2024-10-07 nightly release (e7f89e4)

pytorch · Oct 7, 2024 · 03f302c · 03f302c
1 parent ed75e6c
commit 03f302c
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Showing 2 changed files with 9 additions and 6 deletions.
diff --git a/fbgemm_gpu/experimental/gemm/test/fp8_gemm_test.py b/fbgemm_gpu/experimental/gemm/test/fp8_gemm_test.py
@@ -31,14 +31,13 @@ def setUp(self) -> None:
 
     def test_quantize_fp8_row(self) -> None:
         def _test_quantize_fp8_row(
-            shape: Tuple[int, int],
+            shape: Tuple[int, ...],
             use_triton: bool,
             device: torch.device,
             output_device: Optional[torch.device] = None,
             use_scale_ub: bool = False,
         ) -> None:
-            M, K = shape
-            a = torch.randn(M, K, dtype=torch.bfloat16, device=device)
+            a = torch.randn(shape, dtype=torch.bfloat16, device=device)
 
             scale_ub = (
                 torch.tensor([1200], dtype=torch.float, device=device)
@@ -52,7 +51,8 @@ def _test_quantize_fp8_row(
 
             # Undo scaling.
             a_torch = a_fp8.to(torch.bfloat16)
-            a_torch *= a_scale[:, None]
+            broadcast_shape = list(a_torch.shape[:-1]) + [-1]
+            a_torch *= a_scale.view(broadcast_shape)
 
             self.assertTrue(
                 torch.allclose(
@@ -61,6 +61,8 @@ def _test_quantize_fp8_row(
             )
 
         _test_quantize_fp8_row((2, 3), True, torch.device("cuda"))
+        # Test with batched input.
+        _test_quantize_fp8_row((4, 2, 3), True, torch.device("cuda"))
         _test_quantize_fp8_row((2, 3), True, torch.device("cuda"), use_scale_ub=True)
         _test_quantize_fp8_row((2, 3), False, torch.device("cpu"), torch.device("cuda"))
         _test_quantize_fp8_row(

diff --git a/fbgemm_gpu/experimental/gemm/triton_gemm/fp8_gemm.py b/fbgemm_gpu/experimental/gemm/triton_gemm/fp8_gemm.py
@@ -2128,9 +2128,10 @@ def quantize_fp8_row_meta(
     """Shape function for torch compile."""
     if output_device is None:
         output_device = a.device
-    M, K = a.shape
+    # Flatten to 2D since each row of each potential batch gets a scale.
+    M = a.view(-1, a.shape[-1]).shape[0]
     dtype = get_fp8_constants()[0]
-    fake_out = torch.empty((M, K), device=output_device, dtype=dtype)
+    fake_out = torch.empty(a.shape, device=output_device, dtype=dtype)
     fake_scale = torch.empty((M), device=output_device, dtype=torch.float32)
     return fake_out, fake_scale