text: Title: [Bug]: `triton_scaled_mm` never used on ROCm
  Body: ### Your current environment
  
  <details>
  <summary>The output of `python collect_env.py`</summary>
  
  ```text
  INFO 03-07 02:02:58 [__init__.py:207] Automatically detected platform rocm.
  Collecting environment information...
  PyTorch version: 2.5.1+rocm6.2
  Is debug build: False
  CUDA used to build PyTorch: N/A
  ROCM used to build PyTorch: 6.2.41133-dd7f95766
  
  OS: Ubuntu 22.04.5 LTS (x86_64)
  GCC version: (Ubuntu 10.5.0-1ubuntu1~22.04) 10.5.0
  Clang version: Could not collect
  CMake version: version 3.31.6
  Libc version: glibc-2.35
  
  Python version: 3.10.12 (main, Feb  4 2025, 14:57:36) [GCC 11.4.0] (64-bit runtime)
  Python platform: Linux-5.15.0-131-generic-x86_64-with-glibc2.35
  Is CUDA available: True
  CUDA runtime version: Could not collect
  CUDA_MODULE_LOADING set to: LAZY
  GPU models and configuration: AMD Instinct MI300X (gfx942:sramecc+:xnack-)
  Nvidia driver version: Could not collect
  cuDNN version: Could not collect
  HIP runtime version: 6.2.41133
  MIOpen runtime version: 3.2.0
  Is XNNPACK available: True
  
  CPU:
  Architecture:                         x86_64
  CPU op-mode(s):                       32-bit, 64-bit
  Address sizes:                        46 bits physical, 57 bits virtual
  Byte Order:                           Little Endian
  CPU(s):                               104
  On-line CPU(s) list:                  0-103
  Vendor ID:                            GenuineIntel
  Model name:                           Intel(R) Xeon(R) Platinum 8470
  CPU family:                           6
  Model:                                143
  Thread(s) per core:                   1
  Core(s) per socket:                   52
  Socket(s):                            2
  Stepping:                             8
  BogoMIPS:                             4000.00
  Flags:                                fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf tsc_known_freq pni pclmulqdq dtes64 monitor ds_cpl smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 cat_l2 cdp_l3 invpcid_single cdp_l2 ssbd mba ibrs ibpb stibp ibrs_enhanced fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local split_lock_detect avx_vnni avx512_bf16 wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke waitpkg avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq la57 rdpid bus_lock_detect cldemote movdiri movdir64b enqcmd fsrm md_clear serialize tsxldtrk pconfig arch_lbr amx_bf16 avx512_fp16 amx_tile amx_int8 flush_l1d arch_capabilities
  L1d cache:                            4.9 MiB (104 instances)
  L1i cache:                            3.3 MiB (104 instances)
  L2 cache:                             208 MiB (104 instances)
  L3 cache:                             210 MiB (2 instances)
  NUMA node(s):                         2
  NUMA node0 CPU(s):                    0-51
  NUMA node1 CPU(s):                    52-103
  Vulnerability Gather data sampling:   Not affected
  Vulnerability Itlb multihit:          Not affected
  Vulnerability L1tf:                   Not affected
  Vulnerability Mds:                    Not affected
  Vulnerability Meltdown:               Not affected
  Vulnerability Mmio stale data:        Not affected
  Vulnerability Reg file data sampling: Not affected
  Vulnerability Retbleed:               Not affected
  Vulnerability Spec rstack overflow:   Not affected
  Vulnerability Spec store bypass:      Mitigation; Speculative Store Bypass disabled via prctl and seccomp
  Vulnerability Spectre v1:             Mitigation; usercopy/swapgs barriers and __user pointer sanitization
  Vulnerability Spectre v2:             Mitigation; Enhanced / Automatic IBRS; IBPB conditional; RSB filling; PBRSB-eIBRS SW sequence; BHI BHI_DIS_S
  Vulnerability Srbds:                  Not affected
  Vulnerability Tsx async abort:        Not affected
  
  Versions of relevant libraries:
  [pip3] numpy==1.26.4
  [pip3] pytorch-triton-rocm==3.1.0
  [pip3] pyzmq==26.2.1
  [pip3] torch==2.5.1+rocm6.2
  [pip3] torchaudio==2.5.1+rocm6.2
  [pip3] torchvision==0.20.1+rocm6.2
  [pip3] transformers==4.49.0
  [conda] Could not collect
  ROCM Version: 6.2.41134-65d174c3e
  Neuron SDK Version: N/A
  vLLM Version: 0.7.4.dev189+gae122b1cb
  vLLM Build Flags:
  CUDA Archs: Not Set; ROCm: Disabled; Neuron: Disabled
  GPU Topology:
  ============================ ROCm System Management Interface ============================
  ================================ Weight between two GPUs =================================
         GPU0         GPU1         GPU2         GPU3         GPU4         GPU5         GPU6         GPU7         
  GPU0   0            15           15           15           15           15           15           15           
  GPU1   15           0            15           15           15           15           15           15           
  GPU2   15           15           0            15           15           15           15           15           
  GPU3   15           15           15           0            15           15           15           15           
  GPU4   15           15           15           15           0            15           15           15           
  GPU5   15           15           15           15           15           0            15           15           
  GPU6   15           15           15           15           15           15           0            15           
  GPU7   15           15           15           15           15           15           15           0            
  
  ================================= Hops between two GPUs ==================================
         GPU0         GPU1         GPU2         GPU3         GPU4         GPU5         GPU6         GPU7         
  GPU0   0            1            1            1            1            1            1            1            
  GPU1   1            0            1            1            1            1            1            1            
  GPU2   1            1            0            1            1            1            1            1            
  GPU3   1            1            1            0            1            1            1            1            
  GPU4   1            1            1            1            0            1            1            1            
  GPU5   1            1            1            1            1            0            1            1            
  GPU6   1            1            1            1            1            1            0            1            
  GPU7   1            1            1            1            1            1            1            0            
  
  =============================== Link Type between two GPUs ===============================
         GPU0         GPU1         GPU2         GPU3         GPU4         GPU5         GPU6         GPU7         
  GPU0   0            XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         
  GPU1   XGMI         0            XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         
  GPU2   XGMI         XGMI         0            XGMI         XGMI         XGMI         XGMI         XGMI         
  GPU3   XGMI         XGMI         XGMI         0            XGMI         XGMI         XGMI         XGMI         
  GPU4   XGMI         XGMI         XGMI         XGMI         0            XGMI         XGMI         XGMI         
  GPU5   XGMI         XGMI         XGMI         XGMI         XGMI         0            XGMI         XGMI         
  GPU6   XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         0            XGMI         
  GPU7   XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         XGMI         0            
  
  ======================================= Numa Nodes =======================================
  GPU[0]          : (Topology) Numa Node: 0
  GPU[0]          : (Topology) Numa Affinity: 0
  GPU[1]          : (Topology) Numa Node: 0
  GPU[1]          : (Topology) Numa Affinity: 0
  GPU[2]          : (Topology) Numa Node: 0
  GPU[2]          : (Topology) Numa Affinity: 0
  GPU[3]          : (Topology) Numa Node: 0
  GPU[3]          : (Topology) Numa Affinity: 0
  GPU[4]          : (Topology) Numa Node: 1
  GPU[4]          : (Topology) Numa Affinity: 1
  GPU[5]          : (Topology) Numa Node: 1
  GPU[5]          : (Topology) Numa Affinity: 1
  GPU[6]          : (Topology) Numa Node: 1
  GPU[6]          : (Topology) Numa Affinity: 1
  GPU[7]          : (Topology) Numa Node: 1
  GPU[7]          : (Topology) Numa Affinity: 1
  ================================== End of ROCm SMI Log ===================================
  
  LD_LIBRARY_PATH=/home/luka/git/vllm/.venv/lib/python3.10/site-packages/cv2/../../lib64:
  NCCL_CUMEM_ENABLE=0
  TORCHINDUCTOR_COMPILE_THREADS=1
  CUDA_MODULE_LOADING=LAZY
  ```
  
  </details>
  
  
  ### 🐛 Describe the bug
  
  I found an issue with vLLM and block fp8 linear, where the ROCm platform is incorrectly using a cutlass execution path. Because the cutlass path is always disabled on ROCm, this kernel is never reached, and instead we fall back on either `w8a8_block_fp8_matmul` or `torch.scaled_mm`.
  
  The way we got there:
  - @rasmith added the triton kernel `triton_scaled_mm` into `custom_ops.cutlass_scaled_mm` (not the right place for it in my opinion) in [127c074](https://github.com/vllm-project/vllm/commit/127c07480ecea15e4c2990820c457807ff78a057)
  - @hongxiayang added DeepSeek support, using the cutlass path where cutlass_block_fp8_supported was True by default in [c36ac98](https://github.com/vllm-project/vllm/commit/c36ac98d0118537ec5f3f405a68311a10f9b59a5)
  - @LucasWilkinson fixed the default of `cutlass_block_fp8_supported` param to `cutlass_block_fp8_supported()` which always returns False on ROCm in [76abd0c](https://github.com/vllm-project/vllm/commit/76abd0c88143419826bfc13d2cd29669d0fdfa1b).
  
  The effect of this is that triton_scaled_mm is currently never used.
  
  I think the path forward is to move `triton_scaled_mm` out of the `custom_ops.cutlass_scaled_mm`. This should likely be done as part of larger refactoring of the FP8 code, including the new `Fp8LinearOp` added in #14390. Additionally, it would be good so (at least somewhat) unify the `triton_scaled_mm` with `w8a8_block_fp8_matmul`, which is the fallback for `apply_block_fp8_linear`.
  
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