extend mxfp8 roofline with more recipes

benchmarks/float8/float8_roofline.py

@@ -180,7 +180,7 @@ def get_gemm_times(
             scale_a = torch.ones(M, K // 32, device=device, dtype=torch.float8_e8m0fnu)
             scale_b = torch.ones(N, K // 32, device=device, dtype=torch.float8_e8m0fnu)
         else:
-            assert False, "TODO add cutlass mx gemm here"
+            assert False, f"unsupported {float8_recipe_name=} {mx_recipe_name=}"
 
         def do_matmul(A, B):
             return torch._scaled_mm(
@@ -233,6 +233,20 @@ def run(
     print(f"mx_recipe_name: {mx_recipe_name}")
     print(f"enable_fusion_modeling: {enable_fusion_modeling}")
 
+    assert mx_recipe_name in (
+        # real mxfp8_cublas recipe
+        "mxfp8_cublas",
+        # real mxfp8_cublas_rceil recipe
+        "mxfp8_cublas_rceil",
+        # modeling of what mxfp8 with 32x32 block size and without gemm
+        # operand layout restrictions would look like
+        "mxfp8_32x32_flexible_gemm_layout",
+        # modeling of what mxfp8 with 32x32 block size for weight
+        "mxfp8_32x32_weight",
+        # real mxfp4_cutlass recipe
+        "mxfp4_cutlass",
+    ), f"unsupported {mx_recipe_name=}"
+
     M, K, N = sympy.symbols("M K N")
 
     fp8_ovhd_time_sympy = get_float8_mem_sympy(
@@ -309,7 +323,11 @@ def run(
         rb_fp8_gemm_ratio = -1
 
         if do_benchmarks:
-            assert mx_recipe_name != "mxfp4_cutlass", "unsupported"
+            assert mx_recipe_name not in (
+                "mxfp4_cutlass",
+                "mxfp8_32x32_flexible_gemm_layout",
+                "mxfp8_32x32_weight",
+            ), f"do_benchmarks unsupported with {mx_recipe_name=}"
 
             # TODO(future): make the bf16 gemm times exactly match the e2e
             # benchmarks, there is a slight deviation, probably related to gemm

torchao/testing/training/roofline_utils.py

@@ -187,13 +187,53 @@ def get_tensor_memory_traffic_ovhd_s(
         else:
             assert False, "unsupported"
 
+    elif mx_recipe_name == "mxfp8_32x32_flexible_gemm_layout":
+        # modeling the following:
+        # 1. mxfp8 scaling with 32x32 everywhere, so the format makes sense
+        #    across dim0 and dim1
+        # 2. mxfp8 gemm with TN, NT, TT, NN formats supported (not in
+        #    PyTorch right now)
+        # x_bf16 = ...
+        # kernel 1:               x_bf16 -> x_mxfp8_dim0
+        if fuse_with_prev:
+            kernel_1_rw = 0 + BYTES_PER_EL_FLOAT8 * numel
+        else:
+            kernel_1_rw = BYTES_PER_EL_BF16 * numel + BYTES_PER_EL_FLOAT8 * numel
+        res_bytes = [kernel_1_rw]
+
+    elif mx_recipe_name == "mxfp8_32x32_weight":
+        # modeling the following:
+        # 1. mxfp8 scaling with 32x32 weights, so the format makes sense
+        #    across dim0 and dim1. input and grad_output still 1x32.
+
+        if tensor_role in ("input", "grad_output"):
+            # TODO(future): update all of the mx rooflines to just read once
+            # kernel 1: x_bf16 -> x_mxfp8_dim0
+            # kernel 2: x_bf16 -> x_mxfp8_dim1
+            if fuse_with_prev:
+                kernel_1_rw = 0 + BYTES_PER_EL_FLOAT8 * numel
+            else:
+                kernel_1_rw = BYTES_PER_EL_BF16 * numel + BYTES_PER_EL_FLOAT8 * numel
+            kernel_2_rw = BYTES_PER_EL_BF16 * numel + BYTES_PER_EL_FLOAT8 * numel
+
+        elif tensor_role == "weight":
+            # kernel 1: x_bf16 -> x_mxfp8_dim0
+            # kernel 2: x_mxfp8_dim0 -> x_mxfp8_dim1
+            kernel_1_rw = BYTES_PER_EL_BF16 * numel + BYTES_PER_EL_FLOAT8 * numel
+            kernel_2_rw = BYTES_PER_EL_FLOAT8 * numel * 2
+
+        else:
+            assert False, "unsupported"
+
+        res_bytes = [kernel_1_rw, kernel_2_rw]
+
     else:
         assert mx_recipe_name in (
             "mxfp8_emulated",
             "mxfp8_cublas",
             "mxfp8_cublas_rceil",
             "mxfp4_cutlass",
-        ), "unsupported"
+        ), f"unsupported {mx_recipe_name=}"
         # For now, assume that we can't profitably fuse kernel 1 and kernel 2
         # x_bf16 = ...
         # kernel 1:               x_bf16 -> x_mxfp8_dim0