fix qdq utils issues and remove global cast replacements (#489)

## What does this PR do?

**Type of change:** Bug fix <!-- Use one of the following: Bug fix, new
feature, new example, new tests, documentation. -->

**Overview:** While attempting to `transformers`, `onnx`, `torch` and
`modelopt`, it is shown the following:

- Original `Reshape` - `Cast` - `Transpose` becomes `Reshape` -
`Transpose`, so there is no required `Cast` for int4
- Global cast replacement to FP16 is not required since it will affect
other Modules

## Usage
<!-- You can potentially add a usage example below. -->

```python
# Add a code snippet demonstrating how to use this
```

## Testing
<!-- Mention how have you tested your change if applicable. -->

## Before your PR is "*Ready for review*"
<!-- If you haven't finished some of the above items you can still open
`Draft` PR. -->

- **Make sure you read and follow [Contributor
guidelines](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CONTRIBUTING.md)**
and your commits are signed.
- **Is this change backward compatible?**: Yes <!--- If No, explain why.
-->
- **Did you write any new necessary tests?**: No
- **Did you add or update any necessary documentation?**: No
- **Did you update
[Changelog](https://github.com/NVIDIA/TensorRT-Model-Optimizer/blob/main/CHANGELOG.rst)?**:
No <!--- Only for new features, API changes, critical bug fixes or bw
breaking changes. -->

## Additional Information
<!-- E.g. related issue. -->

Signed-off-by: Luxiao Zheng <luxiaoz@nvidia.com>

Author: nvluxiaoz

modelopt/onnx/quantization/qdq_utils.py

@@ -1111,17 +1111,20 @@ def quantize_weights_to_int4(
         scale_shape = [*weight_shape[:-1], weight_shape[-1] // block_size]
         scale = scale.reshape(scale_shape)
         reshape_child_nodes = [n for n in graph.node if reshape_node.output[0] in n.input]
-        assert len(reshape_child_nodes) == 1, f"Expected exactly one transpose node for {node.name}"
+        assert len(reshape_child_nodes) == 1, f"Expected exactly one child node for {node.name}"
 
-        # Remove unnecessary Cast node
-        cast_node = reshape_child_nodes[0]
-        assert cast_node.op_type == "Cast", f"Expected Cast node for {node.name}"
-        nodes_to_remove.append(cast_node.name)
-        cast_child_nodes = [n for n in graph.node if cast_node.output[0] in n.input]
+        # Check if there's an optional Cast node between Reshape and Transpose/MatMul/Gemm
+        next_node = reshape_child_nodes[0]
+        if next_node.op_type == "Cast":
+            # Remove unnecessary Cast node
+            cast_node = next_node
+            nodes_to_remove.append(cast_node.name)
+            cast_child_nodes = [n for n in graph.node if cast_node.output[0] in n.input]
+            next_node = cast_child_nodes[0]
 
         # Transpose weights and scales if present
-        if cast_child_nodes[0].op_type == "Transpose":
-            transpose_node = cast_child_nodes[0]
+        if next_node.op_type == "Transpose":
+            transpose_node = next_node
             nodes_to_remove.append(transpose_node.name)
             assert transpose_node.op_type == "Transpose", f"Expected Transpose node for {node.name}"
             perm = None
@@ -1138,7 +1141,7 @@ def quantize_weights_to_int4(
             )
             matmul_node = transpose_child_nodes[0]
         else:
-            matmul_node = cast_child_nodes[0]
+            matmul_node = next_node
         assert matmul_node.op_type in ["MatMul", "Gemm"], (
             f"Expected MatMul or Gemm node for {node.name}"
         )
@@ -1189,21 +1192,6 @@ def is_pre_quant_scale_node(node: onnx.NodeProto) -> bool:
     del graph.node[:]
     graph.node.extend(new_nodes)
 
-    def is_fp32_cast(node: onnx.NodeProto) -> bool:
-        return any(
-            attr.name == "to" and attr.i == onnx.TensorProto.FLOAT for attr in node.attribute
-        )
-
-    # Change all Cast nodes that cast to float32 (TensorProto.FLOAT) to cast to float16 (TensorProto.FLOAT16)
-    for node in graph.node:
-        if node.op_type == "Cast":
-            # Skip Cast nodes that are part of normalization layers and outputs
-            if "norm/Cast" in node.name and is_fp32_cast(node):
-                continue
-            for attr in node.attribute:
-                if attr.name == "to" and attr.i == onnx.TensorProto.FLOAT:
-                    attr.i = onnx.TensorProto.FLOAT16
-
     # Cast bias to float16
     for node in graph.node:
         if node.op_type == "Add" and "proj/Add" in node.name:
@@ -1310,13 +1298,6 @@ def quantize_weights_to_mxfp8(
                 if attr.name == "output_dtype":
                     attr.i = onnx_dtype_map["Half"]
 
-    # set Cast to FP16
-    for node in graph.node:
-        if node.op_type == "Cast":
-            for attr in node.attribute:
-                if attr.name == "to" and attr.i == onnx.TensorProto.FLOAT:
-                    attr.i = onnx_dtype_map["Half"]
-
     # Currently only tanh approximation is supported for Gelu
     for node in gelu_nodes:
         for attr in node.attribute: