roofline estimation: delete axiswise scaling, for now (#1782)

* Update

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* Update

[ghstack-poisoned]

* Update

[ghstack-poisoned]

Author: vkuzo

benchmarks/float8/float8_roofline.py

@@ -58,14 +58,12 @@
 )
 
 from torchao.float8 import (
-    Float8LinearConfig,
     convert_to_float8_training,
 )
 from torchao.testing.float8.roofline_utils import (
     get_float8_mem_sympy,
     get_gemm_time_sympy,
 )
-from torchao.utils import is_sm_at_least_90, is_sm_at_least_100
 
 
 class LNLinearSigmoid(torch.nn.Module):
@@ -155,21 +153,13 @@ def do_matmul(A, B):
 
     f8_time_s = get_gpu_kernel_gemm_time_s(do_matmul, A, B)
 
-    if is_sm_at_least_90() and (not is_sm_at_least_100()):
-        scale_a = torch.ones(M, 1, device=device)
-        scale_b = torch.ones(1, N, device=device)
-        fast_accum = True  # for axiswise
-        f8_axs_time_s = get_gpu_kernel_gemm_time_s(do_matmul, A, B)
-    else:
-        f8_axs_time_s = -1.0
-
     # save to cache if needed
     if cache_filename is not None:
-        cache[key] = [bf16_time_s, f8_time_s, f8_axs_time_s]
+        cache[key] = [bf16_time_s, f8_time_s]
         with open(cache_filename, "w") as f:
             json.dump(cache, f)
 
-    return bf16_time_s, f8_time_s, f8_axs_time_s
+    return bf16_time_s, f8_time_s
 
 
 def run(
@@ -229,18 +219,13 @@ def run(
         # gemm microbenchmarks
         "bf16_gemm_s",
         "fp8_gemm_s",
-        "fp8_axs_gemm_time_s",
         # roofline memory overhead estimates
-        "fp8_oh_dyn_limit",
-        "fp8_oh_dyn_nolimit",
+        "fp8_oh_estimated",
+        "fp8_oh_ideal",
         # actual e2e measurements
         "bf16_s",
         "fp8_dyn_s",
-        "fp8_dyn_axs_s",
-        # 'fp8_lw_s',
         "fp8_dyn_sp",
-        "fp8_dyn_axs_sp",
-        # 'fp8_lw_sp',
     ]
     results = []
 
@@ -251,18 +236,17 @@ def run(
             break
 
         if gemm_time_strategy == "benchmarks":
-            bf16_g1, f8_g1, f8_g1_axs = get_gemm_times(
+            bf16_g1, f8_g1 = get_gemm_times(
                 M_val, K_val, N_val, True, gemm_cache_filename
             )
-            bf16_g2, f8_g2, f8_g2_axs = get_gemm_times(
+            bf16_g2, f8_g2 = get_gemm_times(
                 M_val, N_val, K_val, False, gemm_cache_filename
             )
-            bf16_g3, f8_g3, f8_g3_axs = get_gemm_times(
+            bf16_g3, f8_g3 = get_gemm_times(
                 K_val, M_val, N_val, False, gemm_cache_filename
             )
             bf16_time_val = bf16_g1 + bf16_g2 + bf16_g3
             fp8_gemm_time_s = f8_g1 + f8_g2 + f8_g3
-            fp8_axs_gemm_time_s = f8_g1_axs + f8_g2_axs + f8_g3_axs
         else:
             assert gemm_time_strategy == "roofline", "unsupported"
             bf16_time_val = (
@@ -271,8 +255,6 @@ def run(
             fp8_gemm_time_s = (
                 fp8_gemm_time_sympy.subs(M, M_val).subs(K, K_val).subs(N, N_val)
             )
-            # for now, assume axiswise gemm is similar to tensorwise
-            fp8_axs_gemm_time_s = fp8_gemm_time_s
 
         fp8_mem_time_dyn_limit_s = (
             fp8_mem_time_sympy_dyn_limit.subs(M, M_val).subs(K, K_val).subs(N, N_val)
@@ -299,28 +281,6 @@ def run(
         m_fp8_dyn = torch.compile(m_fp8_dyn)
         fp8_dyn_time_actual_s = get_gpu_kernel_time(m_fp8_dyn, x)
 
-        # get the float8 dynamic axiswise scaling gpu kernel time, if supported
-        # on current hardware
-        if is_sm_at_least_90() and (not is_sm_at_least_100()):
-            torch._dynamo.reset()
-            config = Float8LinearConfig.from_recipe_name("rowwise")
-            m_fp8_dyn_axs = convert_to_float8_training(
-                copy.deepcopy(m_orig), config=config
-            )
-            m_fp8_dyn_axs = torch.compile(m_fp8_dyn_axs)
-            fp8_dyn_axs_time_actual_s = get_gpu_kernel_time(m_fp8_dyn_axs, x)
-        else:
-            fp8_dyn_axs_time_actual_s = -1.0
-
-        # get the lw recipe scaling gpu kernel time
-        # TODO(future PR): enable below once basic performance issues
-        # are fixed
-        # torch._dynamo.reset()
-        # config = Float8LinearConfig.from_recipe_name("rowwise_with_gw_hp")
-        # m_fp8_lw = convert_to_float8_training(m_orig, config=config)
-        # m_fp8_lw = torch.compile(m_fp8_lw)
-        # fp8_lw_time_actual_s = get_gpu_kernel_time(m_fp8_lw, x)
-
         results.append(
             [
                 M_val,
@@ -329,18 +289,13 @@ def run(
                 # gemm microbenchmarks
                 bf16_time_val,
                 fp8_gemm_time_s,
-                fp8_axs_gemm_time_s,
                 # roofline overhead estimates
                 fp8_mem_time_dyn_limit_s,
                 fp8_mem_time_dyn_nolimit_s,
                 # e2e numbers
                 bf16_time_actual_s,
                 fp8_dyn_time_actual_s,
-                fp8_dyn_axs_time_actual_s,
-                # fp8_lw_time_actual_s,
                 bf16_time_actual_s / fp8_dyn_time_actual_s,
-                bf16_time_actual_s / fp8_dyn_axs_time_actual_s,
-                # bf16_time_actual_s / fp8_lw_time_actual_s,
             ]
         )