Quantize nearest_interp and nearest_interp_v2 (#38622)

* Quantize nearest_interp and nearest_interp_v2

* Check if avx_core supported

* Add depthwise_conv2d to supported quantization list

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -1641,6 +1641,32 @@ PDNode *patterns::Slice::operator()() {
   return slice_out;
 }
 
+PDNode *patterns::NearestInterp::operator()() {
+  auto prev_op = pattern->NewNode(prev_op_repr())->assert_is_op();
+
+  auto nearest_interp_op =
+      pattern->NewNode(nearest_interp_op_repr())
+          ->assert_is_ops({"nearest_interp", "nearest_interp_v2"});
+
+  auto nearest_interp_in =
+      pattern->NewNode(nearest_interp_in_repr())
+          ->AsInput()
+          ->assert_is_ops_input({"nearest_interp", "nearest_interp_v2"}, "X");
+  auto nearest_interp_out =
+      pattern->NewNode(nearest_interp_out_repr())
+          ->AsOutput()
+          ->assert_is_ops_output({"nearest_interp", "nearest_interp_v2"},
+                                 "Out");
+
+  auto next_op = pattern->NewNode(next_op_repr())->assert_is_op();
+
+  prev_op->LinksTo({nearest_interp_in});
+  nearest_interp_op->LinksFrom({nearest_interp_in})
+      .LinksTo({nearest_interp_out});
+  next_op->LinksFrom({nearest_interp_out});
+  return nearest_interp_out;
+}
+
 PDNode *patterns::Matmul::operator()() {
   auto matmul_op = pattern->NewNode(matmul_op_repr())->assert_is_op("matmul");
 
@@ -2376,15 +2402,8 @@ PDNode *patterns::MultipleQuantize::operator()() {
 
 PDNode *patterns::QuantizePlacement::operator()(
     const std::unordered_set<std::string> &quantize_enabled_op_types) {
-  std::unordered_set<std::string> supported_op_types =
-      std::unordered_set<std::string>({"concat", "conv2d", "elementwise_add",
-                                       "fc", "matmul", "pool2d", "prior_box",
-                                       "reshape2", "transpose2", "fusion_gru",
-                                       "fusion_lstm", "multi_gru", "slice"});
-  if (!quantize_enabled_op_types.empty()) {
-    supported_op_types = quantize_enabled_op_types;
-  }
-  auto *op = pattern->NewNode(op_repr())->assert_is_ops(supported_op_types);
+  auto *op =
+      pattern->NewNode(op_repr())->assert_is_ops(quantize_enabled_op_types);
   return op;
 }
 

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -995,6 +995,21 @@ struct Slice : public PatternBase {
   PATTERN_DECL_NODE(next_op);
 };
 
+// Nearest Interp op
+// Forward pass for nearest_interp.
+// nearest_interp_out is a result of the operator.
+struct NearestInterp : public PatternBase {
+  NearestInterp(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "nearest_interp") {}
+
+  PDNode* operator()();
+  PATTERN_DECL_NODE(prev_op);
+  PATTERN_DECL_NODE(nearest_interp_in);
+  PATTERN_DECL_NODE(nearest_interp_op);
+  PATTERN_DECL_NODE(nearest_interp_out);
+  PATTERN_DECL_NODE(next_op);
+};
+
 // Matmul op
 // Forward pass for matmul.
 struct Matmul : public PatternBase {

paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.cc

@@ -1053,6 +1053,67 @@ void CPUQuantizePass::QuantizeFusionLSTM(Graph* graph) const {
   PrettyLogDetail("---    quantized %d fusion_lstm ops", quantize_count);
 }
 
+void CPUQuantizePass::QuantizeNearestInterp(Graph* graph) const {
+  GraphPatternDetector gpd;
+  auto pattern = gpd.mutable_pattern();
+  patterns::NearestInterp nearest_interp_pattern{pattern, name_scope_};
+  nearest_interp_pattern();
+
+  int quantize_nearest_interp_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
+                     Graph* g) {
+    VLOG(4) << "Quantize nearest_interp op";
+    GET_IR_NODE_FROM_SUBGRAPH(nearest_interp_op, nearest_interp_op,
+                              nearest_interp_pattern);
+
+    // skip if should not be quantized
+    if (!platform::HasOpINT8DataType(nearest_interp_op->Op())) {
+      LogQuantizationDisabled(nearest_interp_op);
+      return;
+    }
+    GET_IR_NODE_FROM_SUBGRAPH(prev_op, prev_op, nearest_interp_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(next_op, next_op, nearest_interp_pattern);
+
+    // skip if prev op and next op is not quantized
+    if (!(IsOpDequantized(prev_op)) && !(IsOpQuantized(next_op))) {
+      LogCannotQuantizeOp(nearest_interp_op,
+                          "There are no other quantized operators nearby, so "
+                          "quantization is not recommended.");
+      return;
+    }
+
+    GET_IR_NODE_FROM_SUBGRAPH(nearest_interp_in, nearest_interp_in,
+                              nearest_interp_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(nearest_interp_out, nearest_interp_out,
+                              nearest_interp_pattern);
+
+    if (!AreScalesPresentForNodes({nearest_interp_in, nearest_interp_out})) {
+      LogCannotQuantizeOp(nearest_interp_op);
+      return;
+    }
+
+    bool is_input_unsigned{false};
+    auto input_scale =
+        GetScaleValueForNode(nearest_interp_in, &is_input_unsigned);
+    QuantizeInput(g, nearest_interp_op, nearest_interp_in, "X", input_scale,
+                  is_input_unsigned);
+
+    bool is_output_unsigned{false};
+    auto output_scale =
+        GetScaleValueForNode(nearest_interp_out, &is_output_unsigned);
+    DequantizeOutput(g, nearest_interp_op, nearest_interp_out, "Out",
+                     output_scale, is_output_unsigned);
+
+    ++quantize_nearest_interp_count;
+  };
+
+  gpd(graph, handler);
+  AddStatis(quantize_nearest_interp_count);
+
+  PrettyLogDetail("---    quantized %d nearest_interp ops",
+                  quantize_nearest_interp_count);
+}
+
 void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
   VLOG(3) << "Quantizing the graph.";
   PADDLE_ENFORCE_NOT_NULL(
@@ -1076,6 +1137,7 @@ void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
   QuantizeMultiGru(graph);
   QuantizeFusionLSTM(graph);
   QuantizeSlice(graph);
+  QuantizeNearestInterp(graph);
 }
 
 }  // namespace ir

paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass.h

@@ -62,6 +62,7 @@ class CPUQuantizePass : public FusePassBase {
   void QuantizeMultiGru(Graph* graph) const;
   void QuantizeFusionLSTM(Graph* graph) const;
   void QuantizeSlice(Graph* graph) const;
+  void QuantizeNearestInterp(Graph* graph) const;
 
   void QuantizeInput(Graph* g, Node* op, Node* input, std::string input_name,
                      double scale_to_one, bool is_input_unsigned,

paddle/fluid/framework/ir/mkldnn/cpu_quantize_pass_tester.cc

@@ -58,7 +58,8 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name,
     op->SetAttr("Scale_in", 1.0f);
     op->SetAttr("Scale_out", 1.0f);
     op->SetAttr("Scale_weights", std::vector<float>{1.0f});
-  } else if (type == "pool2d" || type == "transpose2" || type == "reshape2") {
+  } else if (type == "pool2d" || type == "transpose2" || type == "reshape2" ||
+             type == "nearest_interp" || type == "nearest_interp_v2") {
     op->SetInput("X", {inputs[0]});
     op->SetOutput("Out", {outputs[0]});
   } else if (type == "slice") {
@@ -434,6 +435,18 @@ TEST(CpuQuantizePass, sliceBetweenNonQuantizedOp) {
   TestImmutableOpBetweenNonQuantizedOp("slice");
 }
 
+TEST(CpuQuantizePass, nearestInterp) { TestImmutableOp("nearest_interp"); }
+
+TEST(CpuQuantizePass, nearestInterpBetweenNonQuantizedOp) {
+  TestImmutableOpBetweenNonQuantizedOp("nearest_interp");
+}
+
+TEST(CpuQuantizePass, nearestInterpV2) { TestImmutableOp("nearest_interp_v2"); }
+
+TEST(CpuQuantizePass, nearestInterpV2BetweenNonQuantizedOp) {
+  TestImmutableOpBetweenNonQuantizedOp("nearest_interp_v2");
+}
+
 static const std::initializer_list<std::string> variable_names_matmul = {
     "a", "b", "c", "d", "e", "f"};
 

paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass.cc

@@ -13,6 +13,7 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass.h"
+
 #include <unordered_set>
 
 namespace paddle {
@@ -23,15 +24,34 @@ class Graph;
 
 void CPUQuantizePlacementPass::ApplyImpl(ir::Graph* graph) const {
   VLOG(3) << "Marks operators which are to be quantized.";
+  std::unordered_set<std::string> supported_op_types =
+      std::unordered_set<std::string>(
+          {"concat", "conv2d", "depthwise_conv2d", "elementwise_add", "fc",
+           "matmul", "nearest_interp", "nearest_interp_v2", "pool2d",
+           "prior_box", "reshape2", "transpose2", "fusion_gru", "fusion_lstm",
+           "multi_gru", "slice"});
   const auto& excluded_ids_list =
       Get<std::unordered_set<int>>("quantize_excluded_op_ids");
   const auto& op_types_list =
       Get<std::unordered_set<std::string>>("quantize_enabled_op_types");
+
+  if (!op_types_list.empty()) {
+    // Verify that all user-specified operators can be quantized.
+    for (const auto& op : op_types_list) {
+      PADDLE_ENFORCE_NE(
+          supported_op_types.count(op), 0,
+          platform::errors::InvalidArgument(
+              "Pass attribute quantize_enabled_op_types contains operator %s "
+              "that is not supported by OneDNN quantization.",
+              op));
+    }
+    supported_op_types = op_types_list;
+  }
   Init(name_scope_, graph);
   GraphPatternDetector gpd;
   patterns::QuantizePlacement quantize_placement_pattern{gpd.mutable_pattern(),
                                                          "quantize_placement"};
-  quantize_placement_pattern(op_types_list);
+  quantize_placement_pattern(supported_op_types);
 
   auto handler = [&](const GraphPatternDetector::subgraph_t& subgraph,
                      Graph* g) {
@@ -46,16 +66,7 @@ void CPUQuantizePlacementPass::ApplyImpl(ir::Graph* graph) const {
       return;
     }
 
-    if (op->Op()->HasAttr("mkldnn_data_type") ||
-        op->Op()->HasProtoAttr("mkldnn_data_type")) {
-      // use_quantizer is no longer used
-      // assign value for compatibility
-      if (op->Op()->GetAttrIfExists<bool>("use_quantizer")) {
-        op->Op()->SetAttr("mkldnn_data_type", std::string("int8"));
-      }
-      op->Op()->SetAttr("mkldnn_data_type", std::string("int8"));
-      op->Op()->SetAttr("use_quantizer", true);
-    }
+    op->Op()->SetAttr("mkldnn_data_type", std::string("int8"));
   };
   gpd(graph, handler);
 }

paddle/fluid/framework/ir/mkldnn/cpu_quantize_placement_pass_tester.cc

@@ -140,6 +140,32 @@ TEST(QuantizerPlacementPass, default_attr_value) {
   DefaultAttrTest(5);
 }
 
+void EnabledOpTypesTest(
+    std::initializer_list<std::string> quantize_enabled_op_types,
+    std::string missing_op) {
+  auto prog = BuildProgramDesc();
+  std::unique_ptr<ir::Graph> graph(new ir::Graph(prog));
+
+  auto pass = PassRegistry::Instance().Get("cpu_quantize_placement_pass");
+  pass->Set("quantize_enabled_op_types",
+            new std::unordered_set<std::string>(quantize_enabled_op_types));
+
+  try {
+    graph.reset(pass->Apply(graph.release()));
+  } catch (paddle::platform::EnforceNotMet& err) {
+    std::string ex_msg = err.what();
+    std::string expected_msg =
+        "Pass attribute quantize_enabled_op_types contains operator " +
+        missing_op + " that is not supported by OneDNN quantization.";
+    EXPECT_TRUE(ex_msg.find(expected_msg) != std::string::npos);
+  }
+}
+
+TEST(QuantizerPlacementPass, unsupported_op_type) {
+  // Dropout op is not supported by OneDNN quantization
+  EnabledOpTypesTest({"conv2d", "dropout"}, "dropout");
+}
+
 }  // namespace ir
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/inference/api/mkldnn_quantizer.cc

@@ -124,7 +124,8 @@ void AnalysisPredictor::MkldnnQuantizer::CalculateScalesForOpOutputs(
       } else if (op->Type() == "relu") {
         is_unsigned = true;
       } else if (op->Type() == "transpose2" || op->Type() == "reshape2" ||
-                 op->Type() == "pool2d") {
+                 op->Type() == "pool2d" || op->Type() == "nearest_interp" ||
+                 op->Type() == "nearest_interp_v2") {
         auto input_var_name = op->Input("X")[0];
         PADDLE_ENFORCE_NE(scales_.find(input_var_name), scales_.end(),
                           platform::errors::PreconditionNotMet(

paddle/fluid/inference/api/mkldnn_quantizer_config.cc

@@ -107,6 +107,18 @@ MkldnnQuantizerConfig::MkldnnQuantizerConfig() {
   rules_["fusion_lstm"]["ReorderedC0"] = ScaleAlgo::NONE;
   rules_["fusion_lstm"]["CheckedCell"] = ScaleAlgo::NONE;
   rules_["fusion_lstm"]["Hidden"] = ScaleAlgo::KL;
+
+  rules_["nearest_interp"]["X"] = ScaleAlgo::KL;
+  rules_["nearest_interp"]["OutSize"] = ScaleAlgo::NONE;
+  rules_["nearest_interp"]["SizeTensor"] = ScaleAlgo::NONE;
+  rules_["nearest_interp"]["Scale"] = ScaleAlgo::NONE;
+  rules_["nearest_interp"]["Out"] = ScaleAlgo::NONE;
+
+  rules_["nearest_interp_v2"]["X"] = ScaleAlgo::KL;
+  rules_["nearest_interp_v2"]["OutSize"] = ScaleAlgo::NONE;
+  rules_["nearest_interp_v2"]["SizeTensor"] = ScaleAlgo::NONE;
+  rules_["nearest_interp_v2"]["Scale"] = ScaleAlgo::NONE;
+  rules_["nearest_interp_v2"]["Out"] = ScaleAlgo::NONE;
 }
 
 ScaleAlgo MkldnnQuantizerConfig::scale_algo(

python/paddle/fluid/contrib/slim/quantization/quant2_int8_mkldnn_pass.py

@@ -63,7 +63,8 @@ class Quant2Int8MkldnnPass(object):
         self._op_ids_to_skip = _op_ids_to_skip if _op_ids_to_skip is not None else set(
             [-1])
         self._scale_immutable_ops = [
-            'transpose2', 'reshape2', 'pool2d', 'slice'
+            'transpose2', 'reshape2', 'pool2d', 'slice', 'nearest_interp',
+            'nearest_interp_v2'
         ]
         self._scale_ops = ['scale']
         self._conv_ops = ['conv2d', 'depthwise_conv2d']

python/paddle/fluid/contrib/slim/tests/test_quant2_int8_mkldnn_pass.py

@@ -216,6 +216,141 @@ class TestQuant2Int8MkldnnPassConv2D(unittest.TestCase):
             graph = quant2_int8_mkldnn_pass._update_activations(graph)
             self.check_graph_after_pass(graph)
 
+    class TestQuant2Int8MkldnnPassNearestInterp(unittest.TestCase):
+        def op_name(self):
+            return "nearest_interp"
+
+        def setUp(self):
+            self.scope = fluid.Scope()
+            self.place = fluid.CPUPlace()
+            self.dtype = np.float32
+            self.use_cudnn = False
+            self.use_mkldnn = True
+
+            # conv2d
+            self.data_format = "ANYLAYOUT"
+            self.pad = [0, 0]
+            self.stride = [1, 1]
+            self.dilations = [1, 1]
+            self.groups = 1
+            self.input_size = [1, 3, 5, 5]
+            self.filter_size = [16, 3, 3, 3]
+            self.conv_output_size = [1, 16, 3, 3]
+            self.input = np.random.random(self.input_size).astype(self.dtype)
+            self.filter = np.random.random(self.filter_size).astype(self.dtype)
+            self.conv_output = np.ndarray(self.conv_output_size).astype(
+                self.dtype)
+
+            # nearest_interp
+            self.out_h = 1
+            self.out_w = 1
+            self.scale = 2.0
+            self.interp_method = 'nearest'
+            self.data_layout = 'NCHW'
+            self.nearest_interp_output_size = [1, 1, 2, 2]
+            self.nearest_interp_output = np.ndarray(
+                self.nearest_interp_output_size).astype(self.dtype)
+
+            # dropout
+            self.dropout_prob = 0.5
+            self.dropout_out = np.ndarray(
+                self.nearest_interp_output_size).astype(self.dtype)
+            self.dropout_mask = np.ndarray(self.nearest_interp_output_size)
+
+            self.quantized_ops = {
+                "conv2d", "nearest_interp", "nearest_interp_v2"
+            }
+            self.variables = {
+                "input": self.input,
+                "filter": self.filter,
+                "conv_output": self.conv_output,
+                "nearest_interp_output": self.nearest_interp_output,
+                "dropout_out": self.dropout_out,
+                'dropout_mask': self.dropout_mask
+            }
+
+        def prepare_program(self, program):
+            block = program.global_block()
+            for name in self.variables:
+                block.create_var(
+                    name=name,
+                    dtype="float32",
+                    shape=self.variables[name].shape)
+            block.append_op(
+                type="conv2d",
+                inputs={
+                    "Input": block.var('input'),
+                    'Filter': block.var('filter')
+                },
+                outputs={"Output": block.var('conv_output')},
+                attrs={
+                    'strides': self.stride,
+                    'paddings': self.pad,
+                    'groups': self.groups,
+                    'dilations': self.dilations,
+                    'use_cudnn': self.use_cudnn,
+                    'use_mkldnn': self.use_mkldnn,
+                    'data_format': self.data_format,
+                    'fuse_relu': True
+                })
+            block.append_op(
+                type=self.op_name(),
+                inputs={"X": block.var('conv_output'), },
+                outputs={"Out": block.var('nearest_interp_output')},
+                attrs={
+                    'interp_method': self.interp_method,
+                    'out_h': self.out_h,
+                    'out_w': self.out_w,
+                    'scale': self.scale,
+                    'data_layout': self.data_layout,
+                    'use_mkldnn': self.use_mkldnn
+                })
+            block.append_op(
+                type='dropout',
+                inputs={"X": block.var('nearest_interp_output'), },
+                outputs={
+                    'Out': block.var('dropout_out'),
+                    'Mask': block.var('dropout_mask')
+                },
+                attrs={'dropout_prob': self.dropout_prob, })
+
+        def check_graph_after_pass(self, graph):
+            for op in graph.all_op_nodes():
+                if op.op().type() in self.quantized_ops:
+                    self.assertTrue(op.op().has_attr("mkldnn_data_type"))
+                    self.assertTrue(op.op().attr("mkldnn_data_type") == "int8")
+
+        def test_quant_update_activation(self):
+            program = fluid.Program()
+            with fluid.program_guard(program):
+                self.prepare_program(program)
+                graph = IrGraph(core.Graph(program.desc), for_test=True)
+                quant2_int8_mkldnn_pass = Quant2Int8MkldnnPass(
+                    self.quantized_ops,
+                    _scope=self.scope,
+                    _place=self.place,
+                    _core=core,
+                    _debug=False)
+
+                input_scale_tensor = quant2_int8_mkldnn_pass._convert_scale2tensor(
+                    np.array(self.scale).astype(np.float64))
+                output_scale_tensor = quant2_int8_mkldnn_pass._convert_scale2tensor(
+                    np.array(1. / self.scale * self.scale).astype(np.float64))
+                var_scale = {
+                    "input": (False, input_scale_tensor),
+                    "filter": (False, input_scale_tensor),
+                    "conv_output": (False, output_scale_tensor),
+                }
+                if core.avx_supported():
+                    quant2_int8_mkldnn_pass._var_quant_scales = var_scale
+                    graph = quant2_int8_mkldnn_pass._propagate_scales(graph)
+                    graph = quant2_int8_mkldnn_pass._quantize_fp32_graph(graph)
+                    self.check_graph_after_pass(graph)
+
+    class TestQuant2Int8MkldnnPassNearestInterpV2(unittest.TestCase):
+        def op_name(self):
+            return "nearest_interp_v2"
+
 
 if __name__ == '__main__':
     unittest.main()