[IPU] add more loss ops

paddle/fluid/platform/device/ipu/popart_canonicalization/loss_ops.cc

@@ -278,6 +278,123 @@ Node *kldiv_loss_handler(Graph *graph, Node *node) {
   return loss;
 }
 
+Node *sigmoid_cross_entropy_with_logits_handler(Graph *graph, Node *node) {
+  // Out = max(logits, 0) - logits * label + log(1 + exp(-abs(logits)))
+  auto *op = node->Op();
+  int reduction = 2;
+  if (is_dynamic_graph()) {
+    reduction = RemoveTailReduction(graph, node, "Out");
+  }
+  bool append_identity_loss =
+      is_dynamic_graph() && IsLastVarNode(GetOutputVarNode("Out", node));
+
+  auto logits = GetInputVarNode("X", node);
+  auto label = GetInputVarNode("Label", node);
+  // sigmoid_cross_entropy_with_logits uses float label as input.
+  auto ignore_index_value =
+      static_cast<float>(PADDLE_GET_CONST(int, op->GetAttr("ignore_index")));
+  auto normalize = PADDLE_GET_CONST(bool, op->GetAttr("normalize"));
+
+  // const
+  auto one = CreateConst(
+                 graph, node, std::vector<float>{1.0}, {1}, GetVarDType(logits))
+                 ->outputs.front();
+  auto zero =
+      CreateConst(
+          graph, node, std::vector<float>{0.0}, {1}, GetVarDType(logits))
+          ->outputs.front();
+  auto ignore_index = CreateConst(graph,
+                                  node,
+                                  std::vector<float>{ignore_index_value},
+                                  {1},
+                                  GetVarDType(label))
+                          ->outputs.front();
+  // max(logits, 0)
+  auto max_zero =
+      CreateBaseOp(graph, node, "popart_max", {logits, zero}, {}, {})
+          ->outputs.front();
+
+  // logits * label
+  auto mul = CreateBaseOp(graph, node, "popart_mul", {logits, label}, {}, {})
+                 ->outputs.front();
+
+  // abs(logits)
+  auto abs = CreateBaseOp(graph, node, "popart_abs", {logits}, {}, {})
+                 ->outputs.front();
+  // -abs(logits)
+  auto neg_abs =
+      CreateBaseOp(graph, node, "popart_neg", {abs}, {}, {})->outputs.front();
+  // exp(-abs(logits))
+  auto exp_neg_abs = CreateBaseOp(graph, node, "popart_exp", {neg_abs}, {}, {})
+                         ->outputs.front();
+  // 1+exp(-abs(logits))
+  auto log_term =
+      CreateBaseOp(graph, node, "popart_add", {exp_neg_abs, one}, {}, {})
+          ->outputs.front();
+  // log(1+exp(-abs(logits)))
+  auto log = CreateBaseOp(graph, node, "popart_log", {log_term}, {}, {})
+                 ->outputs.front();
+
+  // max(logits, 0) - logits * label
+  auto sub = CreateBaseOp(graph, node, "popart_sub", {max_zero, mul}, {}, {})
+                 ->outputs.front();
+  // max(logits, 0) - logits * label + log(1 + exp(-abs(logits)))
+  auto loss = CreateBaseOp(graph, node, "popart_add", {sub, log}, {}, {})
+                  ->outputs.front();
+
+  // label == ignore_index ? 0 : loss
+  auto equal_cond =
+      CreateBaseOp(graph, node, "popart_equal", {label, ignore_index}, {}, {})
+          ->outputs.front();
+  loss = CreateBaseOp(graph,
+                      node,
+                      "popart_where",
+                      {equal_cond, zero, loss},
+                      append_identity_loss || normalize
+                          ? std::vector<Node *>{}
+                          : std::vector<Node *>{GetOutputVarNode("Out", node)},
+                      {});
+
+  if (normalize) {
+    // normalize the output as: loss = loss / sum(label != ignore_index)
+    auto not_equal =
+        CreateBaseOp(graph, node, "popart_logical_not", {equal_cond}, {}, {})
+            ->outputs.front();
+    auto mask =
+        CreateCast(graph, node, {not_equal}, {}, logits->Var()->GetDataType())
+            ->outputs.front();
+    auto sum = CreateBaseOp(graph,
+                            node,
+                            "popart_reducesum",
+                            {mask},
+                            {},
+                            {{"keepdims", int64_t{0}}})
+                   ->outputs.front();
+    auto eps =
+        CreateConst(
+            graph, node, std::vector<float>{1e-5}, {1}, GetVarDType(logits))
+            ->outputs.front();
+    // avoid division by zero
+    auto add_eps = CreateBaseOp(graph, node, "popart_add", {sum, eps}, {}, {})
+                       ->outputs.front();
+    loss =
+        CreateBaseOp(graph,
+                     node,
+                     "popart_div",
+                     {loss->outputs[0], add_eps},
+                     append_identity_loss
+                         ? std::vector<Node *>{}
+                         : std::vector<Node *>{GetOutputVarNode("Out", node)},
+                     {});
+  }
+
+  if (append_identity_loss) {
+    loss = CreateIdentityLossOp(
+        graph, node, loss->outputs, {GetOutputVarNode("Out", node)}, reduction);
+  }
+  return loss;
+}
+
 Node *binary_cross_entropy_handler(Graph *graph, Node *node) {
   // Out = -1 * weight * (label * log(x) + (1 - label) * log(1 - x))
   int reduction = 2;
@@ -493,6 +610,97 @@ Node *warpctc_handler(Graph *graph, Node *node) {
   return loss;
 }
 
+Node *rank_loss_handler(Graph *graph, Node *node) {
+  // (1.0f + (left - right).exp()).log() - label * (left - right)
+  auto label = GetInputVarNode("Label", node);
+  auto left = GetInputVarNode("Left", node);
+  auto right = GetInputVarNode("Right", node);
+  auto output = GetOutputVarNode("Out", node);
+  int reduction = 2;
+  if (is_dynamic_graph()) {
+    reduction = RemoveTailReduction(graph, node, "Out");
+  }
+  bool append_identity_loss = is_dynamic_graph() && IsLastVarNode(output);
+
+  auto sub = CreateBaseOp(graph, node, "popart_sub", {left, right}, {}, {})
+                 ->outputs.front();
+  auto mul = CreateBaseOp(graph, node, "popart_mul", {label, sub}, {}, {})
+                 ->outputs.front();
+  // const
+  auto one =
+      CreateConst(graph, node, std::vector<float>{1.0}, {1}, GetVarDType(label))
+          ->outputs.front();
+  auto exp =
+      CreateBaseOp(graph, node, "popart_exp", {sub}, {}, {})->outputs.front();
+  auto add = CreateBaseOp(graph, node, "popart_add", {one, exp}, {}, {})
+                 ->outputs.front();
+  auto log =
+      CreateBaseOp(graph, node, "popart_log", {add}, {}, {})->outputs.front();
+  auto loss = CreateBaseOp(graph,
+                           node,
+                           "popart_sub",
+                           {log, mul},
+                           append_identity_loss ? std::vector<Node *>{}
+                                                : std::vector<Node *>{output},
+                           {})
+                  ->outputs.front();
+  if (append_identity_loss) {
+    loss =
+        CreateIdentityLossOp(graph, node, loss->outputs, {output}, reduction);
+  }
+  return loss;
+}
+
+Node *margin_rank_loss_handler(Graph *graph, Node *node) {
+  // rank_loss = max(0, -label * (left - right) + margin)
+  auto *op = node->Op();
+  auto label = GetInputVarNode("Label", node);
+  auto left = GetInputVarNode("X1", node);
+  auto right = GetInputVarNode("X2", node);
+  auto output = GetOutputVarNode("Out", node);
+  auto margin_value = PADDLE_GET_CONST(float, op->GetAttr("margin"));
+  int reduction = 2;
+  if (is_dynamic_graph()) {
+    reduction = RemoveTailReduction(graph, node, "Out");
+  }
+  bool append_identity_loss = is_dynamic_graph() && IsLastVarNode(output);
+
+  // -(left - right)
+  auto sub = CreateBaseOp(graph, node, "popart_sub", {right, left}, {}, {})
+                 ->outputs.front();
+  // -label * (left - right)
+  auto mul = CreateBaseOp(graph, node, "popart_mul", {label, sub}, {}, {})
+                 ->outputs.front();
+  // const
+  auto zero =
+      CreateConst(graph, node, std::vector<float>{0.0}, {1}, GetVarDType(label))
+          ->outputs.front();
+  auto margin = CreateConst(graph,
+                            node,
+                            std::vector<float>{margin_value},
+                            {1},
+                            GetVarDType(label))
+                    ->outputs.front();
+  auto margin_add =
+      CreateBaseOp(graph, node, "popart_add", {mul, margin}, {}, {})
+          ->outputs.front();
+
+  // max(0, term)
+  auto loss = CreateBaseOp(graph,
+                           node,
+                           "popart_max",
+                           {zero, margin_add},
+                           append_identity_loss ? std::vector<Node *>{}
+                                                : std::vector<Node *>{output},
+                           {})
+                  ->outputs.front();
+  if (append_identity_loss) {
+    loss =
+        CreateIdentityLossOp(graph, node, loss->outputs, {output}, reduction);
+  }
+  return loss;
+}
+
 }  // namespace
 }  // namespace ipu
 }  // namespace platform
@@ -502,7 +710,11 @@ REGISTER_HANDLER(identity_loss, identity_loss_handler);
 REGISTER_HANDLER(softmax_with_cross_entropy,
                  softmax_with_cross_entropy_handler);
 REGISTER_HANDLER(cross_entropy2, cross_entropy2_handler);
+REGISTER_HANDLER(sigmoid_cross_entropy_with_logits,
+                 sigmoid_cross_entropy_with_logits_handler);
 REGISTER_HANDLER(kldiv_loss, kldiv_loss_handler);
 REGISTER_HANDLER(bce_loss, binary_cross_entropy_handler);
 REGISTER_HANDLER(huber_loss, huber_loss_handler);
 REGISTER_HANDLER(warpctc, warpctc_handler);
+REGISTER_HANDLER(rank_loss, rank_loss_handler);
+REGISTER_HANDLER(margin_rank_loss, margin_rank_loss_handler);

python/paddle/fluid/tests/unittests/ipu/test_dy2static_ipu.py

@@ -70,8 +70,8 @@ def set_op_attrs(self):
         self.loss_op = paddle.fluid.layers.cross_entropy
 
     def set_data_feed(self):
-        self.data = paddle.uniform((32, 3, 10, 10), dtype='float32')
-        self.label = paddle.randint(0, 10, shape=[32], dtype='int64')
+        self.data = paddle.uniform((8, 3, 10, 10), dtype='float32')
+        self.label = paddle.randint(0, 10, shape=[8], dtype='int64')
 
     def create_model(self, use_ipu=False):
         return SimpleLayer(loss_op=self.loss_op,
@@ -215,8 +215,8 @@ def set_op_attrs(self):
         self.loss_op = paddle.fluid.layers.softmax_with_cross_entropy
 
     def set_data_feed(self):
-        self.data = paddle.uniform((32, 3, 10, 10), dtype='float32')
-        self.label = paddle.randint(0, 10, shape=[32, 1], dtype='int64')
+        self.data = paddle.uniform((8, 3, 10, 10), dtype='float32')
+        self.label = paddle.randint(0, 10, shape=[8, 1], dtype='int64')
 
     def create_model(self, use_ipu=False):
         return SimpleLayer(loss_op=self.loss_op,
@@ -231,8 +231,41 @@ def set_op_attrs(self):
         self.loss_op = partial(paddle.fluid.layers.kldiv_loss, reduction="none")
 
     def set_data_feed(self):
-        self.data = paddle.uniform((32, 3, 10, 10), dtype='float32')
-        self.label = paddle.rand(shape=[32, 81], dtype='float32')
+        self.data = paddle.uniform((8, 3, 10, 10), dtype='float32')
+        self.label = paddle.rand(shape=[8, 81], dtype='float32')
+
+    def create_model(self, use_ipu=False):
+        return SimpleLayer(loss_op=self.loss_op,
+                           use_softmax=True,
+                           use_reduction=True,
+                           use_identity_loss=False)
+
+
+class TestWithoutIdentityLoss4(TestBase):
+
+    def set_op_attrs(self):
+        self.loss_op = paddle.nn.functional.binary_cross_entropy
+
+    def set_data_feed(self):
+        self.data = paddle.uniform((8, 3, 10, 10), dtype='float32')
+        self.label = paddle.rand(shape=[8, 81], dtype='float32')
+
+    def create_model(self, use_ipu=False):
+        return SimpleLayer(loss_op=self.loss_op,
+                           use_softmax=True,
+                           use_reduction=False,
+                           use_identity_loss=False)
+
+
+class TestWithoutIdentityLoss5(TestBase):
+
+    def set_op_attrs(self):
+        self.loss_op = paddle.fluid.layers.sigmoid_cross_entropy_with_logits
+
+    def set_data_feed(self):
+        self.data = paddle.uniform((8, 3, 10, 10), dtype='float32')
+        self.label = paddle.randint(0, 10, shape=[8, 81],
+                                    dtype='int64').astype('float32')
 
     def create_model(self, use_ipu=False):
         return SimpleLayer(loss_op=self.loss_op,

python/paddle/fluid/tests/unittests/ipu/test_margin_rank_loss_op_ipu.py

@@ -0,0 +1,90 @@
+#  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
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest
+
+
+class TestBase(IPUOpTest):
+
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    def set_data_feed(self):
+        label = np.random.uniform(size=[3, 1])
+        left = np.random.uniform(size=[3, 1])
+        right = np.random.uniform(size=[3, 1])
+        self.feed_fp32 = {
+            "label": label.astype(np.float32),
+            "left": left.astype(np.float32),
+            "right": right.astype(np.float32),
+        }
+        self.feed_fp16 = {
+            "label": label.astype(np.float16),
+            "left": left.astype(np.float16),
+            "right": right.astype(np.float16),
+        }
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed_fp32.values()]
+        self.feed_list = list(self.feed_fp32.keys())
+
+    def set_op_attrs(self):
+        self.attrs = {
+            'margin': 0.1,
+        }
+
+    @IPUOpTest.static_graph
+    def build_model(self, on_ipu):
+        label = paddle.static.data(name=self.feed_list[0],
+                                   shape=self.feed_shape[0],
+                                   dtype="float32")
+        left = paddle.static.data(name=self.feed_list[1],
+                                  shape=self.feed_shape[1],
+                                  dtype='float32')
+        right = paddle.static.data(name=self.feed_list[2],
+                                   shape=self.feed_shape[2],
+                                   dtype='float32')
+        out = paddle.fluid.layers.margin_rank_loss(label, left, right)
+        self.fetch_list = [out.name]
+
+    def run_model(self, exec_mode):
+        self.run_op_test(exec_mode)
+
+    def test(self):
+        for m in IPUOpTest.ExecutionMode:
+            if not self.skip_mode(m):
+                self.build_model(self.is_ipu_mode(m))
+                self.run_model(m)
+        self.check()
+
+
+class TestCase1(TestBase):
+
+    def set_op_attrs(self):
+        self.attrs = {
+            'margin': 0.5,
+        }
+
+
+if __name__ == "__main__":
+    unittest.main()