【Fix PIR Unittest No.213、277】Support to decompose squeeze_grad and unsqueeze_grad

paddle/fluid/primitive/codegen/gen.py

@@ -101,8 +101,10 @@
     'scatter_grad',
     'scatter_nd_add_grad',
     'slice_grad',
+    'squeeze_grad',
     'tile_grad',
     'topk_grad',
+    'unsqueeze_grad',
 ]
 
 # whole vjp list of primitive op vjp

paddle/fluid/primitive/rule/vjp/details.h

@@ -877,6 +877,51 @@ void softmax_grad(const Tensor& out,
   }
 }
 
+template <typename T>
+void squeeze_grad(const Tensor& xshape,
+                  const Tensor& out_grad,
+                  const IntArray& axis,
+                  Tensor* x_grad) {
+  if (x_grad) {
+    auto x_grad_out = unsqueeze<T>(out_grad, axis);
+    set_output<T>(x_grad_out, x_grad);
+  }
+}
+
+template <typename T>
+void unsqueeze_grad(const Tensor& xshape,
+                    const Tensor& out_grad,
+                    const IntArray& axis,
+                    Tensor* x_grad) {
+  // for xshape = [10, 2, 5], axis = [3, 1, 1], out_grad.shape = [10, 1, 1, 2,
+  // 5, 1], it outputs squeeze axis = [5, 2, 1]
+  const auto& IncreaseAxis = [](std::vector<int64_t>* axis_data,
+                                int64_t pivot) {
+    for (size_t i = 0; i < axis_data->size(); ++i) {
+      if ((*axis_data)[i] >= pivot) (*axis_data)[i] += 1;
+    }
+  };
+  const auto& GetRealAxis = [&](const IntArray& axis) -> decltype(auto) {
+    // for axis = [0, 3, 3], it outputs [0, 3, 3+1], because unsqueeze support
+    // duplicated axis.
+    std::vector<int64_t> output_axis;
+    const int64_t x_rank = xshape.dims().size() - 1;
+    const std::vector<int64_t> axis_data = axis.GetData();
+    for (size_t i = 0; i < axis_data.size(); ++i) {
+      int64_t value = axis_data[i];
+      if (value < 0) value += (x_rank + i + 1);
+      IncreaseAxis(&output_axis, value);
+      output_axis.push_back(value);
+    }
+    return output_axis;
+  };
+
+  if (x_grad) {
+    auto x_grad_out = squeeze<T>(out_grad, GetRealAxis(axis));
+    set_output<T>(x_grad_out, x_grad);
+  }
+}
+
 template <typename T>
 void matmul_grad(const Tensor& x,
                  const Tensor& y,

paddle/phi/infermeta/unary.cc

@@ -4286,16 +4286,6 @@ void SqueezeInferMeta(const MetaTensor& x,
                       MetaTensor* out,
                       MetaConfig config) {
   const auto& x_dims = x.dims();
-  // Check input tensor dims (<6) Eigen limit.
-  PADDLE_ENFORCE_LE(x_dims.size(),
-                    6,
-                    phi::errors::InvalidArgument(
-                        "The dimensions of Input(X) "
-                        "should be in the range of [1, 6] (Eigen limit)."
-                        "But received X's dimensions = %d, X's shape = [%s].",
-                        x_dims.size(),
-                        x_dims));
-
   if (!config.is_runtime && axes.FromTensor()) {
     // compile time infershape, set all elements to -1.
     int output_size = static_cast<int>(x.dims().size() - axes.GetData().size());

python/paddle/decomposition/decomp.py

@@ -213,7 +213,7 @@ def decompose(
         blacklist (frozenset): The Operators that will be exclude when decomposed into primitives.
         whitelist (frozenset): Only the operators in whitelist will be decomposed into primitives.
         start_index (int): The start index of decomposed operator in global block, default 0;
-        end_index (int): The end index of decomposed operator in global block, default -1 means all ops will be composed.
+        end_index (int): The end index of decomposed operator in global block, default -1 means all ops will be composed. start_index and end_index follow the principle of left closed and right open, that is [start_index, end_index).
 
     Returns:
         dst_vars (list): A list contains all vars which replace origin ones in src_vars.

test/deprecated/legacy_test/CMakeLists.txt

@@ -750,6 +750,7 @@ set_tests_properties(test_graph_send_uv_op PROPERTIES TIMEOUT 60)
 set_tests_properties(test_pretrained_model PROPERTIES TIMEOUT 120)
 set_tests_properties(test_model PROPERTIES TIMEOUT 300)
 set_tests_properties(test_pretrained_model PROPERTIES TIMEOUT 600)
+set_tests_properties(test_squeeze2_op_rename PROPERTIES TIMEOUT 120)
 
 if(APPLE)
   set_tests_properties(test_callback_early_stop PROPERTIES TIMEOUT 300)
@@ -772,11 +773,9 @@ set(TEST_CINN_OPS
     test_top_k_v2_op
     test_elementwise_mul_op
     test_gather_nd_op
-    test_squeeze2_op
     test_elementwise_pow_op
     test_transpose_op
     test_reshape_op
-    test_unsqueeze2_op
     test_meshgrid_op
     test_scale_op
     test_scatter_op

test/deprecated/legacy_test/test_squeeze2_op_rename.py

@@ -0,0 +1,102 @@
+#   Copyright (c) 2019 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 os
+import unittest
+
+from test_attribute_var import UnittestBase
+
+import paddle
+from paddle.base.framework import Program, program_guard
+
+paddle.enable_static()
+
+
+class TestSqueeze2AxesTensor(UnittestBase):
+    def init_info(self):
+        self.shapes = [[2, 3, 4]]
+        self.save_path = os.path.join(self.temp_dir.name, 'squeeze_tensor')
+
+    def test_static(self):
+        main_prog = Program()
+        startup_prog = Program()
+        with program_guard(main_prog, startup_prog):
+            fc = paddle.nn.Linear(4, 10)
+            x = paddle.randn([2, 3, 4])
+            x.stop_gradient = False
+            feat = fc(x)  # [2,3,10]
+            feat = paddle.unsqueeze(feat, [0, 2])  # [1, 2, 3, 1, 10]
+            # axes is a Variable
+            axes = paddle.assign([0, 2])
+            out = paddle.squeeze(feat, axes)
+            out2 = paddle.squeeze(feat, axes)
+
+            sgd = paddle.optimizer.SGD()
+            sgd.minimize(paddle.mean(out))
+            self.assertTrue("Var[" in str(main_prog))
+
+            exe = paddle.static.Executor()
+            exe.run(startup_prog)
+            res = exe.run(fetch_list=[feat, out, out2])
+            self.assertEqual(res[0].shape, (1, 2, 1, 3, 10))
+            self.assertEqual(res[1].shape, (2, 3, 10))
+            self.assertEqual(res[2].shape, (2, 3, 10))
+
+            paddle.static.save_inference_model(self.save_path, [x], [out], exe)
+            # Test for Inference Predictor
+            infer_out = self.infer_prog()
+            self.assertEqual(infer_out.shape, (2, 3, 10))
+
+
+class TestSqueeze2AxesTensorList(UnittestBase):
+    def init_info(self):
+        self.shapes = [[2, 3, 4]]
+        self.save_path = os.path.join(self.temp_dir.name, 'squeeze_tensor')
+
+    def test_static(self):
+        main_prog = Program()
+        startup_prog = Program()
+        with program_guard(main_prog, startup_prog):
+            fc = paddle.nn.Linear(4, 10)
+            x = paddle.randn([2, 3, 4])
+            x.stop_gradient = False
+            feat = fc(x)  # [2,3,10]
+            feat = paddle.unsqueeze(feat, [0, 2])  # [1, 2, 3, 1, 10]
+            # axes is a list[Variable]
+            axes = [
+                paddle.full([1], 0, dtype='int32'),
+                paddle.full([1], 2, dtype='int32'),
+            ]
+            out = paddle.squeeze(feat, axes)
+            out2 = paddle.squeeze(feat, axes)
+
+            sgd = paddle.optimizer.SGD()
+            sgd.minimize(paddle.mean(out))
+            self.assertTrue("Vars[" in str(main_prog))
+
+            exe = paddle.static.Executor()
+            exe.run(startup_prog)
+            res = exe.run(fetch_list=[feat, out, out2])
+            self.assertEqual(res[0].shape, (1, 2, 1, 3, 10))
+            self.assertEqual(res[1].shape, (2, 3, 10))
+            self.assertEqual(res[2].shape, (2, 3, 10))
+
+            paddle.static.save_inference_model(self.save_path, [x], [out], exe)
+            # Test for Inference Predictor
+            infer_out = self.infer_prog()
+            self.assertEqual(infer_out.shape, (2, 3, 10))
+
+
+if __name__ == "__main__":
+    unittest.main()

test/ir/pir/cinn/sub_graphs/test_sub_graph_59.py

@@ -114,7 +114,7 @@ def test_ast_prim_cinn(self):
         for st, cinn in zip(
             paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out)
         ):
-            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8)
+            np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-6)
 
 
 if __name__ == '__main__':

test/legacy_test/CMakeLists.txt

@@ -943,6 +943,8 @@ set(TEST_CINN_OPS
     test_group_norm_op
     test_tile_op
     test_sum_op
+    test_squeeze2_op
+    test_unsqueeze2_op
     test_elementwise_min_op
     test_take_along_axis_op
     test_strided_slice_op

test/deprecated/legacy_test/test_squeeze2_op.py → test/legacy_test/test_squeeze2_op.py

@@ -1,4 +1,4 @@
-#   Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
+#   Copyright (c) 2024 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.
@@ -12,16 +12,13 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-import os
 import unittest
 
 import numpy as np
 from op_test import OpTest, convert_float_to_uint16
-from test_attribute_var import UnittestBase
 
 import paddle
 from paddle.base import core
-from paddle.base.framework import Program, program_guard
 from paddle.pir_utils import test_with_pir_api
 
 paddle.enable_static()
@@ -31,7 +28,7 @@
 class TestSqueezeOp(OpTest):
     def setUp(self):
         self.op_type = "squeeze2"
-        self.prim_op_type = "comp"
+        self.prim_op_type = "prim"
         self.python_api = paddle.squeeze
         self.public_python_api = paddle.squeeze
         self.python_out_sig = [
@@ -58,7 +55,6 @@ def if_enable_cinn(self):
     def test_check_output(self):
         self.check_output(
             no_check_set=['XShape'],
-            check_prim=True,
             check_pir=True,
             check_prim_pir=True,
         )
@@ -67,7 +63,6 @@ def test_check_grad(self):
         self.check_grad(
             ["X"],
             "Out",
-            check_prim=True,
             check_pir=True,
             check_prim_pir=True,
         )
@@ -191,81 +186,6 @@ def init_dtype(self):
         self.dtype = np.uint16
 
 
-class TestSqueeze2AxesTensor(UnittestBase):
-    def init_info(self):
-        self.shapes = [[2, 3, 4]]
-        self.save_path = os.path.join(self.temp_dir.name, 'squeeze_tensor')
-
-    def test_static(self):
-        main_prog = Program()
-        startup_prog = Program()
-        with program_guard(main_prog, startup_prog):
-            fc = paddle.nn.Linear(4, 10)
-            x = paddle.randn([2, 3, 4])
-            x.stop_gradient = False
-            feat = fc(x)  # [2,3,10]
-            feat = paddle.unsqueeze(feat, [0, 2])  # [1, 2, 3, 1, 10]
-            # axes is a Variable
-            axes = paddle.assign([0, 2])
-            out = paddle.squeeze(feat, axes)
-            out2 = paddle.squeeze(feat, axes)
-
-            sgd = paddle.optimizer.SGD()
-            sgd.minimize(paddle.mean(out))
-            self.assertTrue("Var[" in str(main_prog))
-
-            exe = paddle.static.Executor()
-            exe.run(startup_prog)
-            res = exe.run(fetch_list=[feat, out, out2])
-            self.assertEqual(res[0].shape, (1, 2, 1, 3, 10))
-            self.assertEqual(res[1].shape, (2, 3, 10))
-            self.assertEqual(res[2].shape, (2, 3, 10))
-
-            paddle.static.save_inference_model(self.save_path, [x], [out], exe)
-            # Test for Inference Predictor
-            infer_out = self.infer_prog()
-            self.assertEqual(infer_out.shape, (2, 3, 10))
-
-
-class TestSqueeze2AxesTensorList(UnittestBase):
-    def init_info(self):
-        self.shapes = [[2, 3, 4]]
-        self.save_path = os.path.join(self.temp_dir.name, 'squeeze_tensor')
-
-    def test_static(self):
-        main_prog = Program()
-        startup_prog = Program()
-        with program_guard(main_prog, startup_prog):
-            fc = paddle.nn.Linear(4, 10)
-            x = paddle.randn([2, 3, 4])
-            x.stop_gradient = False
-            feat = fc(x)  # [2,3,10]
-            feat = paddle.unsqueeze(feat, [0, 2])  # [1, 2, 3, 1, 10]
-            # axes is a list[Variable]
-            axes = [
-                paddle.full([1], 0, dtype='int32'),
-                paddle.full([1], 2, dtype='int32'),
-            ]
-            out = paddle.squeeze(feat, axes)
-            out2 = paddle.squeeze(feat, axes)
-
-            sgd = paddle.optimizer.SGD()
-            sgd.minimize(paddle.mean(out))
-            self.assertTrue("Vars[" in str(main_prog))
-
-            exe = paddle.static.Executor()
-            exe.run(startup_prog)
-            res = exe.run(fetch_list=[feat, out, out2])
-            self.assertEqual(res[0].shape, (1, 2, 1, 3, 10))
-            self.assertEqual(res[1].shape, (2, 3, 10))
-            self.assertEqual(res[2].shape, (2, 3, 10))
-
-            paddle.static.save_inference_model(self.save_path, [x], [out], exe)
-            # Test for Inference Predictor
-            infer_out = self.infer_prog()
-            self.assertEqual(infer_out.shape, (2, 3, 10))
-
-
 # test api
 class TestSqueezeAPI(unittest.TestCase):
     def setUp(self):

test/deprecated/legacy_test/test_unsqueeze2_op.py → test/legacy_test/test_unsqueeze2_op.py

@@ -1,4 +1,4 @@
-# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+# Copyright (c) 2024 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.
@@ -37,7 +37,7 @@ def setUp(self):
             "Out": self.inputs["X"].reshape(self.new_shape),
             "XShape": np.random.random(self.ori_shape).astype("float64"),
         }
-        self.prim_op_type = "comp"
+        self.prim_op_type = "prim"
         self.if_enable_cinn()
 
     def if_enable_cinn(self):
@@ -46,7 +46,6 @@ def if_enable_cinn(self):
     def test_check_output(self):
         self.check_output(
             no_check_set=["XShape"],
-            check_prim=True,
             check_pir=True,
             check_prim_pir=True,
         )
@@ -55,7 +54,6 @@ def test_check_grad(self):
         self.check_grad(
             ["X"],
             "Out",
-            check_prim=True,
             check_pir=True,
             check_prim_pir=True,
         )