【Prim】Custom softmax grad (PaddlePaddle#51474)

* [CINN]Enhance CacheKey hash logic by considering input dtypes (PaddlePaddle#50557) * [CINN]Enhance CacheKey hash logic by considering input dtypes * add unittest * fix typo * fix typo * fix map.at * fix find * fix test * fix cinn cache key structure realize * using ordered map for attributes * add test by review advice --------- Co-authored-by: jiangcheng <[email protected]> * [prim] enable dygraph_to_static to support custom_vjp * Pr 50885 (#7) * [CINN]Enhance CacheKey hash logic by considering input dtypes (PaddlePaddle#50557) * [CINN]Enhance CacheKey hash logic by considering input dtypes * add unittest * fix typo * fix typo * fix map.at * fix find * fix test * fix cinn cache key structure realize * using ordered map for attributes * add test by review advice --------- Co-authored-by: jiangcheng <[email protected]> * [prim] enable dygraph_to_static to support custom_vjp * fix code in a dy2static-friendly way. * [dystatic] add hooker for prim --------- Co-authored-by: Aurelius84 <[email protected]> Co-authored-by: jiangcheng <[email protected]> Co-authored-by: cxxly <[email protected]> * [prim] enable dygraph_to_static to support custom_vjp * fix cast prim and vjp dtype mapping error bug * Cxx prim custom vjp (#8) * [CINN]Enhance CacheKey hash logic by considering input dtypes (PaddlePaddle#50557) --------- Co-authored-by: jiangcheng <[email protected]> * [prim] enable dygraph_to_static to support custom_vjp * Pr 50885 (#7) * [CINN]Enhance CacheKey hash logic by considering input dtypes (PaddlePaddle#50557) * [CINN]Enhance CacheKey hash logic by considering input dtypes --------- Co-authored-by: jiangcheng <[email protected]> * [prim] enable dygraph_to_static to support custom_vjp * fix code in a dy2static-friendly way. * [dystatic] add hooker for prim --------- Co-authored-by: Aurelius84 <[email protected]> Co-authored-by: jiangcheng <[email protected]> Co-authored-by: cxxly <[email protected]> * [prim] enable dygraph_to_static to support custom_vjp * fix cast prim and vjp dtype mapping error bug * [dy2static-ci] fix dy2static ci errors. --------- Co-authored-by: Aurelius84 <[email protected]> Co-authored-by: jiangcheng <[email protected]> Co-authored-by: cxxly <[email protected]> * [Prim] enable whitelist and blacklist for custom_vjp * support softmax grad * remove additional code * add test back --------- Co-authored-by: Aurelius84 <[email protected]> Co-authored-by: jiangcheng <[email protected]> Co-authored-by: cxxly <[email protected]> Co-authored-by: xiongkun <[email protected]>
iclementine · Mar 15, 2023 · f124c86 · f124c86
1 parent 50df017
commit f124c86
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diff --git a/paddle/fluid/operators/softmax_op.cc b/paddle/fluid/operators/softmax_op.cc
@@ -19,6 +19,9 @@ limitations under the License. */
 #include "paddle/fluid/framework/infershape_utils.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/platform/device/gpu/gpu_dnn.h"
+#include "paddle/fluid/prim/api/composite_backward/composite_backward_api.h"
+#include "paddle/fluid/prim/utils/static/composite_grad_desc_maker.h"
+#include "paddle/fluid/prim/utils/static/desc_tensor.h"
 #include "paddle/phi/core/infermeta_utils.h"
 #include "paddle/phi/infermeta/backward.h"
 #include "paddle/phi/infermeta/unary.h"
@@ -156,6 +159,23 @@ class SoftmaxOpGradMaker : public framework::SingleGradOpMaker<T> {
   }
 };
 
+class SoftmaxCompositeGradOpMaker : public prim::CompositeGradOpMakerBase {
+  using prim::CompositeGradOpMakerBase::CompositeGradOpMakerBase;
+
+ public:
+  void Apply() override {
+    paddle::Tensor out = this->GetSingleForwardOutput("Out");
+    paddle::Tensor out_grad = this->GetSingleOutputGrad("Out");
+    paddle::Tensor dx = this->GetSingleInputGrad("X");
+    auto* dx_ptr = this->GetOutputPtr(&dx);
+    std::string dx_name = this->GetOutputName(dx);
+    int axis = static_cast<int>(this->Attr<int>("axis"));
+    VLOG(6) << "Runing softmax_grad composite func";
+    prim::softmax_grad<prim::DescTensor>(out, out_grad, axis, dx_ptr);
+    this->RecoverOutputName(dx, dx_name);
+  }
+};
+
 DECLARE_INPLACE_OP_INFERER(SoftmaxInplaceInferer, {"X", "Out"});
 
 }  // namespace operators
@@ -172,6 +192,7 @@ REGISTER_OPERATOR(softmax,
                   ops::SoftmaxOpInferVarType,
                   ops::SoftmaxOpGradMaker<paddle::framework::OpDesc>,
                   ops::SoftmaxOpGradMaker<paddle::imperative::OpBase>,
+                  ops::SoftmaxCompositeGradOpMaker,
                   ops::SoftmaxInplaceInferer,
                   SoftmaxInferShapeFunctor);
 DECLARE_INFER_SHAPE_FUNCTOR(softmax_grad,

diff --git a/paddle/fluid/prim/api/composite_backward/composite_backward_api.h b/paddle/fluid/prim/api/composite_backward/composite_backward_api.h
@@ -30,6 +30,35 @@ using Tensor = paddle::Tensor;
 using IntArray = paddle::experimental::IntArrayBase<paddle::Tensor>;
 //  This function should have as same signature as phi, which defined in
 //  paddle/phi/api/backward/backward_api.h
+template <typename T>
+void softmax_grad(const Tensor& out,
+                  const Tensor& out_grad,
+                  int axis,
+                  Tensor* x_grad) {
+  if (x_grad) {
+    if (out_grad.dims().size() > 0) {
+      if (axis >= 0) {
+        auto new_out_grad = out_grad * out;
+        auto tmp_x_grad = new_out_grad -
+                          out * sum<T>(new_out_grad, {axis}, out.dtype(), true);
+        set_output<T>(tmp_x_grad, x_grad);
+      } else {
+        auto new_out_grad = out_grad * out;
+        auto tmp_x_grad =
+            new_out_grad - out * sum<T>(new_out_grad,
+                                        {out.dims().size() + axis},
+                                        out.dtype(),
+                                        true);
+        set_output<T>(tmp_x_grad, x_grad);
+      }
+    } else {
+      set_output<T>(
+          full<T>(phi::vectorize(out_grad.dims()), 0.0, out_grad.dtype()),
+          x_grad);
+    }
+  }
+}
+
 template <typename T>
 void cast_grad(const Tensor& out_grad, DataType dtype, Tensor* x_grad) {
   if (x_grad) {

diff --git a/paddle/phi/api/yaml/legacy_backward.yaml b/paddle/phi/api/yaml/legacy_backward.yaml
@@ -1144,6 +1144,7 @@
     param : [out]
   kernel :
     func : softmax_grad
+  composite : softmax_grad(out, out_grad, axis, x_grad)
 
 - backward_op : spectral_norm_grad
   forward : spectral_norm (Tensor weight, Tensor u, Tensor v, int dim, int power_iters, float eps) -> Tensor(out)

diff --git a/python/paddle/fluid/tests/unittests/prim/composite_ops/test_composite_softmax_custom_vjp.py b/python/paddle/fluid/tests/unittests/prim/composite_ops/test_composite_softmax_custom_vjp.py
@@ -0,0 +1,200 @@
+# 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 unittest
+
+import numpy as np
+from utils import TOLERANCE
+
+import paddle
+import paddle.nn.functional as F
+from paddle.fluid import core
+
+
+def generate_data(shape, dtype="float32"):
+    np_data = np.random.random(shape).astype(dtype)
+    return np_data
+
+
+class Attr:
+    def __init__(self) -> None:
+        self.dtype = None
+        self.axis = -1
+        self.shape = None
+
+    def set_dtype(self, dtype) -> None:
+        self.dtype = dtype
+        return
+
+    def set_axis(self, axis) -> None:
+        self.axis = axis
+        return
+
+    def set_shape(self, shape) -> None:
+        self.shape = shape
+        return
+
+    def get_rtol(self, flag):
+        rtol = TOLERANCE[self.dtype][flag].get("rtol")
+        return rtol
+
+    def get_atol(self, flag):
+        atol = TOLERANCE[self.dtype][flag].get("atol")
+        return atol
+
+
+attrs = Attr()
+
+
+def fn(x):
+    return F.softmax(x, axis=attrs.axis, dtype=attrs.dtype)
+
+
+def expect_grad(inputs):
+    paddle.disable_static()
+    inputs.stop_gradient = False
+    res = fn(inputs)
+
+    gradients = paddle.grad(res, inputs)
+    return gradients
+
+
+class TestCompositeSoftmax(unittest.TestCase):
+    def setUp(self):
+        self.dtypes = ["float32", "float64"]
+        self.shapes = [[2, 3, 4], [2, 3]]
+        self.axes = [-1, 0, 1]
+
+    def cal_composite_grad(self, inputs):
+        paddle.enable_static()
+        core._set_prim_forward_enabled(True)
+        startup_program = paddle.static.Program()
+        main_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            x = paddle.static.data(
+                'x', shape=inputs.shape, dtype=str(inputs.dtype)
+            )
+            x.stop_gradient = False
+            y = fn(x)
+            blocks = main_program.blocks
+
+            fwd_ops = [op.type for op in blocks[0].ops]
+            # Ensure that softmax in original block
+            self.assertTrue('softmax' in fwd_ops)
+
+            paddle.incubate.autograd.primapi.to_prim(blocks)
+
+            fwd_ops_new = [op.type for op in blocks[0].ops]
+            # Ensure that softmax is splitted into small ops
+            self.assertTrue('softmax' not in fwd_ops_new)
+
+            z = paddle.static.gradients([y], x)
+            fwd_ops_grad = [op.type for op in blocks[0].ops]
+            # Ensure that softmax_grad not in grad block
+
+            self.assertTrue('softmax_grad' not in fwd_ops_grad)
+
+        exe = paddle.static.Executor()
+        exe.run(startup_program)
+        res = exe.run(main_program, feed={'x': inputs}, fetch_list=[z])
+        paddle.disable_static()
+        core._set_prim_forward_enabled(False)
+        return res
+
+    def compare_backward(self):
+        np_data = generate_data(attrs.shape)
+        tensor_data = paddle.to_tensor(np_data)
+
+        expect = expect_grad(tensor_data)[0].numpy()
+        actual = self.cal_composite_grad(np_data)[0]
+
+        assert expect.dtype == actual.dtype
+        np.testing.assert_allclose(
+            expect,
+            actual,
+            rtol=attrs.get_rtol("backward"),
+            atol=attrs.get_atol("backward"),
+        )
+
+    def test_backward(self):
+        for i in self.axes:
+            for j in self.dtypes:
+                for t in self.shapes:
+                    attrs.set_axis(i)
+                    attrs.set_dtype(j)
+                    attrs.set_shape(t)
+                    self.compare_backward()
+
+
+class TestCompositeSoftmaxPrimBackward(unittest.TestCase):
+    "test composite softmax and prim backward"
+
+    def setUp(self):
+        core._set_prim_backward_enabled(True)
+        self.dtypes = ["float32", "float64"]
+        self.shapes = [[], [2, 3, 4], [2, 3]]
+        self.axes = [-1, 0, 1]
+
+    def cal_composite_grad(self, inputs):
+        paddle.enable_static()
+        core._set_prim_all_enabled(True)
+        startup_program = paddle.static.Program()
+        main_program = paddle.static.Program()
+        with paddle.static.program_guard(main_program, startup_program):
+            x = paddle.static.data(
+                'x', shape=inputs.shape, dtype=str(inputs.dtype)
+            )
+            x.stop_gradient = False
+            y = fn(x)
+            blocks = main_program.blocks
+            z = paddle.static.gradients([y], x)
+            paddle.incubate.autograd.primapi.to_prim(blocks)
+
+        exe = paddle.static.Executor()
+        exe.run(startup_program)
+        res = exe.run(main_program, feed={'x': inputs}, fetch_list=[z])
+        paddle.disable_static()
+        core._set_prim_all_enabled(False)
+        return res
+
+    def compare_backward(self):
+        if not attrs.shape and attrs.axis not in [-1, 0]:
+            # op softmax does not support both case
+            return
+        np_data = generate_data(attrs.shape)
+        tensor_data = paddle.to_tensor(np_data)
+
+        expect = expect_grad(tensor_data)[0].numpy()
+        actual = self.cal_composite_grad(np_data)[0]
+
+        assert expect.dtype == actual.dtype
+        np.testing.assert_allclose(
+            expect,
+            actual,
+            rtol=attrs.get_rtol("prim_backward"),
+            atol=attrs.get_rtol("prim_backward"),
+        )
+
+    def test_prim_backward(self):
+        for i in self.axes:
+            for j in self.dtypes:
+                for t in self.shapes:
+                    attrs.set_axis(i)
+                    attrs.set_dtype(j)
+                    attrs.set_shape(t)
+                    self.compare_backward()
+
+
+if __name__ == '__main__':
+    unittest.main()