Fix has not attribute

Fix:

fms_mo/quant/quantizers.py:3898: error: Item "None" of "Parameter | None" has no attribute "fill_"  [union-attr]
fms_mo/quant/quantizers.py:3963: error: Item "float" of "Any | float" has no attribute "round"  [union-attr]
fms_mo/quant/quantizers.py:5392: error: Module has no attribute "float8_e5m2G"  [attr-defined]
fms_mo/custom_ext_kernels/utils.py:77: error: Module has no attribute "custom_op"  [attr-defined]
fms_mo/custom_ext_kernels/utils.py:79: error: Module has no attribute "register_kernel"  [attr-defined]
fms_mo/custom_ext_kernels/utils.py:80: error: Module has no attribute "register_fake"  [attr-defined]
fms_mo/utils/torchscript_utils.py:182: error: Item "None" of "Node | None" has no attribute "inputs"  [union-attr]
fms_mo/utils/torchscript_utils.py:184: error: Item "None" of "Node | None" has no attribute "outputs"  [union-attr]
fms_mo/utils/torchscript_utils.py:204: error: "Node" has no attribute "name"  [attr-defined]
fms_mo/utils/torchscript_utils.py:204: error: "Node" has no attribute "obj"  [attr-defined]
fms_mo/utils/torchscript_utils.py:205: error: "Node" has no attribute "name"  [attr-defined]
fms_mo/utils/torchscript_utils.py:207: error: "Node" has no attribute "name"  [attr-defined]
fms_mo/utils/torchscript_utils.py:209: error: "Node" has no attribute "name"  [attr-defined]
fms_mo/utils/torchscript_utils.py:212: error: "Node" has no attribute "Op"  [attr-defined]
fms_mo/utils/torchscript_utils.py:214: error: "Node" has no attribute "unpackIdx"  [attr-defined]
fms_mo/utils/torchscript_utils.py:216: error: "Node" has no attribute "lineno"  [attr-defined]
fms_mo/utils/torchscript_utils.py:217: error: "Node" has no attribute "operator"  [attr-defined]
fms_mo/utils/torchscript_utils.py:219: error: "Node" has no attribute "parents"  [attr-defined]
fms_mo/utils/torchscript_utils.py:220: error: "Node" has no attribute "parents_ptr"  [attr-defined]
fms_mo/utils/torchscript_utils.py:221: error: "Node" has no attribute "scope"  [attr-defined]
fms_mo/utils/torchscript_utils.py:222: error: "Node" has no attribute "modname"  [attr-defined]
fms_mo/utils/torchscript_utils.py:223: error: "Node" has no attribute "children"  [attr-defined]
fms_mo/utils/torchscript_utils.py:224: error: "Node" has no attribute "children_ptr"  [attr-defined]
fms_mo/utils/torchscript_utils.py:225: error: "Node" has no attribute "TSparents"  [attr-defined]
fms_mo/utils/torchscript_utils.py:226: error: "Node" has no attribute "TSoutputs"  [attr-defined]
fms_mo/utils/torchscript_utils.py:228: error: "Node" has no attribute "name"  [attr-defined]
fms_mo/calib.py:485: error: Item "None" of "FrameType | None" has no attribute "f_code"  [union-attr]
fms_mo/quant/ptq.py:2626: error: Item "None" of "Tensor | None" has no attribute "zero_"  [union-attr]
fms_mo/quant/ptq.py:2627: error: Item "None" of "Tensor | None" has no attribute "fill_"  [union-attr]

Signed-off-by: Martin Hickey <[email protected]>

Author: hickeyma

fms_mo/calib.py

@@ -482,7 +482,8 @@ def qmodel_calib(
         return model
 
     DPorDDPdevices = None
-    if "qmodel_prep" not in sys._getframe().f_back.f_code.co_name:
+    f_back = sys._getframe().f_back
+    if f_back and "qmodel_prep" not in f_back.f_code.co_name:
         model.to(currDev)
         qcfg["wasDPmodel"] = qcfg.get("wasDPmodel", isinstance(model, nn.DataParallel))
         qcfg["wasDDPmodel"] = qcfg.get(

fms_mo/custom_ext_kernels/utils.py

@@ -74,10 +74,10 @@
     # Third Party
     import torch.library as lib
 
-    reg_op = partial(lib.custom_op, mutates_args=())
+    reg_op = partial(lib.custom_op, mutates_args=())  # type: ignore[attr-defined]
     reg_op_func = lib.define  # NOTE this is func, not decorator
-    kernel_impl = lib.register_kernel
-    reg_fake = lib.register_fake
+    kernel_impl = lib.register_kernel  # type: ignore[attr-defined]
+    reg_fake = lib.register_fake  # type: ignore[attr-defined]
 
 else:
     raise RuntimeError("Custom Op registration only works for >PT2.1")

fms_mo/quant/ptq.py

@@ -2623,8 +2623,10 @@ def reset_bn(module: nn.BatchNorm2d):
     Function not currently used.
     """
     if module.track_running_stats:
-        module.running_mean.zero_()
-        module.running_var.fill_(1 - module.eps)
+        if running_mean := module.running_mean:
+            running_mean.zero_()
+        if running_var := module.running_var:
+            running_var.fill_(1 - module.eps)
         # we do not reset numer of tracked batches here
     if module.affine:
         nn.init.ones_(module.weight)

fms_mo/quant/quantizers.py

@@ -3895,7 +3895,8 @@ def forward(self, x: torch.Tensor):
                 self.delta = torch.nn.Parameter(delta)
             else:
                 delta, zero_point = self.init_quantization_scale(x, self.channel_wise)
-                self.delta.fill_(delta)
+                if self_data := self.delta:
+                    self_data.fill_(delta)
                 self.zero_point.fill_(zero_point)
             self.inited = True
 
@@ -3960,7 +3961,7 @@ def init_quantization_scale(self, x: torch.Tensor, channel_wise: bool = False):
                     if score < best_score:
                         best_score = score
                         delta = (new_max - new_min) / (2**self.n_bits - 1)
-                        zero_point = (-new_min / delta).round()
+                        zero_point = (-new_min / delta).round()  # type: ignore[union-attr]
             else:
                 raise NotImplementedError
 
@@ -5389,7 +5390,7 @@ def __init__(
             if "e4m3" in q_mode:
                 self.float8_dtype = torch.float8_e4m3fn
             elif "e5m2" in q_mode:
-                self.float8_dtype = torch.float8_e5m2G
+                self.float8_dtype = torch.float8_e5m2
             else:
                 raise ValueError("FP8 only supports e4m3 and e5m2")
             self.emulate = emulate

fms_mo/utils/torchscript_utils.py

@@ -179,9 +179,14 @@ def __init__(self, node_input, dictionary_of_nodes: dict):
                 )
             operator, operands = parse_operation(op_str)
             if "aten::_conv" in op_str:
-                self.ch_in = list(native_torchscript_node.inputs())[0].type().sizes()
-                # NOTE: Needed for finding shortcut convolutions later
-                self.ch_out = list(native_torchscript_node.outputs())[0].type().sizes()
+                if native_torchscript_node:
+                    self.ch_in = (
+                        list(native_torchscript_node.inputs())[0].type().sizes()
+                    )
+                    # NOTE: Needed for finding shortcut convolutions later
+                    self.ch_out = (
+                        list(native_torchscript_node.outputs())[0].type().sizes()
+                    )
         else:
             node_def = node_input_repr
             op_str, operator, operands = None, None, None
@@ -201,31 +206,34 @@ def __init__(self, node_input, dictionary_of_nodes: dict):
             working_str = node_input_repr[start_index:end_index]
             start_index = end_index + 2
 
-            node_instance.name, node_instance.obj = working_str.split(" : ")
-            node_instance.name = node_instance.name.strip()
+            # pylint: disable=line-too-long
+            node_instance.name, node_instance.obj = working_str.split(" : ")  # type: ignore[attr-defined]
+            node_instance.name = node_instance.name.strip()  # type: ignore[attr-defined]
             if native_torchscript_outputs:
-                if node_instance.name not in native_torchscript_outputs:
+                # pylint: disable=line-too-long
+                if node_instance.name not in native_torchscript_outputs:  # type: ignore[attr-defined]
+                    # pylint: disable=line-too-long
                     logger.error(
-                        f"Node def {node_instance.name} not in nativeTSoutputs "
+                        f"Node def {node_instance.name} not in nativeTSoutputs "  # type: ignore[attr-defined]
                         f"{native_torchscript_outputs}"
                     )
-            node_instance.Op = op_str
+            node_instance.Op = op_str  # type: ignore[attr-defined]
             if node_def_in_one_line > 1:
-                node_instance.unpackIdx = node_index
+                node_instance.unpackIdx = node_index  # type: ignore[attr-defined]
             if line_number:
-                node_instance.lineno = line_number
-            node_instance.operator = operator
+                node_instance.lineno = line_number  # type: ignore[attr-defined]
+            node_instance.operator = operator  # type: ignore[attr-defined]
             # This is the name of parents, not the pointer to the parent nodes
-            node_instance.parents = operands
-            node_instance.parents_ptr = []
-            node_instance.scope = scope_repr
-            node_instance.modname = module_name
-            node_instance.children = []
-            node_instance.children_ptr = []
-            node_instance.TSparents = native_torchscript_parents
-            node_instance.TSoutputs = native_torchscript_outputs
+            node_instance.parents = operands  # type: ignore[attr-defined]
+            node_instance.parents_ptr = []  # type: ignore[attr-defined]
+            node_instance.scope = scope_repr  # type: ignore[attr-defined]
+            node_instance.modname = module_name  # type: ignore[attr-defined]
+            node_instance.children = []  # type: ignore[attr-defined]
+            node_instance.children_ptr = []  # type: ignore[attr-defined]
+            node_instance.TSparents = native_torchscript_parents  # type: ignore[attr-defined]
+            node_instance.TSoutputs = native_torchscript_outputs  # type: ignore[attr-defined]
             # graph.dictionary_of_nodes will keep a record of all the nodes
-            dictionary_of_nodes[node_instance.name] = node_instance
+            dictionary_of_nodes[node_instance.name] = node_instance  # type: ignore[attr-defined]
 
     def __repr__(self):
         return f"{self.name} "