[GPU] support scale/zp as input - #38204

Summary:

As a follow up of #34767 which is to recognize parameters as valid inputs for weights, this PR extends the same support to scale and zero-point (ZP) tensors. The scope doesn't cover conv1x1, because OVEP has blocked scale/zp promotion for conv operator in this related PR

Details:

• Recognize scale / zero-point as model inputs in ConvertFullyConnectedToFullyConnectedCompressed (Intel GPU plugin). Previously the decompression scale and zero point had to be Constants; now they can also come from a Parameter.

  • compressed_weights_pattern.hpp: scale (Multiply) and zero-point (Subtract) operands now match:
    • Constant
    • Parameter
    • Reshape(Parameter)
    • Reshape(Convert(Parameter))
  • convert_fc_to_compressed.cpp:
    • Static shapes required: the callback bails out if the scale or zero-point shape is not static.
    • Reshaping: reshape_const became reshape_input. Constants are still folded into a new Constant; any other input gets a Reshape node.
    • Zero-point conversion: convert_zp_const decides on a Convert from the input's element type, so it works for constant and non-constant zero points. A u8 zero point is passed through unchanged.
    • Runtime info: it is copied onto all newly created nodes.

• oneDNN FC keeps the scale / zero-point input format (fully_connected_onednn.hpp).

  • query_formats now keeps the existing format of the decompression scale and zero-point inputs instead of forcing the default format.
  • This preserves the layout chosen earlier, e.g. fbyx from prepare_quantization for grouped transposed scale/zp. Without it, oneDNN would force a reorder back to the default format.

• Tests

  • convert_fc_to_compressed_test.cpp: added ConvertFCToCompressedParameterScaleZpTest.
    • Scale is Reshape(Convert(Parameter)) or Convert(Reshape(Parameter)), with and without a zero point.
    • Each case expects a FullyConnectedCompressed that takes the parameter scale/zp directly.
  • convert_matmul_to_fc_test.cpp: runs ConvertMatMulToFullyConnected followed by ConvertFullyConnectedToFullyConnectedCompressed.
    • Static scale/zp parameters produce a FullyConnectedCompressed.
    • A dynamic scale or zp Parameter ([?, 1]) leaves the MatMul unchanged. These are regression tests for the shape_b.is_static() assert.
  • fully_connected_gpu_test.cpp: new general (not oneDNN-only) test test_compressed_int8_parameter_scale_zp.

    • Setup: u8 weights [N, K], scale/zp as input_layout with bfyx data.

    • Accuracy: output is compared with a reference computed as (w - zp) * scale.

    • Layout checks per case:

      Test	Scale/zp shape	Expected scale/zp input
      compressed_int8_parameter_scale_zp_per_channel	[1, N]	parameter, bfyx
      compressed_int8_parameter_scale_zp_per_channel_transposed	[N, 1]	parameter, bfyx (already N-innermost, no reorder)
      compressed_int8_parameter_scale_zp_per_group	[G, N]	parameter, bfyx
      compressed_int8_parameter_scale_zp_per_group_transposed	[N, G]	reorder bfyx → fbyx inserted by prepare_quantization

Tickets:

• CVS-194522

AI Assistance:

• AI assistance used: yes
• What AI did: implemented the pattern, callback and oneDNN format changes and wrote the unit tests, iterating on review feedback from the author.
• Human validation (RelWithDebInfo, Windows):
  • ov_gpu_unit_tests --gtest_filter=TransformationTestsF.ConvertFCToCompressed*:*FullyConnected*Compressed*: 27 passed.
  • ov_gpu_unit_tests --device_suffix=0 --gtest_filter=fully_connected_gpu_tests.compressed* (iGPU, UHD 770): 88 passed.
  • ov_gpu_unit_tests --device_suffix=1 --gtest_filter=fully_connected_gpu_tests.compressed* (dGPU, with hardware matmul): 41 passed, 47 skipped by existing device-capability checks, 0 failed.
  • All 4 new compressed_int8_parameter_scale_zp* tests passed on both iGPU and dGPU