[MLU] add check_finite_and_unscale op for amp (#40458)

paddle/fluid/operators/amp/check_finite_and_unscale_op_mlu.cc

+/* 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. */
+
+#include "paddle/fluid/operators/amp/check_finite_and_unscale_op.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class CheckFiniteAndUnscaleMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto& dev_ctx = ctx.template device_context<platform::MLUDeviceContext>();
+    const auto xs = ctx.MultiInput<framework::Tensor>("X");
+    const auto* scale = ctx.Input<framework::Tensor>("Scale");
+    auto outs = ctx.MultiOutput<framework::Tensor>("Out");
+    auto* found_inf = ctx.Output<framework::Tensor>("FoundInfinite");
+
+    found_inf->mutable_data<bool>(dev_ctx.GetPlace());
+
+    MLUCnnlTensorDesc scale_desc(*scale);
+    MLUCnnlTensorDesc found_inf_desc(*found_inf, CNNL_LAYOUT_ARRAY,
+                                     ToCnnlDataType<bool>());
+
+    for (size_t i = 0; i < xs.size(); ++i) {
+      const auto* x = xs[i];
+      auto* out = outs[i];
+      out->mutable_data<T>(ctx.GetPlace());
+
+      // check is_finite or is_nan
+      Tensor is_finite(found_inf->type());
+      if (i != 0) {
+        is_finite.Resize(phi::make_ddim({1}));
+        is_finite.mutable_data<bool>(ctx.GetPlace());
+      } else {
+        is_finite.ShareDataWith(*found_inf);
+      }
+
+      MLUCnnlTensorDesc x_desc(*x);
+
+      MLUCnnl::IsNanInf(ctx, x_desc.get(), GetBasePtr(x),
+                        GetBasePtr(&is_finite));
+
+      // save is_finite by logical_and op after checking every input
+      if (i != 0) {
+        MLUCnnlTensorDesc is_finite_desc(is_finite, CNNL_LAYOUT_ARRAY,
+                                         ToCnnlDataType<bool>());
+        MLUCnnl::Logic(ctx, CNNL_LOGIC_OP_OR, found_inf_desc.get(),
+                       GetBasePtr(found_inf), is_finite_desc.get(),
+                       GetBasePtr(&is_finite), found_inf_desc.get(),
+                       GetBasePtr(found_inf));
+      }
+
+      // The normal logic is :
+      // out = in, if found_inf = true
+      // out = in/scale, if found_inf = false
+      // But when found_inf is true, the data of Out should not be used.
+      // So, on MLU, we always compute out with in/scale.
+      MLUCnnlTensorDesc out_desc(*out);
+      MLUCnnl::Div(ctx, CNNL_COMPUTATION_HIGH_PRECISION, x_desc.get(),
+                   GetBasePtr(x), scale_desc.get(), GetBasePtr(scale),
+                   out_desc.get(), GetBasePtr(out));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+REGISTER_OP_MLU_KERNEL(check_finite_and_unscale,
+                       ops::CheckFiniteAndUnscaleMLUKernel<float>,
+                       ops::CheckFiniteAndUnscaleMLUKernel<plat::float16>);

python/paddle/fluid/tests/unittests/mlu/test_amp_check_finite_and_scale_op_mlu.py

+#  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.
+
+from __future__ import print_function
+
+import numpy as np
+import unittest
+import sys
+sys.path.append("..")
+from op_test import OpTest
+import paddle
+
+paddle.enable_static()
+SEED = 2022
+
+
+class TestCheckFiniteAndUnscaleOp(OpTest):
+    def setUp(self):
+        self.set_mlu()
+        self.op_type = "check_finite_and_unscale"
+        self.init_dtype()
+        self.init_test_case()
+
+    def init_test_case(self):
+        x = np.random.random((129, 129)).astype(self.dtype)
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([0]),
+            'Out': [('out0', x / scale)],
+        }
+
+    def set_mlu(self):
+        self.__class__.use_mlu = True
+        self.place = paddle.device.MLUPlace(0)
+
+    def init_dtype(self):
+        self.dtype = np.float32
+
+    def test_check_output(self):
+        self.check_output_with_place(self.place)
+
+
+class TestCheckFiniteAndUnscaleOpWithNan(TestCheckFiniteAndUnscaleOp):
+    def init_test_case(self):
+        x = np.random.random((129, 129)).astype(self.dtype)
+        x[128][128] = np.nan
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([1]),
+            'Out': [('out0', x)],
+        }
+
+    def test_check_output(self):
+        # When input contains nan, do not check the output, 
+        # since the output may be nondeterministic and will be discarded.
+        self.check_output_with_place(self.place, no_check_set=['Out'])
+
+
+class TestCheckFiniteAndUnscaleOpWithInf(TestCheckFiniteAndUnscaleOp):
+    def init_test_case(self):
+        x = np.random.random((129, 129)).astype(self.dtype)
+        x[128][128] = np.inf
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([1]),
+            'Out': [('out0', x)],
+        }
+
+    def test_check_output(self):
+        # When input contains inf, do not check the output, 
+        # since the output may be nondeterministic and will be discarded.
+        self.check_output_with_place(self.place, no_check_set=['Out'])
+
+
+class TestCheckFiniteAndUnscaleOpMultiInput(TestCheckFiniteAndUnscaleOp):
+    def init_test_case(self):
+        x0 = np.random.random((129, 129)).astype(self.dtype)
+        x1 = np.random.random((129, 129)).astype(self.dtype)
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x0), ('x1', x1)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([0]),
+            'Out': [('out0', x0 / scale), ('out1', x1 / scale)],
+        }
+
+
+class TestCheckFiniteAndUnscaleOpMultiInputWithNan(TestCheckFiniteAndUnscaleOp):
+    def init_test_case(self):
+        x0 = np.random.random((129, 129)).astype(self.dtype)
+        x0[128][128] = np.nan
+        x1 = np.random.random((129, 129)).astype(self.dtype)
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x0), ('x1', x1)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([1]),
+            'Out': [('out0', x0 / scale), ('out1', x1 / scale)],
+        }
+
+    def test_check_output(self):
+        # When input contains inf, do not check the output, 
+        # since the output may be nondeterministic and will be discarded.
+        self.check_output_with_place(self.place, no_check_set=['Out'])
+
+
+class TestCheckFiniteAndUnscaleOpMultiInputWithInf(TestCheckFiniteAndUnscaleOp):
+    def init_test_case(self):
+        x0 = np.random.random((129, 129)).astype(self.dtype)
+        x0[128][128] = np.nan
+        x1 = np.random.random((129, 129)).astype(self.dtype)
+        x1[128][128] = np.inf
+        scale = np.random.random((1)).astype(self.dtype)
+
+        self.inputs = {'X': [('x0', x0), ('x1', x1)], 'Scale': scale}
+        self.outputs = {
+            'FoundInfinite': np.array([1]),
+            'Out': [('out0', x0 / scale), ('out1', x1 / scale)],
+        }
+
+    def test_check_output(self):
+        # When input contains inf, do not check the output, 
+        # since the output may be nondeterministic and will be discarded.
+        self.check_output_with_place(self.place, no_check_set=['Out'])
+
+
+if __name__ == '__main__':
+    unittest.main()