[1/x] mx roofline: make the script work on NVIDIA B200

Summary:

Makes the roofline estimation script work on NVIDIA B200:
1. skip rowwise scaling (does not work yet on B200)
2. add proper values for peak tensor core flops and peak memory
   bandwidth for B200.

This script needs a lot of future improvements to be useful, will do in a stack.

Test Plan:

```
python benchmarks/float8/float8_roofline.py ~/local/tmp/20250225_test.csv
```

Reviewers:

Subscribers:

Tasks:

Tags:

ghstack-source-id: 3c9b1cd
ghstack-comment-id: 2683396978
Pull Request resolved: #1778

Author: vkuzo

benchmarks/float8/float8_roofline.py

@@ -65,6 +65,7 @@
     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):
@@ -154,10 +155,13 @@ def do_matmul(A, B):
 
     f8_time_s = get_gpu_kernel_gemm_time_s(do_matmul, A, B)
 
-    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)
+    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:
@@ -298,17 +302,24 @@ def run(
         bf16_time_actual_s = get_gpu_kernel_time(m_bf16, x)
 
         # get the float8 dynamic scaling gpu kernel time
+
         torch._dynamo.reset()
         m_fp8_dyn = convert_to_float8_training(copy.deepcopy(m_orig))
         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
-        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)
+        # 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

benchmarks/float8/utils.py

@@ -81,6 +81,8 @@ def profiler_output_to_filtered_time_by_kernel_name(
             continue
         elif e.key == "cudaDeviceSynchronize":
             continue
+        elif e.key == "Activity Buffer Request":
+            continue
 
         kernel_name_to_gpu_time_us[e.key] = e.self_device_time_total
     return kernel_name_to_gpu_time_us

torchao/float8/roofline_utils.py

@@ -9,19 +9,43 @@
 BYTES_PER_EL_FLOAT8 = 1
 BYTES_PER_EL_BF16 = 2
 
-# https://www.nvidia.com/en-us/data-center/h100/, divide by 2 because no sparsity
-H100_BF16_PEAK_TOPS = 989e12
-H100_FP8_PEAK_TOPS = 1979e12
+gpu_name_to_specs = {
+    "NVIDIA H100": {
+        # https://www.nvidia.com/en-us/data-center/h100/, divide by 2 because no sparsity
+        "bf16_peak_tops": 989e12,
+        "fp8_peak_tops": 1979e12,
+        # 2.4 TB per second, custom to Meta's H100 variant
+        "peak_mem_bw_bytes_sec": 2.4e12,
+        # based on quick experimental observation with sample large inputs
+        "pct_achievable_gemm_tops": 0.6,
+        # based on previous experience looking at pointwise triton kernels with large inputs,
+        # which would hit about 2.2k GBPS on Meta's H100 variant
+        "pct_achievable_mem_bw": 0.92,
+    },
+    "NVIDIA B200": {
+        # https://resources.nvidia.com/en-us-blackwell-architecture, page 19,
+        # divide by 2 because no sparsity
+        "bf16_peak_tops": 2.25e15,
+        "fp8_peak_tops": 4.5e15,
+        "fp4_peak_tops": 9.0e15,
+        # https://resources.nvidia.com/en-us-blackwell-architecture, page 20
+        # 8.0 TB per second
+        "peak_mem_bw_bytes_sec": 8.0e12,
+        # for now, copy over from H100
+        # TODO(future): measure once we have the hardware
+        "pct_achievable_gemm_tops": 0.6,
+        # for now, copy over from H100
+        # TODO(future): measure once we have the hardware
+        "pct_achievable_mem_bw": 0.92,
+    },
+    # TODO(future): more GPU names
+}
+
+
+def get_specs():
+    gpu_name = torch.cuda.get_device_name(0)
+    return gpu_name_to_specs[gpu_name]
 
-# 2.4 TB per second, custom to Meta's H100 variant
-H100_PEAK_MEM_BW_BYTES_SEC = 2.4e12
-
-# based on quick experimental observation with sample large inputs
-H100_PCT_ACHIEVABLE_GEMM_TOPS = 0.6
-
-# based on previous experience looking at pointwise triton kernels with large inputs,
-# which would hit about 2.2k GBPS on Meta's H100 variant
-H100_PCT_ACHIEVABLE_MEM_BW = 0.92
 
 # Source: run a triton kernel with a single element read/write on an H100 and
 # measure GPU time from the trace
@@ -65,12 +89,13 @@ def get_tensor_memory_traffic_bytes(
 
 
 def get_gemm_time_sympy(M, K, N, dtype):
+    specs = get_specs()
     gemm_ops = 2 * M * K * N + 2 * M * N * K + 2 * K * M * N
     if dtype is torch.bfloat16:
-        peak_tops = H100_BF16_PEAK_TOPS
+        peak_tops = specs["bf16_peak_tops"]
     elif dtype in (torch.float8_e4m3fn, torch.float8_e5m2):
-        peak_tops = H100_FP8_PEAK_TOPS
-    gemm_time_s = gemm_ops / peak_tops / H100_PCT_ACHIEVABLE_GEMM_TOPS
+        peak_tops = specs["fp8_peak_tops"]
+    gemm_time_s = gemm_ops / peak_tops / specs["pct_achievable_gemm_tops"]
     return gemm_time_s
 
 
@@ -87,6 +112,8 @@ def get_float8_mem_sympy(
     assert scaling_type_weight in ("dynamic",), "unsupported"
     assert scaling_type_grad_output in ("dynamic",), "unsupported"
 
+    specs = get_specs()
+
     # there are three gemms in the fwd/bwd of a linear:
     #
     # input @ weight_t = output
@@ -148,7 +175,7 @@ def get_float8_mem_sympy(
     )
     fp8_total_mem = fwd_fp8_total_mem + bwd_fp8_total_mem
     fp8_mem_time_s = (
-        fp8_total_mem / H100_PEAK_MEM_BW_BYTES_SEC / H100_PCT_ACHIEVABLE_MEM_BW
+        fp8_total_mem / specs["peak_mem_bw_bytes_sec"] / specs["pct_achievable_mem_bw"]
     )
 
     # Adjust final estimate for small kernel launches