Fix KeyError in InsertIOQDQ pass for LLM quantization

[mohammed-saalim]

Summary

This PR fixes a KeyError in the InsertIOQDQ pass that occurrs when quantizing LLMs (such as SmolLM2) for the Qualcomm QNN backend.

Problem

In insert_io_qdq.py, the q_dq_map dictionary was missing entries for dequantize operations. When a node's quantization encoding was already a dequantize operation (e.g., dequantize_per_tensor.default), trying to look it up in the map during the _insert phase caused a KeyError.

Solution

Extended the q_dq_map to include dequantize-to-self (identity) mappings for:

• quantized_decomposed.dequantize_per_tensor.default
• quantized_decomposed.dequantize_per_tensor.tensor
• quantized_decomposed.dequantize_per_channel.default
  This allows the pass to correctly handle nodes that have already been processed into dequantized form.

Testing

• Verified that the modified file parses correctly via Python's ast module.
• Confirmed that q_dq_map now contains the expected 6 keys.
• Manual verification on Qualcomm hardware is requested from the maintainers to confirm resolution for the SmolLM2 workflow.
  Fixes Qualcomm Quantization and Lowering for LLM fails #16690

[pytorch-bot]

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[mohammed-saalim]

While changing the quantization recipe (like using 8-bit KV cache) might change the graph structure, the InsertIOQDQ.py
pass should still be robust enough to handle dequantize operations in the IR without throwing a KeyError. This PR ensures the pass is forward-compatible with models that already have these encodings

[mohammed-saalim]

@pytorchbot label "release notes: none"

[Copilot]

Pull request overview

This PR fixes a KeyError in the InsertIOQDQ pass that occurred when quantizing LLMs (such as SmolLM2) for the Qualcomm QNN backend. The error was caused by missing entries in the q_dq_map dictionary for dequantize operations.

Changes:

• Extended q_dq_map with identity mappings for dequantize operations to handle nodes that already have dequantize encodings
• Added three new entries mapping dequantize operations to themselves (per-tensor default, per-tensor tensor, and per-channel default)

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