[not for land] hook up MX to CUDA 12.8 cuBLAS MX gemm

Summary:

Requires https://github.com/pytorch/pytorch/pull/145562/files

None of this is for land - just testing for now as we work on a long
term support plan.

Test Plan:

Reviewers:

Subscribers:

Tasks:

Tags:

ghstack-source-id: 946b39a28673d9a9b7945c4ad51c638bfd3963cc
ghstack-comment-id: 2616580142
Pull Request resolved: #1625

Author: vkuzo

benchmarks/float8/bench_matmul.py

@@ -4,6 +4,7 @@
 # This source code is licensed under the BSD 3-Clause license found in the
 # LICENSE file in the root directory of this source tree.
 import itertools
+from enum import IntEnum
 from typing import Optional
 
 import fire
@@ -26,14 +27,44 @@
 h100_peak_flops_fp16_tc = 989e12
 h100_peak_tops_float8_tc = 1979e12
 
-dtype_to_peak_tops = {
+# HGX B20 specs: https://www.nvidia.com/en-us/data-center/hgx/
+# note: divided numbers from ^ by 2 to undo the effects of sparsity
+# TODO(this PR): I'm achieving 5% of peak TFLOPS with bf16 and float8,
+# something seems funky
+b200_peak_flops_float32 = 600e12
+b200_peak_flops_fp16_tc = 18e15
+b200_peak_tops_float8_tc = 36e15
+b200_peak_tops_float4_tc = 72e15
+
+dtype_to_peak_tops_h100 = {
     torch.float32: h100_peak_flops_float32,
     torch.float16: h100_peak_flops_fp16_tc,
     torch.bfloat16: h100_peak_flops_fp16_tc,
     torch.float8_e4m3fn: h100_peak_tops_float8_tc,
     torch.float8_e5m2: h100_peak_tops_float8_tc,
 }
 
+dtype_to_peak_tops_b200 = {
+    torch.float32: b200_peak_flops_float32,
+    torch.float16: b200_peak_flops_fp16_tc,
+    torch.bfloat16: b200_peak_flops_fp16_tc,
+    torch.float8_e4m3fn: b200_peak_tops_float8_tc,
+    torch.float8_e5m2: b200_peak_tops_float8_tc,
+    # TODO float4
+}
+
+# TODO(this PR): switch automatically by detected hardware type
+# TODO(this PR): fp4 is currently using fp8's peak tops below, fix it
+dtype_to_peak_tops = dtype_to_peak_tops_b200
+
+
+# not for land, matching https://www.internalfb.com/phabricator/paste/view/P1717686991
+class DataType(IntEnum):
+    DEFAULT = 0
+    E8M0 = 1
+    FP4 = 2
+    UFP8 = 3
+
 
 def benchmark_fn_in_sec(f, *args, **kwargs):
     # Manual warmup
@@ -75,6 +106,7 @@ def run(
     N: Optional[int] = None,
     use_gpu_kernel_time: bool = False,
     scaling_granularity: str = "tensorwise",
+    blockwise_dtype: Optional[str] = None,
 ):
     device = "cuda"
 
@@ -85,15 +117,17 @@ def run(
         "K",
         "N",
         "ref_time_s",
-        "fp8_time_s",
-        "fp8_speedup",
+        "lowp_time_s",
+        "lowp_speedup",
     )
     results = []
 
     dtype = torch.bfloat16
     name_to_shapes = get_name_to_shapes_iter(shape_gen_name, M, K, N)
     fast_accum_vals = [True, False]
-    scaling_granularity = ScalingGranularity(scaling_granularity)
+    # Note: blockwise not in enum because blockwise is in prototype
+    if scaling_granularity != "blockwise":
+        scaling_granularity = ScalingGranularity(scaling_granularity)
 
     for idx, (fast_accum, (name, (M, K, N))) in enumerate(
         itertools.product(fast_accum_vals, name_to_shapes)
@@ -119,28 +153,97 @@ def run(
         # raw float8 matmul (upper bound for what we can achive in eager mode)
         # TODO(future): add e5m2
         d1, d2, d3 = torch.float8_e4m3fn, torch.float8_e4m3fn, dtype
-        A = torch.zeros(M, K, device=device, dtype=d1)
-        B = torch.zeros(K, N, device=device, dtype=d2).t().contiguous().t()
+        A = torch.randn(M, K, device=device).to(d1)
+        B = torch.randn(K, N, device=device).to(d2).t().contiguous().t()
         if scaling_granularity == ScalingGranularity.TENSORWISE:
             scale_a = torch.tensor([1.0], device=device)
             scale_b = torch.tensor([1.0], device=device)
-        else:
-            assert scaling_granularity == ScalingGranularity.AXISWISE, "unsupported"
+        elif scaling_granularity == ScalingGranularity.AXISWISE:
             scale_a = torch.ones(M, 1, device=device)
             scale_b = torch.ones(1, N, device=device)
+        elif scaling_granularity == "blockwise" and blockwise_dtype == "float8_e4m3":
+            # TODO(this PR): also block size 16
+            BLOCK_SIZE = 32
+            A = torch.randint(128, (M, K), device=device, dtype=torch.uint8).view(
+                torch.float8_e4m3fn
+            )
+            B = (
+                torch.randint(128, (N, K), device=device, dtype=torch.uint8)
+                .view(torch.float8_e4m3fn)
+                .t()
+            )
+            scale_a = torch.randint(
+                128, (M, K // BLOCK_SIZE), dtype=torch.uint8, device="cuda"
+            )
+            scale_b = torch.randint(
+                128, (N, K // BLOCK_SIZE), dtype=torch.uint8, device="cuda"
+            ).t()
+        elif scaling_granularity == "blockwise" and blockwise_dtype == "float4":
+            # TODO(this PR): also block size 16
+            BLOCK_SIZE = 16
+            A = torch.randint(128, (M, K // 2), device=device, dtype=torch.uint8).view(
+                torch.float8_e4m3fn
+            )
+            B = (
+                torch.randint(128, (N, K // 2), device=device, dtype=torch.uint8)
+                .view(torch.float8_e4m3fn)
+                .t()
+            )
+            scale_a = torch.randint(
+                128, (M, K // BLOCK_SIZE), dtype=torch.uint8, device="cuda"
+            )
+            scale_b = torch.randint(
+                128, (N, K // BLOCK_SIZE), dtype=torch.uint8, device="cuda"
+            ).t()
+        else:
+            raise AssertionError(f"unsupported granularity {scaling_granularity}")
 
         def do_matmul(A, B):
             nonlocal scale_a
             nonlocal scale_b
-            return torch._scaled_mm(
-                A, B, scale_a, scale_b, out_dtype=d3, use_fast_accum=fast_accum
-            )
+
+            if scaling_granularity == "blockwise" and blockwise_dtype == "float8_e4m3":
+                return torch._scaled_mm(
+                    A,
+                    B,
+                    scale_a,
+                    scale_b,
+                    bias=None,
+                    scale_result=None,
+                    out_dtype=d3,
+                    use_fast_accum=fast_accum,
+                    a_dtype=None,  # inferred from A
+                    b_dtype=None,  # inferred from B
+                    scale_dtype=DataType.E8M0,
+                )
+            elif scaling_granularity == "blockwise" and blockwise_dtype == "float4":
+                return torch._scaled_mm(
+                    A,
+                    B,
+                    scale_a,
+                    scale_b,
+                    bias=None,
+                    scale_result=None,
+                    out_dtype=d3,
+                    use_fast_accum=fast_accum,
+                    a_dtype=DataType.FP4,
+                    b_dtype=DataType.FP4,
+                    scale_dtype=DataType.E8M0,
+                )
+
+            else:
+                return torch._scaled_mm(
+                    A, B, scale_a, scale_b, out_dtype=d3, use_fast_accum=fast_accum
+                )
+
+        # test
+        # res = do_matmul(A, B)
 
         fp8_time_sec, fp8_tops_sec, fp8_pct_top_peak = do_benchmarks(
             tops, dtype_to_peak_tops[d1], use_gpu_kernel_time, do_matmul, A, B
         )
         print(
-            f"fp8 time_sec {fp8_time_sec:.2E}, tops/sec {fp8_tops_sec:.2E}, pct_peak {fp8_pct_top_peak:.3f}"
+            f"lowp time_sec {fp8_time_sec:.2E}, tops/sec {fp8_tops_sec:.2E}, pct_peak {fp8_pct_top_peak:.3f}"
         )
 
         del A, B, scale_a, scale_b