fix dimensions error for mobilenetv1_KL_quant (#26776)

* fix dimensions error for mobilenetv1_KL_quant

fixes AssertionError: The size of weight scales vector (1000) does not match the number of output channels (1024) in the weights tensor fc7_weights.

add mul test

* remove comment

* add third case unit test

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

@@ -299,11 +299,14 @@ def _dequantize_op_weights(self, graph, op_node, weight_name, output_name):
         # Convert int8 range weights to fp32 range weights
         scales = self._weight_scales[output_var_name]
         weight = self._load_param(self._scope, weight_var_name)
-        assert scales.size == 1 or scales.size == len(
-            weight
-        ), "The size of weight scales vector ({}) does not match the number of output channels ({}) in the weights tensor {}.".format(
-            scales.size, len(weight), weight_var_name)
-        w_fp32 = np.divide(np.multiply(weight, self._s8_max).T, scales.T).T
+        if scales.size == 1 or scales.size == weight.shape[0]:
+            w_fp32 = np.divide(np.multiply(weight, self._s8_max).T, scales.T).T
+        elif len(weight.shape) > 1 and scales.size == weight.shape[1]:
+            w_fp32 = np.divide(np.multiply(weight, self._s8_max), scales)
+        else:
+            raise ValueError(
+                "The size of weight scales vector ({}) does not match the dimensions ({}) of the weights tensor {}."
+                .format(scales.size, weight.shape, weight_var_name))
         w_fp32 = w_fp32.reshape(weight.shape).astype(np.float32)
         self._restore_var(weight_var_name, w_fp32)
 

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

@@ -43,14 +43,30 @@ def setUp(self):
         self.conv_output = np.ndarray(self.conv_output_size).astype(self.dtype)
         self.conv_output2 = np.ndarray(self.conv_output2_size).astype(
             self.dtype)
-        self.quantized_ops = 'conv2d'
+        self.quantized_ops = 'conv2d,mul'
         self.variables = {
             "input": self.input,
             "filter": self.filter,
             "filter2": self.filter2,
             "conv_output": self.conv_output,
             "conv_output2": self.conv_output2,
         }
+        self.mul_input_size = [1, 3]
+        self.mul_weights_size = [3, 5]
+        self.mul_output_size = [1, 5]
+        self.mul_input = np.random.random(self.mul_input_size).astype(
+            self.dtype)
+        self.mul_weights = np.ones(self.mul_weights_size, self.dtype)
+        self.mul_weights_bad = np.ones([1, 1], self.dtype)
+        self.mul_output = np.ndarray(self.mul_output_size).astype(self.dtype)
+        self.mul_output_scale = np.linspace(1, 5, num=5).astype(self.dtype)
+
+        self.variables_mul = {
+            "mul_input": self.mul_input,
+            "mul_weights": self.mul_weights,
+            "mul_output": self.mul_output,
+            "mul_weights_bad": self.mul_weights_bad
+        }
 
     def prepare_program(self, program):
         block = program.global_block()
@@ -92,6 +108,23 @@ def prepare_program(self, program):
                 'fuse_brelu': True
             })
 
+    def prepare_program_mul(self, program):
+        block = program.global_block()
+        for name in self.variables_mul:
+            block.create_var(
+                name=name,
+                dtype="float32",
+                shape=self.variables_mul[name].shape)
+
+        mul_op1 = block.append_op(
+            type="mul",
+            inputs={
+                "X": block.var('mul_input'),
+                "Y": block.var('mul_weights')
+            },
+            outputs={"Out": block.var('mul_output')},
+            attrs={'use_mkldnn': self.use_mkldnn})
+
     def remove_fuse_activation_attribute(self, graph):
         for op in graph.all_op_nodes():
             op.op().remove_attr("fuse_activation")
@@ -103,11 +136,13 @@ def check_graph_before_pass(self, graph):
 
     def check_graph_after_pass(self, graph):
         for op in graph.all_op_nodes():
-            self.assertTrue(op.op().has_attr("fuse_activation"))
-            if op.op().has_attr("fuse_relu") and op.op().attr("fuse_relu"):
-                self.assertTrue(op.op().attr("fuse_activation") == "relu")
-            if op.op().has_attr("fuse_brelu") and op.op().attr("fuse_brelu"):
-                self.assertTrue(op.op().attr("fuse_activation") == "relu6")
+            if op.op().type() == "conv2d":
+                self.assertTrue(op.op().has_attr("fuse_activation"))
+                if op.op().has_attr("fuse_relu") and op.op().attr("fuse_relu"):
+                    self.assertTrue(op.op().attr("fuse_activation") == "relu")
+                if op.op().has_attr("fuse_brelu") and op.op().attr(
+                        "fuse_brelu"):
+                    self.assertTrue(op.op().attr("fuse_activation") == "relu6")
 
     def test_quant_update_activation(self):
         program = fluid.Program()
@@ -125,6 +160,39 @@ def test_quant_update_activation(self):
             graph = quant2_int8_mkldnn_pass._update_activations(graph)
             self.check_graph_after_pass(graph)
 
+    def test_dequantize_op_weights(self):
+        program = fluid.Program()
+        with fluid.program_guard(program):
+            self.prepare_program_mul(program)
+            graph = IrGraph(core.Graph(program.desc), for_test=True)
+
+            for op in graph.all_op_nodes():
+                if op.op().type() == "mul":
+                    op_node = op
+                    break
+
+            qpass = Quant2Int8MkldnnPass(
+                self.quantized_ops,
+                _scope=self.scope,
+                _place=self.place,
+                _core=core,
+                _debug=False)
+            qpass._weight_scales["mul_output"] = self.mul_output_scale
+            param = self.scope.var("mul_weights").get_tensor()
+            param.set(self.variables_mul["mul_weights"], self.place)
+            qpass._dequantize_op_weights(graph, op_node, "Y", "Out")
+
+            assert np.allclose(
+                self.scope.find_var("mul_weights").get_tensor(),
+                [[127, 63.5, 42.3333, 31.75, 25.4],
+                 [127, 63.5, 42.3333, 31.75, 25.4],
+                 [127, 63.5, 42.3333, 31.75, 25.4]])
+
+            param = self.scope.var("mul_weights").get_tensor()
+            param.set(self.variables_mul["mul_weights_bad"], self.place)
+            with self.assertRaises(ValueError):
+                qpass._dequantize_op_weights(graph, op_node, "Y", "Out")
+
 
 if __name__ == '__main__':
     unittest.main()