[IPU] add more ops (#40691)

* add more ops

* add authors
Co-authored-by: NXiaobing Wang <xiaobingw@graphcore.ai>
Co-authored-by: NAllen Guo <alleng@graphcore.ai>
Co-authored-by: NZhixin Yao <zhixiny@graphcore.ai>
Co-authored-by: NZhaorui Chen <zhaoruic@graphcore.ai>
Co-authored-by: NHan Zhao <hanzhao@graphcore.ai>

* rm ipu_strategy.check()

* fix UT fail

* fix typo
Co-authored-by: NXiaobing Wang <xiaobingw@graphcore.ai>
Co-authored-by: NZhixin Yao <zhixiny@graphcore.ai>
Co-authored-by: NZhaorui Chen <zhaoruic@graphcore.ai>
Co-authored-by: NHan Zhao <hanzhao@graphcore.ai>

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

@@ -34,15 +34,36 @@ Node *logical_not_handler(Graph *graph, Node *node) {
                       {GetOutputVarNode("Out", node)}, {});
 }
 
+Node *logical_or_handler(Graph *graph, Node *node) {
+  return CreateBaseOp(graph, node, "popart_logical_or",
+                      {GetInputVarNode("X", node), GetInputVarNode("Y", node)},
+                      {GetOutputVarNode("Out", node)}, {});
+}
+
+Node *logical_and_handler(Graph *graph, Node *node) {
+  return CreateBaseOp(graph, node, "popart_logical_and",
+                      {GetInputVarNode("X", node), GetInputVarNode("Y", node)},
+                      {GetOutputVarNode("Out", node)}, {});
+}
+
 Node *greater_than_handler(Graph *graph, Node *node) {
   return CreateBaseOp(graph, node, "popart_greater",
                       {GetInputVarNode("X", node), GetInputVarNode("Y", node)},
                       {GetOutputVarNode("Out", node)}, {});
 }
 
+Node *less_than_handler(Graph *graph, Node *node) {
+  return CreateBaseOp(graph, node, "popart_less",
+                      {GetInputVarNode("X", node), GetInputVarNode("Y", node)},
+                      {GetOutputVarNode("Out", node)}, {});
+}
+
 REGISTER_HANDLER(equal, equal_handler);
 REGISTER_HANDLER(logical_not, logical_not_handler);
+REGISTER_HANDLER(logical_or, logical_or_handler);
+REGISTER_HANDLER(logical_and, logical_and_handler);
 REGISTER_HANDLER(greater_than, greater_than_handler);
+REGISTER_HANDLER(less_than, less_than_handler);
 
 }  // namespace
 }  // namespace ipu

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

@@ -98,6 +98,12 @@ Node *matmul_handler(Graph *graph, Node *node) {
   if (x_rank == 1) {
     perm = std::vector<int64_t>{0};
   } else if (x_rank == 2) {
+    if (!transpose_x && !transpose_y && is_float_equal(alpha, 1.0f)) {
+      return CreateBaseOp(
+          graph, node, "popart_matmul",
+          {GetInputVarNode("X", node), GetInputVarNode("Y", node)},
+          node->outputs);
+    }
     return CreateGemm(graph, node,
                       {GetInputVarNode("X", node), GetInputVarNode("Y", node)},
                       node->outputs, transpose_x, transpose_y, alpha);

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

@@ -54,10 +54,36 @@ Node *checkpointoutput_handler(Graph *graph, Node *node) {
                       node->outputs);
 }
 
+Node *custom_nll_loss_handler(Graph *graph, Node *node) {
+  auto *op = node->Op();
+  auto reduction = BOOST_GET_CONST(int, op->GetAttr("reduction"));
+  auto ignoreIndex = BOOST_GET_CONST(int, op->GetAttr("ignoreIndex"));
+  auto inputIsLogProbability =
+      BOOST_GET_CONST(bool, op->GetAttr("inputIsLogProbability"));
+  return CreateBaseOp(graph, node, "popart_nllloss_v2", node->inputs,
+                      node->outputs,
+                      {{"reduction", reduction},
+                       {"ignoreIndex", ignoreIndex},
+                       {"inputIsLogProbability", inputIsLogProbability}});
+}
+
+Node *identity_handler(Graph *graph, Node *node) {
+  return CreateBaseOp(graph, node, "popart_identity", node->inputs,
+                      node->outputs);
+}
+
+Node *detach_handler(Graph *graph, Node *node) {
+  return CreateBaseOp(graph, node, "popart_detach_v2", node->inputs,
+                      node->outputs);
+}
+
 REGISTER_HANDLER(custom_op, custom_op_handler);
 REGISTER_HANDLER(print, print_handler);
 REGISTER_HANDLER(popart_optimizer, popart_optimizer_handler);
 REGISTER_HANDLER(checkpointoutput, checkpointoutput_handler);
+REGISTER_HANDLER(custom_nll_loss, custom_nll_loss_handler);
+REGISTER_HANDLER(identity, identity_handler);
+REGISTER_HANDLER(detach, detach_handler);
 
 }  // namespace
 }  // namespace ipu

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

@@ -49,6 +49,9 @@ Node *fill_constant_handler(Graph *graph, Node *node) {
     case framework::proto::VarType::INT64:
       value = std::vector<int64_t>(size, value_);
       break;
+    case framework::proto::VarType::BOOL:
+      value = std::vector<bool>(size, value_);
+      break;
     default:
       PADDLE_THROW(
           platform::errors::Unimplemented("fill_constant dtype: %d", dtype_));
@@ -417,6 +420,45 @@ Node *assign_handler(Graph *graph, Node *node) {
                       {GetOutputVarNode("Out", node)}, {});
 }
 
+Node *assign_value_handler(Graph *graph, Node *node) {
+  auto *op = node->Op();
+  auto dtype_ = BOOST_GET_CONST(int, op->GetAttr("dtype"));
+  auto dtype = VarType2OnnxDtype(dtype_);
+  auto dims_ = BOOST_GET_CONST(std::vector<int>, op->GetAttr("shape"));
+  std::vector<int64_t> dims(dims_.begin(), dims_.end());
+  Attribute values;
+  std::string value_name;
+  switch (dtype_) {
+    case framework::proto::VarType::BOOL: {
+      value_name = "bool_values";
+      auto vec_int = BOOST_GET_CONST(std::vector<int>, op->GetAttr(value_name));
+      std::vector<bool> vec_bool(vec_int.begin(), vec_int.end());
+      values = vec_bool;
+    } break;
+    case framework::proto::VarType::INT32:
+      value_name = "int32_values";
+      values = BOOST_GET_CONST(std::vector<int>, op->GetAttr(value_name));
+      break;
+    case framework::proto::VarType::FP32:
+      value_name = "fp32_values";
+      values = BOOST_GET_CONST(std::vector<float>, op->GetAttr(value_name));
+      break;
+    case framework::proto::VarType::INT64:
+      value_name = "int64_values";
+      values = BOOST_GET_CONST(std::vector<int64_t>, op->GetAttr(value_name));
+      break;
+    default:
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "Unsupported data type(code %d) for AssignValue operator, only "
+          "supports bool, int32, float32 and int64.",
+          dtype));
+  }
+  return CreateConst(graph, node, node->inputs, node->outputs,
+                     AttributeMap{
+                         {"value", values}, {"dims", dims}, {"dtype", dtype},
+                     });
+}
+
 Node *fill_any_like_handler(Graph *graph, Node *node) {
   auto *op = node->Op();
   auto value = BOOST_GET_CONST(float, op->GetAttr("value"));
@@ -482,6 +524,41 @@ Node *one_hot_handler(Graph *graph, Node *node) {
   }
 }
 
+Node *one_hot_v2_handler(Graph *graph, Node *node) {
+  auto *op = node->Op();
+  auto depth = BOOST_GET_CONST(int, op->GetAttr("depth"));
+  auto allow_out_of_range =
+      BOOST_GET_CONST(bool, op->GetAttr("allow_out_of_range"));
+  if (allow_out_of_range) {
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "Do not support allow_out_of_range=True"));
+  } else {
+    auto depth_tensor =
+        CreateConst(graph, node, {}, {}, {{"value", std::vector<int>{depth}},
+                                          {"dims", std::vector<int64_t>{1}},
+                                          {"dtype", ONNXDataType::INT32}});
+    Node *value_tensor = nullptr;
+    if (GetOutputVarNode("Out", node)->Var()->GetDataType() ==
+        framework::proto::VarType::FP16) {
+      value_tensor =
+          CreateConst(graph, node, {}, {}, {{"value", std::vector<float>{0, 1}},
+                                            {"dims", std::vector<int64_t>{2}},
+                                            {"dtype", ONNXDataType::FLOAT16}});
+    } else {
+      value_tensor =
+          CreateConst(graph, node, {}, {}, {{"value", std::vector<float>{0, 1}},
+                                            {"dims", std::vector<int64_t>{2}},
+                                            {"dtype", ONNXDataType::FLOAT}});
+    }
+
+    return CreateBaseOp(graph, node, "popart_onehot",
+                        {GetInputVarNode("X", node), depth_tensor->outputs[0],
+                         value_tensor->outputs[0]},
+                        {GetOutputVarNode("Out", node)},
+                        {{"axis", int64_t{-1}}});
+  }
+}
+
 Node *split_handler(Graph *graph, Node *node) {
   auto *op = node->Op();
   auto axis = BOOST_GET_CONST(int, op->GetAttr("axis"));
@@ -510,10 +587,12 @@ REGISTER_HANDLER(shape, shape_handler);
 REGISTER_HANDLER(slice, slice_handler);
 REGISTER_HANDLER(expand, expand_handler);
 REGISTER_HANDLER(assign, assign_handler);
+REGISTER_HANDLER(assign_value, assign_value_handler);
 REGISTER_HANDLER(fill_any_like, fill_any_like_handler);
 REGISTER_HANDLER(lookup_table_v2, lookup_table_v2_handler);
 REGISTER_HANDLER(split, split_handler);
 REGISTER_HANDLER(one_hot, one_hot_handler);
+REGISTER_HANDLER(one_hot_v2, one_hot_v2_handler);
 
 }  // namespace
 }  // namespace ipu

python/paddle/fluid/compiler.py

@@ -542,7 +542,7 @@ class IpuStrategy(object):
     def set_graph_config(self,
                          num_ipus=1,
                          is_training=True,
-                         batch_size=1,
+                         micro_batch_size=1,
                          enable_manual_shard=False):
         """
         Set graph configuration to the IpuStrategy instance.
@@ -571,7 +571,7 @@ class IpuStrategy(object):
                 ipu_strategy = static.IpuStrategy()
                 ipu_strategy.set_graph_config(num_ipus=1,
                                             is_training=True,
-                                            batch_size=1,
+                                            micro_batch_size=1,
                                             enable_manual_shard=False)
         """
         if num_ipus == 1 and enable_manual_shard:
@@ -581,7 +581,7 @@ class IpuStrategy(object):
         options = {
             'num_ipus': num_ipus,
             'is_training': is_training,
-            'micro_batch_size': batch_size,
+            'micro_batch_size': micro_batch_size,
             'enable_manual_shard': enable_manual_shard,
         }
         self.set_options(options)
@@ -589,6 +589,7 @@ class IpuStrategy(object):
     def set_pipelining_config(self,
                               enable_pipelining=False,
                               batches_per_step=1,
+                              enable_gradient_accumulation=False,
                               accumulation_factor=1):
         """
         Set pipelining configuration to the IpuStrategy instance. Used to optimize the throughput performance.
@@ -598,6 +599,8 @@ class IpuStrategy(object):
                 Default False, which means disabled.
             batches_per_step (int, optional): Set the batches per run in data pipelining mode. Only if enable_pipelining=True, batches_per_step is able to be set > 1.
                 Default 1, which means no data pipelining.
+            enable_gradient_accumulation (bool, optional): Enable to accumulate gradients before updating the weights in training mode. Only if enable_pipelining=True,
+                enable_gradient_accumulation is able to be set True. Default False, which means no gradient accumulation. 
             accumulation_factor (int, optional): Specify the number of micro-batches to accumulate 
                 before applying the varUpdate. Default 1, which means disable the accumulation.
         
@@ -617,6 +620,7 @@ class IpuStrategy(object):
                 ipu_strategy = static.IpuStrategy()
                 ipu_strategy.set_pipelining_config(enable_pipelining=False,
                                                     batches_per_step=1,
+                                                    enable_gradient_accumulation=False,
                                                     accumulation_factor=1)
         """
         enable_manual_shard = self.get_option('enable_manual_shard')
@@ -627,6 +631,7 @@ class IpuStrategy(object):
         options = {
             'enable_pipelining': enable_pipelining,
             'batches_per_step': batches_per_step,
+            'enable_gradient_accumulation': enable_gradient_accumulation,
             'accumulation_factor': accumulation_factor,
         }
         self.set_options(options)
@@ -754,6 +759,56 @@ class IpuStrategy(object):
         """
         return self._ipu_strategy.get_option(option)['value']
 
+    def enable_pattern(self, pattern):
+        """
+        Enable PopART pattern to optimize the graph.
+
+        Args:
+            pattern(string): the name of the pattern.
+        
+        Returns:
+            None.
+
+        Examples:
+            .. code-block:: python
+
+                # required: ipu
+
+                import paddle
+                import paddle.static as static
+
+                paddle.enable_static()
+
+                ipu_strategy = static.IpuStrategy()
+                ipu_strategy.enable_pattern("ViewSimplifyPattern")
+        """
+        self._ipu_strategy.enable_pattern(pattern)
+
+    def disable_pattern(self, pattern):
+        """
+        Disable PopART pattern.
+
+        Args:
+            pattern(string): the name of the pattern.
+        
+        Returns:
+            None.
+
+        Examples:
+            .. code-block:: python
+
+                # required: ipu
+
+                import paddle
+                import paddle.static as static
+
+                paddle.enable_static()
+
+                ipu_strategy = static.IpuStrategy()
+                ipu_strategy.disable_pattern("ViewSimplifyPattern")
+        """
+        self._ipu_strategy.disable_pattern(pattern)
+
     @property
     def num_ipus(self):
         """
@@ -817,8 +872,8 @@ class IpuCompiledProgram(object):
             main_prog = static.default_main_program()
             
             ipu_strategy = static.IpuStrategy()
-            ipu_strategy.set_graph_config(num_ipus=1, is_training=True, batch_size=1)
-            ipu_strategy.set_pipelining_config(enable_pipelining=False, batches_per_step=1, accumulation_factor=1)
+            ipu_strategy.set_graph_config(num_ipus=1, is_training=True, micro_batch_size=1)
+            ipu_strategy.set_pipelining_config(enable_pipelining=False, batches_per_step=1, enable_gradient_accumulation=False, accumulation_factor=1)
             ipu_strategy.set_precision_config(enable_fp16=False)
             
             ipu_compiled_program = static.IpuCompiledProgram(
@@ -891,8 +946,8 @@ class IpuCompiledProgram(object):
                 main_prog = static.default_main_program()
 
                 ipu_strategy = static.IpuStrategy()
-                ipu_strategy.set_graph_config(num_ipus=1, is_training=True, batch_size=1)
-                ipu_strategy.set_pipelining_config(enable_pipelining=False, batches_per_step=1, accumulation_factor=1)
+                ipu_strategy.set_graph_config(num_ipus=1, is_training=True, micro_batch_size=1)
+                ipu_strategy.set_pipelining_config(enable_pipelining=False, batches_per_step=1, enable_gradient_accumulation=False, accumulation_factor=1)
                 ipu_strategy.set_precision_config(enable_fp16=False)
                 
                 program = static.IpuCompiledProgram(

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

@@ -98,5 +98,117 @@ class TestBase(IPUOpTest):
         self.check(output_dict)
 
 
+class TestAssignFp32Value(TestBase):
+    def set_data_feed(self):
+        data = np.random.uniform(size=[2, 3, 1])
+        self.feed_fp32 = {'in_0': data.astype(np.float32)}
+        self.feed_fp16 = {'in_0': data.astype(np.float16)}
+
+        data = np.random.uniform(size=[2, 3, 1])
+        self.assign_fp32 = data.astype(np.float32)
+
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype='float32')
+
+                assign = paddle.assign(self.assign_fp32)
+                out = paddle.fluid.layers.elementwise_add(x, assign)
+
+                fetch_list = [out.name]
+
+            if exec_mode == ExecutionMode.CPU_FP32:
+                place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if exec_mode != ExecutionMode.CPU_FP32:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+            return result[0]
+
+
+class TestAssignBoolValue(TestBase):
+    def set_data_feed(self):
+        data = np.random.uniform(size=[2, 3, 1])
+        self.feed_fp32 = {'in_0': data.astype(np.float32)}
+        self.feed_fp16 = {'in_0': data.astype(np.float16)}
+        data = np.random.choice([True, False], size=(2, 3, 1))
+        self.assign_bool = data.astype(np.bool)
+
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype='float32')
+                x = paddle.less_than(x, x)
+                assign = paddle.assign(self.assign_bool)
+                out = paddle.logical_and(x, assign)
+                out = paddle.cast(out, 'float32')
+
+                fetch_list = [out.name]
+
+            if exec_mode == ExecutionMode.CPU_FP32:
+                place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if exec_mode != ExecutionMode.CPU_FP32:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            feed = self.feed_fp32
+            if exec_mode > ExecutionMode.IPU_FP32:
+                feed = self.feed_fp16
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+            return result[0]
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -22,33 +22,18 @@ from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMod
 
 @unittest.skipIf(not paddle.is_compiled_with_ipu(),
                  "core is not compiled with IPU")
-class TestBase(IPUOpTest):
+class TestGreaterThan(IPUOpTest):
     def setUp(self):
         self.set_atol()
         self.set_training()
-        self.set_data_feed()
-        self.set_feed_attr()
-        self.set_op_attrs()
+        self.set_test_op()
 
     @property
     def fp16_enabled(self):
         return True
 
-    def set_data_feed(self):
-        x = np.random.randn(3, 4, 5)
-        y = np.random.randn(3, 4, 5)
-        self.feed_fp32 = {
-            "x": x.astype(np.float32),
-            "y": y.astype(np.float32),
-        }
-        self.feed_fp16 = {
-            "x": x.astype(np.float16),
-            "y": y.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_test_op(self):
+        self.op = paddle.fluid.layers.greater_than
 
     def set_op_attrs(self):
         self.attrs = {}
@@ -71,7 +56,7 @@ class TestBase(IPUOpTest):
                     shape=self.feed_shape[1],
                     dtype='float32')
 
-                out = paddle.fluid.layers.greater_than(x, y, **self.attrs)
+                out = self.op(x, y, **self.attrs)
 
                 fetch_list = [out.name]
 
@@ -102,7 +87,7 @@ class TestBase(IPUOpTest):
             result = exe.run(program, feed=feed, fetch_list=fetch_list)
             return result[0]
 
-    def test(self):
+    def run_test_base(self):
         output_dict = {}
         for mode in ExecutionMode:
             if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
@@ -111,29 +96,73 @@ class TestBase(IPUOpTest):
 
         self.check(output_dict)
 
+    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_data_feed0(self):
+        x = np.random.randn(3, 4, 5)
+        y = np.random.randn(3, 4, 5)
+        self.feed_fp32 = {
+            "x": x.astype(np.float32),
+            "y": y.astype(np.float32),
+        }
+        self.feed_fp16 = {
+            "x": x.astype(np.float16),
+            "y": y.astype(np.float16),
+        }
+        self.set_feed_attr()
 
-class TestCase1(TestBase):
-    def set_data_feed(self):
+    def set_data_feed1(self):
         x = np.ones([1, 10])
         y = np.ones([10])
         self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
         self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+        self.set_feed_attr()
 
-
-class TestCase2(TestBase):
-    def set_data_feed(self):
+    def set_data_feed2(self):
         x = np.ones([1, 10])
         y = np.zeros([1, 10])
         self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
         self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+        self.set_feed_attr()
 
-
-class TestCase3(TestBase):
-    def set_data_feed(self):
+    def set_data_feed3(self):
         x = np.zeros([1, 10])
         y = np.ones([1, 10])
         self.feed_fp32 = {"x": x.astype(np.float32), "y": y.astype(np.float32)}
         self.feed_fp16 = {"x": x.astype(np.float16), "y": y.astype(np.float16)}
+        self.set_feed_attr()
+
+    def test_case0(self):
+        self.set_data_feed0()
+        self.set_op_attrs()
+        self.run_test_base()
+
+    def test_case1(self):
+        self.set_data_feed1()
+        self.set_op_attrs()
+        self.run_test_base()
+
+    def test_case2(self):
+        self.set_data_feed2()
+        self.set_op_attrs()
+        self.run_test_base()
+
+    def test_case3(self):
+        self.set_data_feed3()
+        self.set_op_attrs()
+        self.run_test_base()
+
+
+class TestLessThan(TestGreaterThan):
+    def set_test_op(self):
+        self.op = paddle.fluid.layers.less_than
+
+
+class TestEqual(TestGreaterThan):
+    def set_test_op(self):
+        self.op = paddle.fluid.layers.equal
 
 
 if __name__ == "__main__":

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

@@ -12,11 +12,11 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
-from __future__ import print_function
+import unittest
 
 import numpy as np
-import unittest
 import paddle
+import paddle.static
 
 paddle.enable_static()
 
@@ -26,30 +26,31 @@ paddle.enable_static()
 class TestIpuShard(unittest.TestCase):
     def _test(self):
         # build graph
-        a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
-        b = a + 2  # scale : scale * x + bias, ipu_index : no
+        main_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog):
+            a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
+            b = a + 2  # scale : scale * x + bias, ipu_index : no
+
+            with paddle.static.ipu_shard_guard(index=1):
+                c = b + 1  # scale, ipu_index : 1
+                with paddle.static.ipu_shard_guard(index=2):
+                    d = c * 2  # scale, ipu_index : 2
+                with paddle.static.ipu_shard_guard(index=3):
+                    e = d + 3  # scale, ipu_index : 3
+                    with paddle.static.ipu_shard_guard(index=1):
+                        e = e + 3  # scale, ipu_index : 1
+                        with paddle.static.ipu_shard_guard(index=2):
+                            e = e + 3  # scale, ipu_index : 2
+
+            with paddle.static.ipu_shard_guard(index=1):
+                f = paddle.tensor.pow(e, 2.0)  # pow, ipu_index : 1
 
-        with paddle.static.ipu_shard_guard(index=1):
-            c = b + 1  # scale, ipu_index : 1
             with paddle.static.ipu_shard_guard(index=2):
-                d = c * 2  # scale, ipu_index : 2
-            with paddle.static.ipu_shard_guard(index=3):
-                e = d + 3  # scale, ipu_index : 3
-                with paddle.static.ipu_shard_guard(index=1):
-                    e = e + 3  # scale, ipu_index : 1
-                    with paddle.static.ipu_shard_guard(index=2):
-                        e = e + 3  # scale, ipu_index : 2
-
-        with paddle.static.ipu_shard_guard(index=1):
-            f = paddle.tensor.pow(e, 2.0)  # pow, ipu_index : 1
+                g = f - 1  # scale, ipu_index : 2
 
-        with paddle.static.ipu_shard_guard(index=2):
-            g = f - 1  # scale, ipu_index : 2
-
-        h = g + 1  # scale, ipu_index : no
+            h = g + 1  # scale, ipu_index : no
 
         ipu_index_list = []
-        main_prog = paddle.static.default_main_program()
         for op in main_prog.global_block().ops:
             if op.desc.has_attr("ipu_index"):
                 ipu_index_list.append(op.desc.attr("ipu_index"))
@@ -69,30 +70,31 @@ class TestIpuShard(unittest.TestCase):
 class TestIpuPipeline(unittest.TestCase):
     def _test(self):
         # build graph
-        a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
-        b = a + 2  # scale : scale * x + bias, ipu_stage : no
+        main_prog = paddle.static.Program()
+        with paddle.static.program_guard(main_prog):
+            a = paddle.static.data(name='data', shape=[None, 1], dtype='int32')
+            b = a + 2  # scale : scale * x + bias, ipu_stage : no
+
+            with paddle.static.ipu_shard_guard(stage=1):
+                c = b + 1  # scale, ipu_stage : 1
+                with paddle.static.ipu_shard_guard(stage=2):
+                    d = c * 2  # scale, ipu_stage : 2
+                with paddle.static.ipu_shard_guard(stage=3):
+                    e = d + 3  # scale, ipu_stage : 3
+                    with paddle.static.ipu_shard_guard(stage=1):
+                        e = e + 3  # scale, ipu_stage : 1
+                        with paddle.static.ipu_shard_guard(stage=2):
+                            e = e + 3  # scale, ipu_stage : 2
+
+            with paddle.static.ipu_shard_guard(stage=1):
+                f = paddle.tensor.pow(e, 2.0)  # pow, ipu_stage : 1
 
-        with paddle.static.ipu_shard_guard(stage=1):
-            c = b + 1  # scale, ipu_stage : 1
             with paddle.static.ipu_shard_guard(stage=2):
-                d = c * 2  # scale, ipu_stage : 2
-            with paddle.static.ipu_shard_guard(stage=3):
-                e = d + 3  # scale, ipu_stage : 3
-                with paddle.static.ipu_shard_guard(stage=1):
-                    e = e + 3  # scale, ipu_stage : 1
-                    with paddle.static.ipu_shard_guard(stage=2):
-                        e = e + 3  # scale, ipu_stage : 2
-
-        with paddle.static.ipu_shard_guard(stage=1):
-            f = paddle.tensor.pow(e, 2.0)  # pow, ipu_stage : 1
-
-        with paddle.static.ipu_shard_guard(stage=2):
-            g = f - 1  # scale, ipu_stage : 2
+                g = f - 1  # scale, ipu_stage : 2
 
-        h = g + 1  # scale, ipu_stage : no
+            h = g + 1  # scale, ipu_stage : no
 
         ipu_index_list = []
-        main_prog = paddle.static.default_main_program()
         for op in main_prog.global_block().ops:
             if op.desc.has_attr("ipu_stage"):
                 ipu_index_list.append(op.desc.attr("ipu_stage"))

python/paddle/fluid/tests/unittests/ipu/test_logical_x_op_ipu.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.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestLogicalAnd(IPUOpTest):
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_test_op()
+
+    @property
+    def fp16_enabled(self):
+        return False
+
+    def set_test_op(self):
+        self.op = paddle.fluid.layers.logical_and
+
+    def set_op_attrs(self):
+        self.attrs = {}
+
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype=self.feed_dtype[0])
+                y = paddle.static.data(
+                    name=self.feed_list[1],
+                    shape=self.feed_shape[1],
+                    dtype=self.feed_dtype[1])
+
+                out = self.op(x, y, **self.attrs)
+
+            fetch_list = [out.name]
+
+            if exec_mode == ExecutionMode.CPU_FP32:
+                place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if exec_mode != ExecutionMode.CPU_FP32:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            result = exe.run(program, feed=self.feed, fetch_list=fetch_list)
+            return result[0]
+
+    def run_test_base(self):
+        output_dict = {}
+        for mode in ExecutionMode:
+            if mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled:
+                break
+            output_dict[mode] = self._test_base(mode).astype(np.int32)
+
+        self.check(output_dict, check_shape=True)
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed.values()]
+        self.feed_list = list(self.feed.keys())
+        self.feed_dtype = ['bool', 'bool']
+
+    def set_data_feed0(self):
+        x = np.random.choice([True, False], size=(1, 3, 5, 5))
+        y = np.random.choice([True, False], size=(1, 3, 5, 5))
+        self.feed = {
+            "x": x.astype('bool'),
+            "y": y.astype('bool'),
+        }
+        self.set_feed_attr()
+
+    def test_case0(self):
+        self.set_data_feed0()
+        self.set_op_attrs()
+        self.run_test_base()
+
+
+class TestLogicalOr(TestLogicalAnd):
+    def set_test_op(self):
+        self.op = paddle.fluid.layers.logical_or
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_one_hot_op_ipu.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.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        data1 = np.array([[1], [1], [3], [0]])
+
+        self.feed = {'x': data1.astype(np.int32)}
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed.values()]
+        self.feed_list = list(self.feed.keys())
+
+    def set_op_attrs(self):
+        self.attrs = {"depth": 4, "allow_out_of_range": False}
+
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype='int32')
+
+                out = paddle.fluid.layers.one_hot(x, **self.attrs)
+
+                fetch_list = [out.name]
+
+            if exec_mode == ExecutionMode.CPU_FP32:
+                place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if exec_mode != ExecutionMode.CPU_FP32:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            feed = self.feed
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+
+            return result[0]
+
+    def test_base(self):
+        output_dict = {}
+        for mode in ExecutionMode:
+            if (mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled):
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
+
+        self.check(output_dict)
+
+
+@unittest.skip('does not support allow_out_of_range=True')
+class TestCase1(TestBase):
+    def set_op_attrs(self):
+        self.attrs = {"depth": 4, "allow_out_of_range": True}
+
+
+if __name__ == "__main__":
+    unittest.main()

python/paddle/fluid/tests/unittests/ipu/test_one_hot_v2_op_ipu.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.
+
+import unittest
+
+import numpy as np
+import paddle
+import paddle.static
+from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest, ExecutionMode
+
+
+@unittest.skipIf(not paddle.is_compiled_with_ipu(),
+                 "core is not compiled with IPU")
+class TestBase(IPUOpTest):
+    def setUp(self):
+        self.set_atol()
+        self.set_training()
+        self.set_data_feed()
+        self.set_feed_attr()
+        self.set_op_attrs()
+
+    @property
+    def fp16_enabled(self):
+        return True
+
+    def set_data_feed(self):
+        data1 = np.array([[1], [1], [3], [0]])
+
+        self.feed = {'x': data1.astype(np.int32)}
+
+    def set_feed_attr(self):
+        self.feed_shape = [x.shape for x in self.feed.values()]
+        self.feed_list = list(self.feed.keys())
+
+    def set_op_attrs(self):
+        self.attrs = {"depth": 4, "allow_out_of_range": False}
+
+    def _test_base(self, exec_mode):
+        scope = paddle.static.Scope()
+        main_prog = paddle.static.Program()
+        startup_prog = paddle.static.Program()
+        main_prog.random_seed = self.SEED
+        startup_prog.random_seed = self.SEED
+
+        with paddle.static.scope_guard(scope):
+            with paddle.static.program_guard(main_prog, startup_prog):
+                x = paddle.static.data(
+                    name=self.feed_list[0],
+                    shape=self.feed_shape[0],
+                    dtype='int32')
+
+                out = paddle.fluid.input.one_hot(x, **self.attrs)
+
+                fetch_list = [out.name]
+
+            if exec_mode == ExecutionMode.CPU_FP32:
+                place = paddle.CPUPlace()
+            else:
+                place = paddle.IPUPlace()
+
+            exe = paddle.static.Executor(place)
+            exe.run(startup_prog)
+
+            if exec_mode != ExecutionMode.CPU_FP32:
+                feed_list = self.feed_list
+                ipu_strategy = paddle.static.IpuStrategy()
+                ipu_strategy.set_graph_config(is_training=self.is_training)
+                if exec_mode == ExecutionMode.IPU_POPART_FP16:
+                    ipu_strategy.set_precision_config(enable_fp16=True)
+                program = paddle.static.IpuCompiledProgram(
+                    main_prog,
+                    ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)
+            else:
+                program = main_prog
+
+            feed = self.feed
+
+            result = exe.run(program, feed=feed, fetch_list=fetch_list)
+
+            return result[0]
+
+    def test_base(self):
+        output_dict = {}
+        for mode in ExecutionMode:
+            if (mode > ExecutionMode.IPU_FP32 and not self.fp16_enabled):
+                break
+            output_dict[mode] = self._test_base(mode).flatten()
+
+        self.check(output_dict)
+
+
+@unittest.skip('does not support allow_out_of_range=True')
+class TestCase1(TestBase):
+    def set_op_attrs(self):
+        self.attrs = {"depth": 4, "allow_out_of_range": True}
+
+
+if __name__ == "__main__":
+    unittest.main()

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

@@ -91,6 +91,15 @@ class TestBase(IPUOpTest):
                 ipu_strategy = paddle.static.IpuStrategy()
                 ipu_strategy.set_graph_config(is_training=True)
                 ipu_strategy.loss_scaling = self.attrs["loss_scaling"]
+                if "use_no_bias_optimizer" in self.attrs.keys():
+                    ipu_strategy.set_options({
+                        "use_no_bias_optimizer":
+                        self.attrs["use_no_bias_optimizer"]
+                    })
+                if "accl1_type" in self.attrs.keys():
+                    ipu_strategy.set_options({
+                        "accl1_type": self.attrs["accl1_type"]
+                    })
                 program = paddle.static.IpuCompiledProgram(
                     main_prog, ipu_strategy=ipu_strategy).compile(feed_list,
                                                                   fetch_list)
@@ -141,6 +150,28 @@ class TestAdamCase2(TestBase):
         }
 
 
+@unittest.skip('cpu do not support AdamNoBias')
+class TestAdamNoBias(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "optimizer": 'adam',
+            "weight_decay": 0.0,
+            "loss_scaling": 4.0,
+            "use_no_bias_optimizer": True,
+        }
+
+
+@unittest.skip('cpu do not support FLOAT16')
+class TestAdamCase3(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "optimizer": 'adam',
+            "weight_decay": 0.0,
+            "loss_scaling": 4.0,
+            "accl1_type": "FLOAT16",
+        }
+
+
 @unittest.skip('seems cpu output wrong')
 class TestLambCase1(TestBase):
     def set_attrs(self):
@@ -161,5 +192,27 @@ class TestLamb(TestBase):
         }
 
 
+@unittest.skip('cpu do not support LambNoBias')
+class TestLambNoBias(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "optimizer": 'lamb',
+            "weight_decay": 0.1,
+            "loss_scaling": 6.0,
+            "use_no_bias_optimizer": True
+        }
+
+
+@unittest.skip('cpu do not support FLOAT16')
+class TestLambCase2(TestBase):
+    def set_attrs(self):
+        self.attrs = {
+            "optimizer": 'lamb',
+            "weight_decay": 0.1,
+            "loss_scaling": 6.0,
+            "accl1_type": "FLOAT16"
+        }
+
+
 if __name__ == "__main__":
     unittest.main()

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

@@ -88,11 +88,10 @@ class TestBase(IPUOpTest):
             if exec_mode != ExecutionMode.CPU_FP32:
                 feed_list = self.feed_list
                 ipu_strategy = paddle.static.IpuStrategy()
-                ipu_strategy.set_graph_config(is_training=self.is_training)
+                ipu_strategy.set_graph_config(
+                    is_training=self.is_training, micro_batch_size=2)
                 if exec_mode == ExecutionMode.IPU_POPART_FP16:
                     ipu_strategy.set_precision_config(enable_fp16=True)
-                # set batch size
-                ipu_strategy.micro_batch_size = 2
                 program = paddle.static.IpuCompiledProgram(
                     main_prog,
                     ipu_strategy=ipu_strategy).compile(feed_list, fetch_list)