From b8aa1767cdca37def5d21cfa8aaf4a23e8ed3905 Mon Sep 17 00:00:00 2001
From: Jerry Zhang <jerryzh168@gmail.com>
Date: Thu, 13 Oct 2022 17:02:32 -0700
Subject: [PATCH] [quant][be] Remove unused helper functions in convert.py
 (#86913)

Summary:
att

Test Plan:
python test/test_quantization.py TestQuantizeFx

Reviewers:

Subscribers:

Tasks:

Tags:
Pull Request resolved: https://github.com/pytorch/pytorch/pull/86913
Approved by: https://github.com/vkuzo
---
 torch/ao/quantization/fx/convert.py | 65 -----------------------------
 1 file changed, 65 deletions(-)

diff --git a/torch/ao/quantization/fx/convert.py b/torch/ao/quantization/fx/convert.py
index bb9e261cef9c..aa402e882abc 100644
--- a/torch/ao/quantization/fx/convert.py
+++ b/torch/ao/quantization/fx/convert.py
@@ -77,14 +77,11 @@ __all__ = [
     "convert_custom_module",
     "convert_standalone_module",
     "convert_weighted_module",
-    "duplicate_dequantize_node",
-    "duplicate_quantize_dynamic_node",
     "get_module_path_and_prefix",
     "has_none_qconfig",
     "insert_dequantize_node",
     "maybe_get_observer_for_node",
     "maybe_recursive_remove_dequantize",
-    "remove_extra_dequantize",
     "restore_state",
     "run_weight_observers",
 ]
@@ -129,68 +126,6 @@ def run_weight_observers(observed: GraphModule, backend_config: BackendConfig) -
                 # run the weight observer
                 weight_observer_module()
 
-# this method is temporary will be removed soon
-def duplicate_quantize_dynamic_node(quantized: QuantizedGraphModule) -> QuantizedGraphModule:
-    quantized_root = quantized
-    for node in quantized.graph.nodes:
-        if (node.op == "call_function" and node.target == torch.quantize_per_tensor_dynamic):
-            users = list(node.users)
-            if len(users) > 1:
-                for user in users:
-                    with quantized.graph.inserting_before(node):
-                        new_node = quantized.graph.create_node(
-                            "call_function",
-                            torch.quantize_per_tensor_dynamic,
-                            node.args,
-                            node.kwargs)
-                    user.replace_input_with(node, new_node)
-                quantized.graph.erase_node(node)
-
-    quantized = QuantizedGraphModule(quantized_root, quantized.graph, quantized_root.preserved_attr_names)
-    return quantized
-
-def duplicate_dequantize_node(quantized: QuantizedGraphModule) -> QuantizedGraphModule:
-    """
-    If a dequantize node has multiple uses, duplicate it and create one dequantize node for each use.
-    This is to enable the pattern matching to map from individual quant - dequant - ref_module to
-    final quantized module.
-    """
-    quantized_root = quantized
-    for node in quantized.graph.nodes:
-        if (node.op == "call_method" and node.target == "dequantize" or
-           (node.op == "call_function" and node.target == torch.dequantize)):
-            users = list(node.users)
-            if len(users) > 1:
-                for user in users:
-                    with quantized.graph.inserting_before(node):
-                        new_node = quantized.graph.create_node("call_method", "dequantize", node.args, {})
-                    user.replace_input_with(node, new_node)
-                quantized.graph.erase_node(node)
-
-    quantized = QuantizedGraphModule(quantized_root, quantized.graph, quantized_root.preserved_attr_names)
-    return quantized
-
-def remove_extra_dequantize(quantized: QuantizedGraphModule) -> QuantizedGraphModule:
-    """
-    Removes duplicate dequant nodes in the graph, for an operator that has multiple dequant nodes as a user,
-    replace them with a single dequant node that can be shared across all the uses.
-    """
-    quantized_root = quantized
-    for node in quantized.graph.nodes:
-        users = list(node.users)
-        dequant_users = [user for user in node.users if user.op == "call_method" and user.target == "dequantize" or
-                         (user.op == "call_function" and user.target == torch.dequantize)]
-
-        if len(dequant_users) > 1:
-            with quantized.graph.inserting_after(node):
-                unique_dq = quantized.graph.create_node("call_method", "dequantize", users[0].args, {})
-            for dequant in dequant_users:
-                dequant.replace_all_uses_with(unique_dq)
-                quantized.graph.erase_node(dequant)
-
-    quantized = QuantizedGraphModule(quantized_root, quantized.graph, quantized_root.preserved_attr_names)
-    return quantized
-
 def maybe_recursive_remove_dequantize(arg: Any, node: Node, graph: Graph):
     """ If the arg is a dequantize Node, or a list/tuple/dict of dequantize Node,
     we'll recursively remove the dequantize Node