANDLER(elementwise_pow, elementwise_pow_handler); | ||
| REGISTER_HANDLER(elementwise_mod, elementwise_mod_handler); | ||
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| } // namespace | ||
| } // namespace ipu | ||
| } // namespace framework | ||
| } // namespace paddle |
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86 changes: 86 additions & 0 deletions
86
python/paddle/fluid/tests/unittests/ipu/test_elemetwise_add_op.py
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| # Copyright (c) 2021 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. | ||
|
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| from __future__ import print_function | ||
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| import numpy as np | ||
| import unittest | ||
| import paddle | ||
| import paddle.fluid | ||
| import paddle.static | ||
| import paddle.fluid.compiler as compiler | ||
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| paddle.enable_static() | ||
| SEED = 2021 | ||
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| @unittest.skipIf(not paddle.is_compiled_with_ipu(), | ||
| "core is not compiled with IPU") | ||
| class TestAdd(unittest.TestCase): | ||
| def _test_add(self, run_ipu=True): | ||
| main_prog = paddle.static.Program() | ||
| startup_prog = paddle.static.Program() | ||
| main_prog.random_seed = SEED | ||
| startup_prog.random_seed = SEED | ||
| np.random.seed(SEED) | ||
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| np_a = np.random.rand(3, 3, 3).astype(np.float32) | ||
| np_b = np.arange(1, 4).reshape([3]).astype(np.float32) | ||
| with paddle.static.program_guard(main_prog, startup_prog): | ||
| a = paddle.static.data( | ||
| name="a", | ||
| shape=[3, 3, 3], | ||
| dtype='float32', ) | ||
| b = paddle.static.data( | ||
| name="b", | ||
| shape=[3], | ||
| dtype='float32', ) | ||
| # out = paddle.fluid.layers.elementwise_add(a, b, axis=-1) | ||
| # out = paddle.fluid.layers.elementwise_add(a, b, axis=0) | ||
| # out = paddle.fluid.layers.elementwise_add(a, b, axis=1) | ||
| out = paddle.fluid.layers.elementwise_add(a, b, axis=2) | ||
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| if run_ipu: | ||
| place = paddle.IPUPlace() | ||
| else: | ||
| place = paddle.CPUPlace() | ||
| exe = paddle.static.Executor(place) | ||
| exe.run(startup_prog) | ||
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| if run_ipu: | ||
| feed_list = [a.name, b.name] | ||
| fetch_list = [out.name] | ||
| ipu_strategy = compiler.get_ipu_strategy() | ||
| ipu_strategy.is_training = False | ||
| program = compiler.IpuCompiler( | ||
| main_prog, ipu_strategy=ipu_strategy).compile(feed_list, | ||
| fetch_list) | ||
| else: | ||
| program = main_prog | ||
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| result = exe.run( | ||
| program, | ||
| feed={'a': np_a, | ||
| 'b': np_b}, | ||
| fetch_list=[out], ) | ||
| return result[0] | ||
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| def test_add(self): | ||
| ipu_res = self._test_add(True) | ||
| cpu_res = self._test_add(False) | ||
| self.assertTrue(np.allclose(ipu_res, cpu_res, atol=1e-4)) | ||
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| if __name__ == "__main__": | ||
| unittest.main() |