[BUG] Stream-K kernel breaks for some GEMM Problem-K

[manishucsd]

GEMM Problem Shape --m=8 --n=8192 --k=8192 Does NOT Work

/tools/profiler/cutlass_profiler --dist=uniform,min:-2.3,max:2.3,scale:-1 --kernels=cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x128x64_4x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_nosmem --m=8 --n=8192  --k=8192 --verification-enabled
=false 



=============================
  Problem ID: 1

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x128x64_4x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_nosmem

          Status: Success
    Verification: OFF
     Disposition: Failed


       Arguments: --gemm_kind=universal --m=8 --n=8192 --k=8192 --A=bf16:row --B=bf16:column --C=bf16:column --D=bf16:column  \
                  --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 --batch_count=1 --raster_order=heuristic  \
                  --runtime_input_datatype_a=invalid --runtime_input_datatype_b=invalid --use_pdl=false --enable_sm90_mixed_dtype_shuffle_test=false  \
                  --swizzle_size=1 --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=64 --cluster_m=1 --cluster_n=1  \
                  --cluster_k=1 --cluster_m_fallback=0 --cluster_n_fallback=0 --cluster_k_fallback=0 --stages=7 --warps_m=4  \
                  --warps_n=2 --warps_k=1 --inst_m=64 --inst_n=128 --inst_k=16 --min_cc=90 --max_cc=90

           Bytes: 134479872  bytes
           FLOPs: 1073872896  flops
           FLOPs/Byte: 7

GEMM Problem Shape --m=8 --n=8192 --k=128 Works

./tools/profiler/cutlass_profiler --dist=uniform,min:-2.3,max:2.3,scale:-1 --kernels=cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x128x64_4x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_nosmem --m=8 --n=8192  --k=128 --verification-enabled=false



=============================
  Problem ID: 1

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x128x64_4x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_nosmem

          Status: Success
    Verification: OFF
     Disposition: Not verified


       Arguments: --gemm_kind=universal --m=8 --n=8192 --k=128 --A=bf16:row --B=bf16:column --C=bf16:column --D=bf16:column  \
                  --alpha=1 --beta=0 --split_k_mode=serial --split_k_slices=1 --batch_count=1 --raster_order=heuristic  \
                  --runtime_input_datatype_a=invalid --runtime_input_datatype_b=invalid --use_pdl=false --enable_sm90_mixed_dtype_shuffle_test=false  \
                  --swizzle_size=1 --op_class=tensorop --accum=f32 --cta_m=128 --cta_n=128 --cta_k=64 --cluster_m=1 --cluster_n=1  \
                  --cluster_k=1 --cluster_m_fallback=0 --cluster_n_fallback=0 --cluster_k_fallback=0 --stages=7 --warps_m=4  \
                  --warps_n=2 --warps_k=1 --inst_m=64 --inst_n=128 --inst_k=16 --min_cc=90 --max_cc=90

           Bytes: 2230272  bytes
           FLOPs: 16908288  flops
           FLOPs/Byte: 7

         Runtime: 0.0130992  ms
          Memory: 158.567 GiB/s

            Math: 1290.79 GFLOP/s

[manishucsd]

  Problem ID: 1

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x128x64_4x1x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_tma

          Status: Success
    Verification: OFF
     Disposition: Failed

Another one that failed . The common pattern between these are cluster=4x1x1, stream_k, ws

[manishucsd]

  Problem ID: 1

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x128x64_4x2x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_nosmem

          Status: Success
    Verification: OFF
     Disposition: Failed

Problem ID: 1

        Provider: CUTLASS
   OperationKind: gemm
       Operation: cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16_128x128x64_4x4x1_0_tnn_align8_stream_k_warpspecialized_cooperative_epi_nosmem

          Status: Error: internal
    Verification: OFF
     Disposition: Failed

[hwu36]

@jackkosaian

[jackkosaian]

Can you provide the full CMake config you used?

Also, we recently fixed a similar issue internally (also occurring with larger clusters only). It is planned to be pushed here soon.

Can you see if the following change (which is the one that will be upstreamed) fixes the issue for you?
Add the following line here:

      new_hw_info.max_active_clusters = hw_info.max_active_clusters;

[manishucsd]

cmake -DCMAKE_BUILD_TYPE:STRING=Release -DCUTLASS_NVCC_ARCHS:STRING=90a -DCUTLASS_NVCC_KEEP:STRING=OFF -DCUTLASS_ENABLE_F16C:STRING=ON -DCUTLASS_LIBRARY_KERNELS:STRING=cutlass3x_sm90_tensorop_s64x128x16gemm_bf16_bf16_f32_bf16_bf16*tnn*align8 -DCUTLASS_LIBRARY_INSTANTIATION_LEVEL:STRING=max -DCUTLASS_LIBRARY_IGNORE_KERNELS:STRING=gemm_grouped*,gemm_planar* -DCUTLASS_ENABLE_CUBLAS:STRING=ON -DCMAKE_EXPORT_COMPILE_COMMANDS:BOOL=TRUE -DCMAKE_C_COMPILER:FILEPATH=/usr/bin/gcc -DCMAKE_CXX_COMPILER:FILEPATH=/usr/bin/g++ --no-warn-unused-cli -S/home/manish_magic_dev/repos/cutlass/cutlass_tree_2/cutlass -B/home/manish_magic_dev/repos/cutlass/cutlass_tree_2/build

[manishucsd]

hw_info.sm_count if set will restrict the kernel to run on lower than max SMs? For e.g., if I set this to 128 than 4 SMs on H100 with 132 SMs will be left out. Is this understanding correct? Or there is more to it?

@jackkosaian , code in your comment suggest the change is in hw_info.max_active_clusters and the hyperlink takes me to hw_info.sm_count. I will wait for the full fix to merge in the main, hopefully we can get it merged soon.

[jackkosaian]

Sorry, the suggestion I was trying to make was to add the code that I pasted in the comment (max_active_clusters) below the code linked.

Here's the diff:

diff --git a/include/cutlass/gemm/kernel/tile_scheduler_params.h b/include/cutlass/gemm/kernel/tile_scheduler_params.h
index aa599a35..a4467e8a 100644
--- a/include/cutlass/gemm/kernel/tile_scheduler_params.h
+++ b/include/cutlass/gemm/kernel/tile_scheduler_params.h
@@ -1204,6 +1204,7 @@ struct PersistentTileSchedulerSm90StreamKParams {
       KernelHardwareInfo new_hw_info;
       new_hw_info.device_id = hw_info.device_id;
       new_hw_info.sm_count = hw_info.sm_count;
+      new_hw_info.max_active_clusters = hw_info.max_active_clusters;
       if (new_hw_info.sm_count <= 0) {
         CUTLASS_TRACE_HOST("  WARNING: Arguments do not include a valid SM count.\n"
             "  For optimal performance, populate the arguments KernelHardwareInfo struct with the SM count.");

I was able to reproduce the issue you mentioned before making this diff, and the issue went away after the diff.

[manishucsd]

Let us check that in into mainline.

[jackkosaian]

It will be merged in when we tag 3.8 (soon).

[manishucsd]

Please close it if this merged and you verified it on your end. We will enable stream_k again, and reopen, if we see an issue.

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[github-actions]

This issue has been labeled inactive-90d due to no recent activity in the past 90 days. Please close this issue if no further response or action is needed. Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed.