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[oneDNN] lookup_table op with support for BF16 data type. (#31558)
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python/paddle/fluid/tests/unittests/test_lookup_table_bf16_op.py
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| Original file line number | Diff line number | Diff line change |
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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 unittest | ||
| import numpy as np | ||
| from paddle.fluid.tests.unittests.op_test import ( | ||
| OpTest, convert_float_to_uint16, convert_uint16_to_float, | ||
| skip_check_grad_ci) | ||
| import paddle.fluid as fluid | ||
| import paddle.fluid.core as core | ||
| from paddle.fluid.op import Operator | ||
| from paddle import enable_static | ||
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| def _lookup(weights, ids, flat_ids): | ||
| w_shape = weights.shape | ||
| out_shape = list(ids.shape[:-1]) | ||
| out_shape.append(w_shape[-1]) | ||
| out = weights[flat_ids].reshape(out_shape) | ||
| return out | ||
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| def _get_grad(weights, ids, flat_ids): | ||
| w_shape = weights.shape | ||
| w_grad = np.zeros((w_shape), dtype=weights.dtype) | ||
| out_grad_shape = (np.prod(ids.shape[:-1]), w_shape[-1]) | ||
| out_grad = weights[flat_ids].reshape(out_grad_shape) | ||
| for i, idx in enumerate(flat_ids): | ||
| w_grad[idx, :] += out_grad[i] | ||
| return w_grad | ||
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| @unittest.skipIf(not core.supports_bfloat16(), | ||
| "place does not support BF16 evaluation") | ||
| class TestLookupTableBF16Op(OpTest): | ||
| def setUp(self): | ||
| self.op_type = "lookup_table" | ||
| self.dtype = np.uint16 | ||
|
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| table = np.random.random((17, 31)).astype("float32") | ||
| self.ids = np.random.randint(0, 17, (4, 1)).astype("int64") | ||
| self.flat_ids = self.ids.flatten() | ||
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| self.w_bf16 = convert_float_to_uint16(table) | ||
| self.out_bf16 = _lookup(self.w_bf16, self.ids, self.flat_ids) | ||
| self.out_fp32 = _lookup(table, self.ids, self.flat_ids) | ||
| self.w_grad_fp32 = _get_grad(table, self.ids, self.flat_ids) | ||
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| self.inputs = {'W': self.w_bf16, 'Ids': self.ids} | ||
| self.outputs = {'Out': self.out_fp32} | ||
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| def test_check_output(self): | ||
| self.check_output_with_place(core.CPUPlace(), check_dygraph=False) | ||
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| def test_check_grad(self): | ||
| self.check_grad_with_place( | ||
| core.CPUPlace(), ['W'], | ||
| 'Out', | ||
| no_grad_set=set('Ids'), | ||
| check_dygraph=False, | ||
| max_relative_error=1.5e-2, | ||
| user_defined_grads=[self.w_grad_fp32], | ||
| user_defined_grad_outputs=[self.out_bf16]) | ||
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| @unittest.skipIf(not core.supports_bfloat16(), | ||
| "place does not support BF16 evaluation") | ||
| class TestLookupTableBF16OpIds4D(TestLookupTableBF16Op): | ||
| def setUp(self): | ||
| super(TestLookupTableBF16OpIds4D, self).setUp() | ||
| self.ids = np.random.randint(0, 17, (2, 4, 5, 1)).astype("int64") | ||
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| @unittest.skipIf(not core.supports_bfloat16(), | ||
| "place does not support BF16 evaluation") | ||
| class TestLookupTableBF16OpWIsSelectedRows(unittest.TestCase): | ||
| def setUp(self): | ||
| self.ids = np.random.randint( | ||
| low=0, high=15, size=(10, 1)).astype("int64") | ||
| self.flat_ids = self.ids.flatten() | ||
| self.w_fp32 = np.random.random((15, 32)).astype("float32") | ||
| self.w_bf16 = convert_float_to_uint16(self.w_fp32) | ||
| self.scope = core.Scope() | ||
| self.place = core.CPUPlace() | ||
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| def prepare_w(self): | ||
| rows = [a for a in range(self.w_bf16.shape[0])] | ||
| row_numel = self.w_bf16.shape[1] | ||
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| w_selected_rows = self.scope.var('W').get_selected_rows() | ||
| w_selected_rows.set_height(len(rows)) | ||
| w_selected_rows.set_rows(rows) | ||
| w_tensor = w_selected_rows.get_tensor() | ||
| w_tensor.set(self.w_bf16, self.place) | ||
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| def prepare_ids(self): | ||
| ids_tensor = self.scope.var('Ids').get_tensor() | ||
| ids_tensor.set(self.ids, self.place) | ||
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| def _check_output(self, reference, result_array): | ||
| result_array_fp32 = convert_uint16_to_float(result_array) | ||
| np.testing.assert_allclose(result_array_fp32, reference, rtol=1.5e-2) | ||
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| def test_check_output(self): | ||
| self.prepare_ids() | ||
| self.prepare_w() | ||
| out_tensor = self.scope.var('Out').get_tensor() | ||
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| # create and run lookup_table operator | ||
| lookup_table = Operator("lookup_table", W='W', Ids='Ids', Out='Out') | ||
| lookup_table.run(self.scope, self.place) | ||
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| # get result from Out | ||
| result_array = np.array(out_tensor) | ||
| ref = _lookup(self.w_fp32, self.ids, self.flat_ids) | ||
| self._check_output(ref, result_array) | ||
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| @unittest.skipIf(not core.supports_bfloat16(), | ||
| "place does not support BF16 evaluation") | ||
| class TestLookupTableBF16OpWIsSelectedRows4DIds( | ||
| TestLookupTableBF16OpWIsSelectedRows): | ||
| def setUp(self): | ||
| super(TestLookupTableBF16OpWIsSelectedRows4DIds, self).setUp() | ||
| self.ids = np.random.randint( | ||
| low=0, high=15, size=(3, 4, 5, 1)).astype("int64") | ||
| self.flat_ids = self.ids.flatten() | ||
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| @skip_check_grad_ci( | ||
| reason="Since paddings are not trainable and fixed in forward," | ||
| "the gradient of paddings makes no sense and we don't " | ||
| "test the gradient here.") | ||
| @unittest.skipIf(not core.supports_bfloat16(), | ||
| "place does not support BF16 evaluation") | ||
| class TestLookupTableBF16OpWithPadding(TestLookupTableBF16Op): | ||
| def test_check_output(self): | ||
| ids = np.squeeze(self.inputs['Ids']) | ||
| padding_idx = np.random.choice(ids, 1)[0] | ||
| self.outputs['Out'][ids == padding_idx] = np.zeros(31) | ||
| self.attrs = {'padding_idx': int(padding_idx)} | ||
| self.check_output_with_place(core.CPUPlace(), check_dygraph=False) | ||
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| @skip_check_grad_ci( | ||
| reason="Since paddings are not trainable and fixed in forward," | ||
| "the gradient of paddings makes no sense and we don't " | ||
| "test the gradient here.") | ||
| @unittest.skipIf(not core.supports_bfloat16(), | ||
| "place does not support BF16 evaluation") | ||
| class TestLookupTableBF16OpIds4DPadding(TestLookupTableBF16OpIds4D): | ||
| def test_check_output(self): | ||
| ids = self.inputs['Ids'] | ||
| flatten_idx = ids.flatten() | ||
| padding_idx = np.random.choice(flatten_idx, 1)[0] | ||
| self.outputs['Out'][np.squeeze(ids == padding_idx)] = np.zeros(31) | ||
| self.attrs = {'padding_idx': int(padding_idx)} | ||
| self.check_output_with_place(core.CPUPlace(), check_dygraph=False) | ||
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| if __name__ == "__main__": | ||
| enable_static() | ||
| unittest.main() |
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