bind elementwise_mod_op_xpu (#42175)

* bind elementwise_mod_op_xpu *test=kunlun * add more supported dtypes and UTs *test=kunlun * fix datatype error * add op to in xpu1_op_list * Update Mac cmake version >=3.15 (#41456) * Update Mac cmake version >=3.15 * notest;read test1 notest;read test2 notest;read test3 * fix inference link error * fix inference link error * fix windows link error * fix cmake_policy * fix build big size * Add paddle::variant and replace paddle::any (#42139) * add variant and replace any * split attribute * disable unittest failed in eager CI in temporary (#42101) * test=py3-eager * test=py3-eager * test=py3-eager * combine graph_table and feature_table in graph_engine (#42134) * extract sub-graph * graph-engine merging * fix * fix * fix heter-ps config * test performance * test performance * test performance * test * test * update bfs * change cmake * test * test gpu speed * gpu_graph_engine optimization * add dsm sample method * add graph_neighbor_sample_v2 * Add graph_neighbor_sample_v2 * fix for loop * add cpu sample interface * fix kernel judgement * add ssd layer to graph_engine * fix allocation * fix syntax error * fix syntax error * fix pscore class * fix * change index settings * recover test * recover test * fix spelling * recover * fix * move cudamemcpy after cuda stream sync * fix linking problem * remove comment * add cpu test * test * add cpu test * change comment * combine feature table and graph table * test * test * pybind * test * test * test * test * pybind * pybind * fix cmake * pybind * fix * fix * add pybind * add pybind Co-authored-by: DesmonDay <908660116@qq.com> * [CustomDevice] add eager mode support (#42034) * fix FlattenContiguousRangeOpConverter out dim error (#42087) * fix FlattenContiguousRangeOpConverter out dim error * update code * fix python3.10 compile bug on windows (#42140) * Optimize dygraph GetExpectedKernelType perf (#42154) * opt dygraph scheduling * revert part impl * fix incorrect usages of std::move and other compile errors (#41045) * fix bug of std::move and others * fix an compile error in debug mode * fix wrong copy assignment operator Signed-off-by: tiancaishaonvjituizi <452565578@qq.com> * reformat Signed-off-by: tiancaishaonvjituizi <452565578@qq.com> * reformat Signed-off-by: tiancaishaonvjituizi <452565578@qq.com> * fix ArrayRef constructor following llvm * fix format * fix conflict with master * fix variant compile error (#42203) * [Eager] Support numpy.ndarry in CastNumpy2Scalar (#42136) * [Eager] Remove redundancy code, fix fp16 case (#42169) * [Eager] Support div(scalar) in eager mode (#42148) * [Eager] Support div scalar in eager mode * Updated and remove debug logs * Remove list, use 'or' directly * Remove useless statement * fix recompute (#42128) * fix recompute * modify return * add LICENSE in wheel dist-info package (#42187) * replace any by variant in infermeta (#42181) * 【PaddlePaddle Hackathon 2】24、为 Paddle 新增 nn.ChannelShuffle 组网 API (#40743) * Add infermeta for ChannelShuffle * Create channel_shuffle_grad_kernel.h * Create channel_shuffle_kernel.h * Create channel_shuffle_sig.cc * Create channel_shuffle_op.cc ChannelShuffle算子的描述 * Create channel_shuffle_kernel_impl.h ChannelShuffle核函数的实现 * Create channel_shuffle_grad_kernel_impl.h ChannelShuffle反向核函数的实现 * Add kernel register of channel shuffle and grad 注册ChannelShuffle及其反向的核函数 * add nn.functional.channel_shuffle * add nn.ChannelShuffle * Create test_channel_shuffle.py * Update example of ChannelShuffle in vision.py * Update test_channel_shuffle.py * 修改channel_shuffle核函数的实现位置 * 修正代码格式 * 删除多余空格 * 完善channel_shuffle的错误检查 * Update unary.cc * Update channel_shuffle_op.cc * Update test_channel_shuffle.py * Update unary.cc * add channel_shuffle * Update test_channel_shuffle.py * Update vision.py * 调整代码格式 * Update channel_shuffle_sig.cc * 更新ChannelShuffle的文档 * 更新channel_shuffle的文档 * remove ChannelShuffleOpArgumentMapping * add ChannelShuffleGradInferMeta * Update channel_shuffle_op.cc * 调整channel_shuffle及其梯度的核函数的位置 * Do not reset default stream for StreamSafeCUDAAllocator (#42149) * remove redundant computation in Categorical.probs (#42114) * Downloading data for test_analyzer_vit_ocr (#42041) * Change server URL * update config * add test to parallel UT rule * add checksum to ensure files are downloaded * change downloading target * reuse existing variable * change target directory * fix en docs of some Apis (gradients, scope_guard, cuda_places, name_scope, device_guard, load_program_state, scale, ParamAttr and WeightNormParamAttr) (#41604) * Update scope_guard; test=document_fix * gradients; test=document_fix * gradients; test=document_fix * name_scope; test=document_fix * cpu_places; test=document_fix * WeightNormParamAttr; test=document_fix * cuda_places; test=document_fix * load_program_state; test=document_fix * device_guard; test=document_fix * device_guard; test=document_fix * ParamAttr; test=document_fix * scale; test=document_fix * scale; test=document_fix * update code example；test=document_fix Co-authored-by: Chen Long <1300851984@qq.com> * fix datatype error add op to in xpu1_op_list *test=kunlun * fix elementwise_mod op path error *test=kunlun * fix elementwise_mod UT error *test=kunlun * fix datatype error add op to in xpu1_op_list *test=kunlun add op to in xpu1_op_list fix elementwise_mod op path error *test=kunlun fix elementwise_mod UT error *test=kunlun Co-authored-by: tianshuo78520a <707759223@qq.com> Co-authored-by: Chen Weihang <chenweihang@baidu.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: seemingwang <seemingwang@users.noreply.github.com> Co-authored-by: DesmonDay <908660116@qq.com> Co-authored-by: ronnywang <524019753@qq.com> Co-authored-by: baoachun <962571062@qq.com> Co-authored-by: Zhou Wei <1183042833@qq.com> Co-authored-by: tiancaishaonvjituizi <452565578@qq.com> Co-authored-by: Weilong Wu <veyron_wu@163.com> Co-authored-by: Roc <30228238+sljlp@users.noreply.github.com> Co-authored-by: BrilliantYuKaimin <91609464+BrilliantYuKaimin@users.noreply.github.com> Co-authored-by: Ruibiao Chen <chenruibiao@baidu.com> Co-authored-by: Feiyu Chan <chenfeiyu@baidu.com> Co-authored-by: Sławomir Siwek <slawomir.siwek@intel.com> Co-authored-by: Yilingyelu <103369238+Yilingyelu@users.noreply.github.com> Co-authored-by: Chen Long <1300851984@qq.com>
PaddlePaddle · May 6, 2022 · 6ea2f04 · 6ea2f04
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diff --git a/paddle/fluid/operators/elementwise/elementwise_mod_op_xpu.cc b/paddle/fluid/operators/elementwise/elementwise_mod_op_xpu.cc
@@ -0,0 +1,49 @@
+/* Copyright (c) 2022 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. */
+
+#ifdef PADDLE_WITH_XPU
+#include <memory>
+#include <string>
+#include "paddle/fluid/framework/data_layout.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/operators/common_infer_shape_functions.h"
+#include "paddle/fluid/operators/elementwise/elementwise_op_function.h"
+
+#include "paddle/fluid/operators/elementwise/elementwise_xpu.h"
+#include "paddle/fluid/platform/device/device_wrapper.h"
+
+namespace paddle {
+namespace operators {
+
+template <typename T>
+class ElementwiseModXPUKernel : public framework::OpKernel<T> {
+  using XPUType = typename XPUTypeTrait<T>::Type;
+
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const override {
+    XPUElementwise<T, XPUType>(ctx, xpu::broadcast_mod<XPUType>);
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+
+REGISTER_OP_XPU_KERNEL(elementwise_mod, ops::ElementwiseModXPUKernel<float>,
+                       ops::ElementwiseModXPUKernel<paddle::platform::float16>,
+                       ops::ElementwiseModXPUKernel<int32_t>,
+                       ops::ElementwiseModXPUKernel<int64_t>);
+
+#endif
diff --git a/paddle/fluid/platform/device/xpu/xpu2_op_list.h b/paddle/fluid/platform/device/xpu/xpu2_op_list.h
@@ -130,6 +130,11 @@ XPUOpMap& get_kl2_ops() {
       {"elementwise_sub",
        XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
                      pOpKernelType(vartype::FP16, XPUPlace())})},
+      {"elementwise_mod",
+       XPUKernelSet({pOpKernelType(vartype::FP32, XPUPlace()),
+                     pOpKernelType(vartype::FP16, XPUPlace()),
+                     pOpKernelType(vartype::INT64, XPUPlace()),
+                     pOpKernelType(vartype::INT32, XPUPlace())})},
       {"equal", XPUKernelSet({pOpKernelType(vartype::INT64, XPUPlace()),
                               pOpKernelType(vartype::INT32, XPUPlace()),
                               pOpKernelType(vartype::FP32, XPUPlace())})},

diff --git a/python/paddle/fluid/tests/unittests/xpu/test_elementwise_mod_op_xpu.py b/python/paddle/fluid/tests/unittests/xpu/test_elementwise_mod_op_xpu.py
@@ -0,0 +1,133 @@
+#  Copyright (c) 2022 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 sys
+sys.path.append("..")
+import unittest
+import numpy as np
+from op_test import OpTest
+import paddle.fluid as fluid
+from paddle.fluid import compiler, Program, program_guard
+
+import paddle
+from op_test_xpu import XPUOpTest
+from xpu.get_test_cover_info import create_test_class, get_xpu_op_support_types, XPUOpTestWrapper
+
+paddle.enable_static()
+
+
+class XPUTestElementwiseModOp(XPUOpTestWrapper):
+    def __init__(self) -> None:
+        self.op_name = 'elementwise_mod'
+        self.use_dynamic_create_class = False
+
+    class ElementwiseModOp(XPUOpTest):
+        def init_kernel_type(self):
+            self.use_mkldnn = False
+
+        def init_input_output(self):
+            self.x = np.random.uniform(0, 10000, [10, 10]).astype(self.dtype)
+            self.y = np.random.uniform(0, 1000, [10, 10]).astype(self.dtype)
+            self.out = np.mod(self.x, self.y)
+            self.inputs = {
+                'X': OpTest.np_dtype_to_fluid_dtype(self.x),
+                'Y': OpTest.np_dtype_to_fluid_dtype(self.y)
+            }
+            self.outputs = {'Out': self.out}
+            self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn}
+
+        def init_dtype(self):
+            pass
+
+        def init_axis(self):
+            pass
+
+        def setUp(self):
+            self.op_type = 'elementwise_mod'
+            self.use_xpu = True
+            self.dtype = self.in_type
+            self.axis = -1
+            self.init_dtype()
+            self.init_input_output()
+            self.init_kernel_type()
+            self.init_axis()
+
+        def test_check_output(self):
+            if paddle.is_compiled_with_xpu():
+                place = paddle.XPUPlace(0)
+                self.check_output_with_place(place)
+
+    class TestElementwiseModOp_broadcast_1(ElementwiseModOp):
+        def init_input_output(self):
+            self.inputs = {
+                'X': np.random.rand(2, 100, 3).astype(self.dtype),
+                'Y': np.random.rand(2, 100, 3).astype(self.dtype)
+            }
+
+            self.attrs = {'axis': 1}
+            self.outputs = {'Out': self.inputs['X'] % self.inputs['Y']}
+
+    class TestElementwiseModOp_broadcast_2(ElementwiseModOp):
+        def init_input_output(self):
+            self.inputs = {
+                'X': np.random.rand(22, 128, 3).astype(self.dtype),
+                'Y': np.random.rand(22, 128, 3).astype(self.dtype)
+            }
+
+            self.attrs = {'axis': 1}
+            self.outputs = {'Out': self.inputs['X'] % self.inputs['Y']}
+
+    class TestRemainderOp(unittest.TestCase):
+        def test_dygraph(self):
+            with fluid.dygraph.guard():
+                np_x = np.random.rand(22, 128, 3).astype('int64')
+                np_y = np.random.rand(22, 128, 3).astype('int64')
+                x = paddle.to_tensor(np_x)
+                y = paddle.to_tensor(np_y)
+                z = paddle.remainder(x, y)
+                np_z = z.numpy()
+                z_expected = np.mod(np_x, np_y)
+                self.assertEqual((np_z == z_expected).all(), True)
+
+                np_x = np.array([-3.3, 11.5, -2, 3.5])
+                np_y = np.array([-1.2, 2., 3.3, -2.3])
+                x = paddle.to_tensor(np_x)
+                y = paddle.to_tensor(np_y)
+                z = x % y
+                z_expected = np.array([-0.9, 1.5, 1.3, -1.1])
+                self.assertEqual(np.allclose(z_expected, z.numpy()), True)
+
+                np_x = np.random.rand(22, 128, 3).astype('int32')
+                np_y = np.random.rand(22, 128, 3).astype('int32')
+                x = paddle.to_tensor(np_x)
+                y = paddle.to_tensor(np_y)
+                z = paddle.remainder(x, y)
+                np_z = z.numpy()
+                z_expected = np.mod(np_x, np_y)
+                self.assertEqual((np_z == z_expected).all(), True)
+
+                np_x = np.array([-3, 11, -2, 3])
+                np_y = np.array([-1, 2, 3, -2])
+                x = paddle.to_tensor(np_x, dtype="float16")
+                y = paddle.to_tensor(np_y, dtype="float16")
+                z = x % y
+                z_expected = np.array([0, 1, 1, -1])
+                self.assertEqual(np.allclose(z_expected, z.numpy()), True)
+
+
+support_types = get_xpu_op_support_types('elementwise_mod')
+for stype in support_types:
+    create_test_class(globals(), XPUTestElementwiseModOp, stype)
+
+if __name__ == '__main__':
+    unittest.main()