onnxruntime

b668e476 - fix(quantization): emit axis on DequantizeLinear for per-channel dynamic quantization (#28228)

fix(quantization): emit axis on DequantizeLinear for per-channel dynamic quantization (#28228) ## Summary - Fix `quantize_dynamic(per_channel=True)` so weights quantized per-channel produce a `DequantizeLinear` node with the correct `axis` attribute. - Stop dropping the channel axis when `quantize_weight_per_channel` populates `QuantizedValue` (was hardcoded to `None`). - Gate the scalar-scale assertion in `_dequantize_value` on `axis is None` so per-channel scales (1-D tensors) are accepted. ## Motivation Fixes #19997. When a model is quantized with `quantize_dynamic(..., per_channel=True)` and a per-channel weight reaches `_dequantize_value` (e.g. via `_dequantize_outputs` when the weight is in the graph outputs), two bugs surface: 1. `quantize_weight_per_channel` stores `QuantizedValue.axis = None` even though it received a real `channel_axis`, so the per-channel information is lost. 2. `_dequantize_value` (a) asserts `scale_init.size == 1`, which fails for a 1-D per-channel scale, and (b) builds the `DequantizeLinear` node without an `axis` attribute, producing an invalid ONNX node when the model is consumed. PR #22283 (Nov 2024) softened the assertion against `None`-typed scales but left the underlying axis-propagation bug in place. ## Changes - `onnxruntime/python/tools/quantization/onnx_quantizer.py` - `quantize_weight_per_channel`: pass `channel_axis` (was `None`) into `QuantizedValue`. - `_dequantize_value`: only require a scalar scale on the per-tensor path (`axis is None`); forward `axis=quantized_value.axis` to `onnx.helper.make_node("DequantizeLinear", ...)`. `make_node` silently omits the attribute when `axis` is `None`, so the per-tensor path is unchanged. - `onnxruntime/test/python/quantization/test_quant_issues.py` - New regression test `test_dynamic_quantize_per_channel_emits_axis_attribute` that builds a minimal MatMul model with the weight routed to a graph output (to force the `_dequantize_outputs` -> `_dequantize_value` path), runs `quantize_dynamic(per_channel=True)`, and asserts the emitted `DequantizeLinear` has the `axis` attribute and a 1-D multi-element scale initializer. ## Test Plan - `python -m pytest onnxruntime/test/python/quantization/test_quant_issues.py -xvs` — new test passes; existing test skipped as before. - `python -m pytest onnxruntime/test/python/quantization/test_op_matmul.py` — 7 passed, 8 skipped (no regression). - `python -m pytest onnxruntime/test/python/quantization/test_qdq.py -k per_channel` — 1 passed. - `lintrunner -a` on changed files: clean.