Add xetla streamk gemm into benchmark

Author: yudongsi

benchmarks/triton_kernels_benchmark/gemm_streamk_benchmark.py

@@ -10,6 +10,7 @@
 import triton.language as tl
 
 import triton_kernels_benchmark as benchmark_suit
+import xetla_kernel
 
 if benchmark_suit.USE_IPEX_OPTION:
     import intel_extension_for_pytorch  # type: ignore # noqa: F401
@@ -253,9 +254,9 @@ def matmul(a: torch.Tensor, b: torch.Tensor, c: torch.Tensor):
         line_arg='provider',
         # argument name whose value corresponds to a different line in the plot
         # possible values for `line_arg``
-        line_vals=['triton'],
+        line_vals=['triton', 'xetla'],
         # label name for the lines
-        line_names=['Triton'],
+        line_names=['Triton', 'XeTLA'],
         # line styles
         styles=[('green', '-'), ('green', '--'), ('blue', '-'), ('blue', '--')],
         ylabel=['GB/s', 'TFlops'],  # label name for the y-axis
@@ -280,6 +281,20 @@ def benchmark(M, N, K, provider):
         benchmark_suit.assert_close(triton_fn(), torch_fn(), atol=1e-4, rtol=1e-2, err_msg='triton to torch')
         _, min_ms, max_ms, mean_ms, cv = benchmark_suit.do_bench(triton_fn, warmup=10, rep=10, quantiles=quantiles,
                                                                  kernel_name=['first_wave', 'full_tiles'])
+    elif provider == 'xetla':
+        c = torch.empty((M, N), device='xpu', dtype=torch.float32)
+        acc = torch.empty((M, N), device='xpu', dtype=torch.float32)
+        cnt = torch.empty((M, N), device='xpu', dtype=torch.int32)
+
+        name = f'gemm_streamk_shape_{M}_{K}_{N}'
+        func = getattr(xetla_kernel, name)
+        xetla_fn = lambda: func(a, b, c, acc, cnt)
+        torch_fn = lambda: torch.matmul(a, b).to(torch.float32)
+
+        # benchmark_suit.assert_close(xetla_fn(), torch_fn(), atol=1e-4, rtol=1.0, err_msg='xetla to torch')
+        _, min_ms, max_ms, mean_ms, cv = benchmark_suit.do_bench(
+            xetla_fn, warmup=10, rep=10, quantiles=quantiles,
+            kernel_name='gpu::xetla::kernel::gemm_universal_t<dispatch_stream_k')
     else:
         raise NotImplementedError(f'Unsupported provider {provider}')
 

benchmarks/xetla_kernel/python_main.cpp

@@ -280,7 +280,10 @@ PYBIND11_MODULE(xetla_kernel, m) {
         &bf16_gemm<Test_4096x8x128x16384_row_row>, "bf16_gemm (XeTLA)");
   m.def("gemm_shape_4096_8_16384_128",
         &bf16_gemm<Test_4096x8x16384x128_row_row>, "bf16_gemm (XeTLA)");
-  // flash_attn_fwd
+  // gemm stream k
+  m.def("gemm_streamk_shape_3072_4096_3072", &bf16_stream_k_gemm,
+        "bf16_gemm_streamk (XeTLA)");
+  // flash_attn
   m.def("flash_attn_causal_false", &flash_attn<false, false, false>,
         "flash attn fwd (XeTLA)");
   m.def("flash_attn_causal_true", &flash_attn<false, true, false>,

benchmarks/xetla_kernel/stream_k_gemm/stream_k_gemm.h

@@ -36,9 +36,6 @@ sycl::event stream_k_gemm_run(void *_A, void *_B, void *_C, void *_Acc,
   using data_type_c = float;
   using data_type_acc = float;
 
-  auto context = queue.get_info<sycl::info::queue::context>();
-  auto device = queue.get_info<sycl::info::queue::device>();
-
   data_type_a *A = static_cast<data_type_a *>(_A);
   data_type_b *B = static_cast<data_type_b *>(_B);
   data_type_c *C = static_cast<data_type_c *>(_C);
@@ -52,7 +49,7 @@ sycl::event stream_k_gemm_run(void *_A, void *_B, void *_C, void *_Acc,
   constexpr uint32_t sg_tile_k = 32;
 
   // StreamK parameters - xecores available for stream_k dispatch
-  uint32_t avail_xecores = 32;
+  uint32_t avail_xecores = 64;
 
   // Org the compute shape for sub-matrix
   using tile_shape =