Update GEMM list of problem sizes (#1662)

The new list includes all problem sizes we reported in presentations.

Signed-off-by: Whitney Tsang <whitney.tsang@intel.com>

benchmarks/xetla_benchmark/gemm_benchmark.py

@@ -324,18 +324,35 @@ def matmul(a, b):
 @triton.testing.perf_report(
     triton.testing.Benchmark(
         # argument names to use as an x-axis for the plot
-        x_names=['B', 'M', 'N', 'K'],
+        x_names=['B', 'M', 'K', 'N'],
         # different possible values for `x_name`
-        x_vals=[[1, 256 * i, 256 * i, 256 * i] for i in range(1, 17)] +  #
-        [[4, 32768, 128, 4096],  #
+        x_vals=[[1, 1024 * i, 1024 * i, 1024 * i] for i in [1, 2, 4, 8]] +  #
+        [[1, 1, 5120, 13824],  #
+         [1, 4, 4096, 12288],  #
+         [1, 512, 8192, 8192],  #
+         [1, 512, 8192, 32768],  #
+         [1, 512, 32768, 8192],  #
+         [1, 1024, 16384, 8192],  #
+         [1, 1024, 28672, 8192],  #
+         [1, 3072, 4096, 3072],  #
+         [1, 4096, 16384, 8192],  #
+         [1, 8192, 16384, 1024],  #
+         [1, 8192, 16384, 4096],  #
+         [1, 16384, 1024, 8192],  #
+         [1, 16384, 4096, 8192],  #
+         [1, 16384, 8192, 1024],  #
+         [1, 16384, 8192, 4096],  #
+         [4, 32768, 128, 4096],  #
          [4, 32768, 4096, 128],  #
-         [32, 4096, 4096, 128]],
+         [32, 4096, 4096, 128],  #
+         [4096, 8, 128, 16384],  #
+         [4096, 8, 16384, 128]],
         line_arg='provider',
         # argument name whose value corresponds to a different line in the plot
         # possible values for `line_arg``
         line_vals=['onednn', 'triton', 'xetla'],
         # label name for the lines
-        line_names=["oneDNN", "Triton", "Xetla"],
+        line_names=["oneDNN", "Triton", "XeTLA"],
         # line styles
         #styles=[('green', '-'), ('green', '--'), ('blue', '-'), ('blue', '--')],
         ylabel="TFLOPS",  # label name for the y-axis
@@ -351,7 +368,7 @@ def benchmark(B, M, N, K, provider):
         a = torch.rand((B, M, K), device='xpu', dtype=torch.bfloat16)
         b = torch.rand((B, K, N), device='xpu', dtype=torch.bfloat16)
 
-    quantiles = [0.5, 0.2, 0.8]
+    quantiles = [0.5, 0.0, 1.0]
 
     # calculate tflops for oneDNN kernel
     def calculate_tflops(ms):