[ET-VK][qconv] Add apply_relu support to q8ta conv operators

[SS-JIA]

Stack from ghstack (oldest at bottom):

• [ET-VK][ez][qconv] Add auto-selection to prefer im2col for q8ta_conv2d #17568
• [ET-VK][q8ta] Fix addmm arg indexing in QuantizedLinearMatch #17567
• [ET-VK][q8ta] Add q8ta_linear_gemv op for batch-1 int8 linear #17566
• [ET-VK][q8ta] Add q8ta_linear operator for int8 quantized linear #17565
• [ET-VK][export] Update tensor representation sync logic to allow for flexibility in memory layouts #17564
• [ET-VK] Support different input layouts in q8ta_binary operator #17563
• [ET-VK][ez] Always partition batch norm as it will be fused #17508
• [ET-VK] Add fused q8ta_relu unary operator for int8x4 tensors #17507
• -> [ET-VK][qconv] Add apply_relu support to q8ta conv operators #17506
• [ET-VK][qconv] Pad weight_sums buffer to multiple-of-4 alignment #17505
• [ET-VK][qconv] Fix depthwise weight_sums sum dimension #17504

The quantized convolution pattern detector correctly identifies ReLU nodes between conv output and the output quantize node, but the pattern replacement did not pass this information to the fused q8ta operator. When the pattern replaced dequant → conv → relu → quant with q8ta_conv2d, the relu node was removed from the graph but its effect was not preserved. This silently removed all conv-relu non-linearity from int8 quantized models.

Add an apply_relu parameter throughout the full pipeline:

• Custom op schemas and reference implementations (custom_ops_lib.py)
• Pattern replacement (quantized_convolution.py)
• C++ dispatch logic extracts apply_relu and passes it as a spec constant (Q8taConv2d.cpp, Q8taConv2dDW.cpp, Q8taConv2dPW.cpp, Q8taConv2dIm2Col.cpp)
• GLSL shaders apply conditional max(value, 0) after dequantization and before requantization (q8ta_conv2d.glsl, q8ta_conv2d_dw.glsl, q8ta_conv2d_pw.glsl)
• Test operator wrappers updated with proper legacy path handling (TestQ8taConv2d.cpp)

Differential Revision: D93511632

[pytorch-bot]

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❌ 4 New Failures, 1 Pending, 3 Unrelated Failures

As of commit 22ac237 with merge base 7b843e4 ():

NEW FAILURES - The following jobs have failed:

• pull / test-openvino-linux / linux-job (gh)
  RuntimeError: Command docker exec -t 1b81ef20bc4dd1f66e1064673e612bca203f96dee4f716b74a65c71c6dcc0f16 /exec failed with exit code 1
• pull / unittest-arm-backend-with-no-deps (test_pytest_ops_tosa) / linux-job (gh)
  RuntimeError: Command docker exec -t f063df63d91e84f21f1c9fd5394ce8177ca5647ae4155438a0e2e43acf20b106 /exec failed with exit code 1
• pull / unittest-nxp-neutron / linux-job (gh)
  RuntimeError: Command docker exec -t 787eee54737ff62ba4d05b988c5dd1c7366c0272405f343b4f3e5c5af6648de0 /exec failed with exit code 1
• Test Metal Backend / export-model-metal-artifact (openai, whisper-small, non-quantized) / macos-job (gh)
  RuntimeError: Command bash /Users/ec2-user/runner/_work/_temp/exec_script failed with exit code 1

FLAKY - The following jobs failed but were likely due to flakiness present on trunk:

• Test CUDA Builds / export-model-cuda-artifact (mistralai, Voxtral-Mini-3B-2507, quantized-int4-weight-only) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)
• Test CUDA Builds / export-model-cuda-artifact (openai, whisper-small, quantized-int4-weight-only) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)
• Test CUDA Builds / test-models-cuda (mv2) / linux-job (gh) (detected as infra flaky with no log or failing log classifier)

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