Quantize shape operator (#44124)

* Quantize shape operator

* Add shape op to propagate scales pass

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -1056,7 +1056,7 @@ struct ResidualElementwise : public PatternBase {
 };
 
 // General struct for immutable ops:
-// reshape, transpose, slice, nearest-interp
+// reshape, transpose, slice, shape, nearest-interp
 // Forward pass for no weights-op.
 // immutable_out is a result of the operator.
 struct Immutable : public PatternBase {

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

@@ -372,7 +372,7 @@ std::unordered_set<std::string> ComputePropagateScalesMkldnnPass::UpdateScales(
     const auto op_name = op_node->Name();
     if (scale_immutable_ops.count(op_name)) {
       std::string input_name;
-      if (op_name == "slice") {
+      if (op_name == "slice" || op_name == "shape") {
         input_name = op_node->Op()->Input("Input")[0];
       } else {
         input_name = op_node->Op()->Input("X")[0];
@@ -445,6 +445,7 @@ void ComputePropagateScalesMkldnnPass::ApplyImpl(ir::Graph* graph) const {
       "reshape2",
       "pool2d",
       "slice",
+      "shape",
       "nearest_interp",
       "nearest_interp_v2"};
 

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

@@ -1136,6 +1136,7 @@ void CPUQuantizePass::ApplyImpl(ir::Graph* graph) const {
   QuantizeImmutable(graph, "reshape2", "X");
   QuantizeImmutable(graph, "transpose2", "X");
   QuantizeImmutable(graph, "slice", "Input");
+  QuantizeImmutable(graph, "shape", "Input");
   QuantizeImmutable(graph, "nearest_interp", "X");
   QuantizeImmutable(graph, "nearest_interp_v2", "X");
   QuantizeElementwise(graph, "elementwise_add");

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

@@ -66,7 +66,7 @@ void SetOp(ProgramDesc* prog,
              type == "nearest_interp" || type == "nearest_interp_v2") {
     op->SetInput("X", {inputs[0]});
     op->SetOutput("Out", {outputs[0]});
-  } else if (type == "slice") {
+  } else if (type == "slice" || type == "shape") {
     op->SetInput("Input", {inputs[0]});
     op->SetOutput("Out", {outputs[0]});
   } else if (type == "dropout") {
@@ -550,8 +550,12 @@ void TestImmutableOpWithManyOutputs(const std::string tested_op) {
            SCALE * S8_MAX);
 }
 
-const std::vector<std::string> immutables = {
-    "reshape2", "transpose2", "slice", "nearest_interp", "nearest_interp_v2"};
+const std::vector<std::string> immutables = {"reshape2",
+                                             "transpose2",
+                                             "slice",
+                                             "shape",
+                                             "nearest_interp",
+                                             "nearest_interp_v2"};
 
 class TestImmutables : public testing::TestWithParam<std::string> {};
 

paddle/fluid/inference/api/mkldnn_quantizer.cc

@@ -142,7 +142,7 @@ void AnalysisPredictor::MkldnnQuantizer::CalculateScalesForOpOutputs(
           scales_[var_name] = scales_[input_var_name];
         }
         compute_scale = false;
-      } else if (op->Type() == "slice") {
+      } else if (op->Type() == "slice" || op->Type() == "shape") {
         auto input_var_name = op->Input("Input")[0];
         PADDLE_ENFORCE_NE(scales_.find(input_var_name),
                           scales_.end(),

paddle/fluid/inference/api/mkldnn_quantizer_config.cc

@@ -45,6 +45,9 @@ MkldnnQuantizerConfig::MkldnnQuantizerConfig() {
   rules_["slice"]["Input"] = ScaleAlgo::KL;
   rules_["slice"]["Out"] = ScaleAlgo::NONE;
 
+  rules_["shape"]["Input"] = ScaleAlgo::KL;
+  rules_["shape"]["Out"] = ScaleAlgo::NONE;
+
   rules_["fc"]["Input"] = ScaleAlgo::KL;
   rules_["fc"]["W"] = ScaleAlgo::MAX_CH_T;
   rules_["fc"]["Bias"] = ScaleAlgo::NONE;
@@ -62,6 +65,10 @@ MkldnnQuantizerConfig::MkldnnQuantizerConfig() {
   rules_["elementwise_mul"]["Y"] = ScaleAlgo::KL;
   rules_["elementwise_mul"]["Out"] = ScaleAlgo::KL;
 
+  rules_["elementwise_sub"]["X"] = ScaleAlgo::KL;
+  rules_["elementwise_sub"]["Y"] = ScaleAlgo::KL;
+  rules_["elementwise_sub"]["Out"] = ScaleAlgo::KL;
+
   // Reshape2 does not perform calculation on the data and shapes are not
   // changed. Scale is calculated on input data and assign to Quantize and
   // Dequantize scale.

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

@@ -63,8 +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', 'nearest_interp',
-            'nearest_interp_v2'
+            'transpose2', 'reshape2', 'pool2d', 'slice', 'shape',
+            'nearest_interp', 'nearest_interp_v2'
         ]
         self._scale_ops = ['scale']
         self._conv_ops = ['conv2d', 'depthwise_conv2d']
@@ -247,7 +247,7 @@ class Quant2Int8MkldnnPass(object):
             waiting_for_scale = set()
             for op in graph.all_op_nodes():
                 if op.name() in self._scale_immutable_ops:
-                    if op.name() == 'slice':
+                    if op.name() == 'slice' or op.name() == 'shape':
                         input_name = op.input("Input")[0]
                     else:
                         input_name = op.input("X")[0]