feat:[AutoDeploy] Support Quantized MoE matcher

[Fridah-nv]

Description

• Step 1: update moe pattern matcher to handle fp8_linear in addition to linear. Also, add the corresponding torch_fp8_moe.
• Update FP4 for the same
• Update EP sharding with quantized ops
• Step 2: Handle patched case. For this, update quantize() to be able to map torch_moe to torch_fp8_moe and torch_fp4_moe based on the information in the hf_quant_config.json
• Step 3: update fuse_moe to map torch_{fp4, fp8}moe to trtllm{fp4, fp8}_moe

tests

E2E run

python build_and_run_ad.py --config '{"world_size":8, "model":"/home/fridahou/models/Mixtral-8x7B-Instruct-v0.1-FP8", "attn_backend
":"FlashInfer", "compile_backend":"torch-simple", "benchmark": false}'  --model-kwargs '{}'

Test Coverage

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[Fridah-nv]

/bot run --disable-fail-fast

[Fridah-nv]

Step 1 and 2 merged in :nv-auto-deploy#68
Will open another PR for step 3