[frontend][tflite] float16 quant support (apache#7736)

* [frontend][tflite] float16 quant support * remove skip conditions in tests
trevor-m · May 6, 2021 · 6161899 · 6161899
1 parent c8584ef
commit 6161899
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Showing 2 changed files with 349 additions and 149 deletions.
diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py
@@ -1882,9 +1882,12 @@ def convert_fully_connected(self, op):
                 # bias tensor type should be INT32 (quantization) or FLOAT32
                 assert bias_tensor_type in (TensorType.INT32, TensorType.FLOAT32)
                 bias_tensor_type_str = self.get_tensor_type_str(bias_tensor_type)
-                bias_expr = self.exp_tab.new_const(
-                    self.get_tensor_value(bias_tensor), dtype=bias_tensor_type_str
-                )
+                if self.has_expr(bias_tensor.tensor_idx):
+                    bias_expr = self.get_expr(bias_tensor.tensor_idx)
+                else:
+                    bias_expr = self.exp_tab.new_const(
+                        self.get_tensor_value(bias_tensor), dtype=bias_tensor_type_str
+                    )
                 out = _op.nn.bias_add(out, bias_expr)
 
         # Finally if the dense is quantized. Add a requantize at the end.
@@ -2852,11 +2855,18 @@ def convert_transpose_conv(self, op):
         # weights tensor type should be UINT8 (quantization) or FLOAT32
         assert weights_tensor_type in (TensorType.INT8, TensorType.UINT8, TensorType.FLOAT32)
         weight_tensor_type_str = self.get_tensor_type_str(weights_tensor_type)
-        weight_value_ohwi = self.get_tensor_value(weights_tensor)
-        # Relay kernel_layout should be OIHW
-        # Relay weights layout should be different from kernel_layout - it should be IOHW
-        weight_value_iohw = np.transpose(weight_value_ohwi, (3, 0, 1, 2))
-        weight_expr_iohw = self.exp_tab.new_const(weight_value_iohw, dtype=weight_tensor_type_str)
+
+        if self.has_expr(weights_tensor.tensor_idx):
+            weight_expr_iohw = self.get_expr(weights_tensor.tensor_idx)
+            weight_expr_iohw = _op.transpose(weight_expr_iohw, axes=(3, 0, 1, 2))
+        else:
+            weight_value_ohwi = self.get_tensor_value(weights_tensor)
+            # Relay kernel_layout should be OIHW
+            # Relay weights layout should be different from kernel_layout - it should be IOHW
+            weight_value_iohw = np.transpose(weight_value_ohwi, (3, 0, 1, 2))
+            weight_expr_iohw = self.exp_tab.new_const(
+                weight_value_iohw, dtype=weight_tensor_type_str
+            )
 
         # Output shape value
         output_shape_value = self.get_tensor_value(output_shape_tensor)
@@ -2912,9 +2922,12 @@ def convert_transpose_conv(self, op):
             # bias tensor type should be INT32 (quantization) or FLOAT32
             assert bias_tensor_type in (TensorType.INT32, TensorType.FLOAT32)
             bias_tensor_type_str = self.get_tensor_type_str(bias_tensor_type)
-            bias_expr = self.exp_tab.new_const(
-                self.get_tensor_value(bias_tensor), dtype=bias_tensor_type_str
-            )
+            if self.has_expr(bias_tensor.tensor_idx):
+                bias_expr = self.get_expr(bias_tensor.tensor_idx)
+            else:
+                bias_expr = self.exp_tab.new_const(
+                    self.get_tensor_value(bias_tensor), dtype=bias_tensor_type_str
+                )
             channel_axis = 3
             out = _op.nn.bias_add(out, bias_expr, axis=channel_axis)