Test PARQ with torchao activation quantization

[lisjin]

Added a test case to show numerical equivalency between quantizing:

1. intx weights with PARQ's UnifTorchaoQuantizer + int8 activations with torchao's FakeQuantizeConfig
2. intx weights + int8 activations with torchao's Int8DynamicActivationIntxWeightConfig

Next steps with target EmbeddingQuantizer and PackedLinearInt8DynamicActivationIntxWeightLayout.

[pytorch-bot]

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🧪 See artifacts and rendered test results at hud.pytorch.org/pr/pytorch/ao/2370

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❌ 2 New Failures, 1 Cancelled Job, 1 Unrelated Failure

As of commit 4c951a8 with merge base dd22777 ():

NEW FAILURES - The following jobs have failed:

• Run Regression Tests / test-nightly (CUDA Nightly, linux.g5.12xlarge.nvidia.gpu, --pre torch --index-url https://downloa... / linux-job (gh)
  test/sparsity/test_sparse_api.py::TestQuantSemiSparse::test_quant_semi_sparse_compile_False
• Run TorchAO Experimental Tests / test-cpu-ops (macos-14) (gh)
  test_replace_q_dq_patterns_with_quantized_linear_ops_pass

CANCELLED JOB - The following job was cancelled. Please retry:

• Run TorchAO Experimental Tests / test-cpu-ops (linux.arm64.2xlarge) (gh)
  ##[error]The operation was canceled.

BROKEN TRUNK - The following job failed but were present on the merge base:

👉 Rebase onto the `viable/strict` branch to avoid these failures

• Run Regression Tests / test-nightly (CPU Nightly, linux.4xlarge, --pre torch --index-url https://download.pytorch.org/wh... / linux-job (gh) (trunk failure)
  test/quantization/pt2e/test_x86inductor_fusion.py::TestPatternMatcher::test_smooth_quant_with_int_mm_has_bias_True_float32_per_channel_quant_True_dynamic_True

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