…#165132)

Summary:
Pull Request resolved: #165132

Support tile-wise `1x128` scaling in Inductor Triton for FP8 GEMMs, i.e. scaling values along tensors `a` and `b` represent a `1x128` slice of input.

NOTE: Block-wise `128x128` and `1x128` scaling is only supported in CUDA 12.9+; therefore, tile-wise scaling is currently unsupported in `fbcode` (CUDA 12.4). Use OSS PyTorch to run tile-wise scaling (as with deepseek-style scaling).

Test Plan:
Works out-of-the-box with TritonBench:
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 256 --n 768 --k 512 --output="/home/jananisriram/personal/random_bench.csv" --scaling-pair=BlockWise1x128,BlockWise1x128 --atol=1e-2 --rtol=0.5
```

Differential Revision: D84025878

…#165132)

Summary:
Pull Request resolved: #165132

Support tile-wise `1x128` scaling in Inductor Triton for FP8 GEMMs, i.e. scaling values along tensors `a` and `b` represent a `1x128` slice of input.

NOTE: Block-wise `128x128` and `1x128` scaling is only supported in CUDA 12.9+; therefore, tile-wise scaling is currently unsupported in `fbcode` (CUDA 12.4). Use OSS PyTorch to run tile-wise scaling (as with deepseek-style scaling).

Test Plan:
Works out-of-the-box with TritonBench:
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 256 --n 768 --k 512 --output="/home/jananisriram/personal/random_bench.csv" --scaling-pair=BlockWise1x128,BlockWise1x128 --atol=1e-2 --rtol=0.5
```

Reviewed By: njriasan

Differential Revision: D84025878

…#165132)

Summary:

Support tile-wise `1x128` scaling in Inductor Triton for FP8 GEMMs, i.e. scaling values along tensors `a` and `b` represent a `1x128` slice of input.

NOTE: Block-wise `128x128` and `1x128` scaling is only supported in CUDA 12.9+; therefore, tile-wise scaling is currently unsupported in `fbcode` (CUDA 12.4). Use OSS PyTorch to run tile-wise scaling (as with deepseek-style scaling).

Test Plan:
Works out-of-the-box with TritonBench:
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 256 --n 768 --k 512 --output="/home/jananisriram/personal/random_bench.csv" --scaling-pair=BlockWise1x128,BlockWise1x128 --atol=1e-2 --rtol=0.5
```

Reviewed By: njriasan

Differential Revision: D84025878

…#165132)

Summary:

Support tile-wise `1x128` scaling in Inductor Triton for FP8 GEMMs, i.e. scaling values along tensors `a` and `b` represent a `1x128` slice of input.

NOTE: Block-wise `128x128` and `1x128` scaling is only supported in CUDA 12.9+; therefore, tile-wise scaling is currently unsupported in `fbcode` (CUDA 12.4). Use OSS PyTorch to run tile-wise scaling (as with deepseek-style scaling).

Test Plan:
Works out-of-the-box with TritonBench:
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 256 --n 768 --k 512 --output="/home/jananisriram/personal/random_bench.csv" --scaling-pair=BlockWise1x128,BlockWise1x128 --atol=1e-2 --rtol=0.5
```

Reviewed By: njriasan

Differential Revision: D84025878

…#165132)

Summary:

Support tile-wise `1x128` scaling in Inductor Triton for FP8 GEMMs, i.e. scaling values along tensors `a` and `b` represent a `1x128` slice of input.

NOTE: Block-wise `128x128` and `1x128` scaling is only supported in CUDA 12.9+; therefore, tile-wise scaling is currently unsupported in `fbcode` (CUDA 12.4). Use OSS PyTorch to run tile-wise scaling (as with deepseek-style scaling).

Test Plan:
Works out-of-the-box with TritonBench:
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 256 --n 768 --k 512 --output="/home/jananisriram/personal/random_bench.csv" --scaling-pair=BlockWise1x128,BlockWise1x128 --atol=1e-2 --rtol=0.5
```

Reviewed By: njriasan

Differential Revision: D84025878

…#165132)

Summary:

Support tile-wise `1x128` scaling in Inductor Triton for FP8 GEMMs, i.e. scaling values along tensors `a` and `b` represent a `1x128` slice of input.

NOTE: Block-wise `128x128` and `1x128` scaling is only supported in CUDA 12.9+; therefore, tile-wise scaling is currently unsupported in `fbcode` (CUDA 12.4). Use OSS PyTorch to run tile-wise scaling (as with deepseek-style scaling).

Test Plan:
Works out-of-the-box with TritonBench:
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 256 --n 768 --k 512 --output="/home/jananisriram/personal/random_bench.csv" --scaling-pair=BlockWise1x128,BlockWise1x128 --atol=1e-2 --rtol=0.5
```

Reviewed By: njriasan

Differential Revision: D84025878

…#165132)

Summary:
Support tile-wise `1x128` scaling in Inductor Triton for FP8 GEMMs, i.e. scaling values along tensors `a` and `b` represent a `1x128` slice of input.

NOTE: Block-wise `128x128` and `1x128` scaling is only supported in CUDA 12.9+; therefore, tile-wise scaling is currently unsupported in `fbcode` (CUDA 12.4). Use OSS PyTorch to run tile-wise scaling (as with deepseek-style scaling).

Test Plan:
Works out-of-the-box with TritonBench:
```
TORCHINDUCTOR_CACHE_DIR=~/personal/cache_dir_inductor CUDA_LAUNCH_BLOCKING=1 TORCH_USE_CUDA_DSA=1 TRITON_PRINT_AUTOTUNING=1 TRITON_ALWAYS_COMPILE=1 TORCH_LOGS=+inductor TORCHINDUCTOR_FORCE_DISABLE_CACHES=1 ENABLE_PERSISTENT_TMA_MATMUL=1 TORCHINDUCTOR_MAX_AUTOTUNE_GEMM=1 buck2 run mode/{opt,inplace} pytorch/tritonbench:run -- --op fp8_gemm --only torch_fp8_gemm,pt2_fp8_gemm --metrics tflops,accuracy --m 256 --n 768 --k 512 --output="/home/jananisriram/personal/random_bench.csv" --scaling-pair=BlockWise1x128,BlockWise1x128 --atol=1e-2 --rtol=0.5
```

Differential Revision: D84025878

Pull Request resolved: #165132
Approved by: https://github.com/eqy, https://github.com/drisspg, https://github.com/njriasan