[ROCm] Guard group quant RMS norm fusion patterns

[yeqcharlotte]

Summary:
Fix AMD compilation failure for DeepSeek models introduced in #27883.

The issue was that RMSNormQuantFusionPass unconditionally creates
FusedAddRMSNormGroupQuantPattern and RMSNormGroupQuantPattern for
group quantization (GroupShape 64 and 128), but the underlying C++
operation per_token_group_fp8_quant is only available on CUDA
(wrapped in #ifndef USE_ROCM in torch_bindings.cpp).

On AMD platforms, this caused an assertion failure:

AssertionError: unsupported quantization scheme QuantKey(f8e4m3fnuz,scale(f32,dynamic,GroupShape(row=1, col=128)),symmetric)

The fix guards the creation of group quant patterns with
current_platform.is_cuda(), matching the guard used for registering
these keys in QUANT_OPS.

Test Plan:
Ran e2e correctness/perf tests for DeepSeek on AMD. Will paste results after it's completed.
Perf

Ran 40/40 requests in 48.11s
Success rate:        100.00%
QPS:                 0.83
Avg latency:         4.566s
Avg TTFT (client):   553.11ms
P50 TTFT (client):   551.61ms
P99 TTFT (client):   569.35ms
Avg TTIT (client):   40.13ms
P50 TTIT (client):   40.78ms
P99 TTIT (client):   41.43ms
Avg TTFT (server):   825.17ms
Avg TTIT (server):   36.83ms
Avg prefill len:     5823.10 tokens
P50 prefill len:     5824.00 tokens
P99 prefill len:     5886.00 tokens
Avg decode len:      100.00 tokens
P50 decode len:      100.00 tokens
P99 decode len:      100.00 tokens
Peak TPGS: 9.875

Correctness

[2025-12-07 22:38:16,439] [rank 0] [INFO] Evaluation results on task gsm8k.8_shot.1_gen: em: 0.980000 | f1: 0.980000 | em_maj1@1: 0.980000 | f1_maj1@1: 0.980000

Will also wait for external CI

Differential Revision:
D88608586

[gemini-code-assist]

Code Review

This pull request correctly fixes a compilation failure on AMD platforms by guarding CUDA-specific group quantization fusion patterns. The change is logical and aligns with existing patterns in the codebase for platform-specific operations. I've added one suggestion to refactor the new code to reduce duplication and improve maintainability.

[gemini-code-assist]

While this correctly guards the CUDA-specific patterns, there's an opportunity to reduce code duplication. The logic for group shapes 128 and 64 is identical. You can use a loop to register these patterns, which will make the code more concise and easier to maintain.

                for group_size in [128, 64]:
                    group_shape = GroupShape(1, group_size)
                    FusedAddRMSNormGroupQuantPattern(
                        epsilon, FP8_DTYPE, group_shape=group_shape
                    ).register(self.patterns)

                    # Fuse rms_norm + fp8 group quant
                    RMSNormGroupQuantPattern(
                        epsilon, FP8_DTYPE, group_shape=group_shape
                    ).register(self.patterns)

[tjtanaa]

LGTM.

[tjtanaa]

Waiting for the test results from @yeqcharlotte before merging.

[yeqcharlotte]

Waiting for the test results from @yeqcharlotte before merging.

added test results