Refactor tests

tests/test_core.py

@@ -49,13 +49,10 @@ def construct(m: int, k: int, n: int) -> \
 
 def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k: int, n: int) -> \
         Tuple[int, Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
-    m = 0
+    alignment = get_m_alignment_for_contiguous_layout()
-    m_aligned = get_m_alignment_for_contiguous_layout()
+    group_ms = [int(expected_m_per_group * random.uniform(0.7, 1.3))]
-    group_m_list = []
+    m = sum([ceil_div(x, alignment) * alignment for x in group_ms])
-    for i in range(num_groups):
+
-        group_m = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
-        m += ceil_div(group_m, m_aligned) * m_aligned
-        group_m_list.append(group_m)
     x = torch.randn((m, k), device='cuda', dtype=torch.bfloat16)
     y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)    
     m_indices = torch.empty(m, device='cuda', dtype=torch.int32)
@@ -63,13 +60,14 @@ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k:
     ref_out = torch.randn((m, n), device='cuda', dtype=torch.bfloat16)
 
     start = 0
-    for i, group_m in enumerate(group_m_list):
+    for i, group_m in enumerate(group_ms):
         actual_end = start + group_m
-        aligned_end = start + ceil_div(group_m, m_aligned) * m_aligned
+        aligned_end = start + ceil_div(group_m, alignment) * alignment
         m_indices[start:actual_end] = i
         m_indices[actual_end:aligned_end] = -1
         ref_out[start:aligned_end] = x[start:aligned_end] @ y[i].t()
         start = aligned_end
+    ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_out)
 
     assert m % 4 == 0, f'TMA alignment error: {m}'
     x_fp8 = per_token_cast_to_fp8(x)
@@ -80,15 +78,15 @@ def construct_contiguous_grouped(num_groups: int, expected_m_per_group: int, k:
     return m, x_fp8, y_fp8, m_indices, out, ref_out
 
 
-def construct_masked_grouped(num_groups: int, m: int, k: int, n: int) -> \
+def construct_masked_grouped(num_groups: int, max_m: int, expected_m_per_group: int, k: int, n: int) -> \
-        Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor]:
+        Tuple[Tuple[torch.Tensor, torch.Tensor], Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]:
-    x = torch.randn((num_groups, m, k), device='cuda', dtype=torch.bfloat16)
+    x = torch.randn((num_groups, max_m, k), device='cuda', dtype=torch.bfloat16)
     y = torch.randn((num_groups, n, k), device='cuda', dtype=torch.bfloat16)
-    out = torch.empty((num_groups, m, n), device='cuda', dtype=torch.bfloat16)
+    out = torch.empty((num_groups, max_m, n), device='cuda', dtype=torch.bfloat16)
     ref_out = torch.einsum('gmk,gnk->gmn', x, y)
 
-    assert m % 4 == 0, f'TMA alignment error: {m}'
+    assert max_m % 4 == 0, f'TMA alignment error: {max_m}'
-    x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn), torch.empty((num_groups, m, k // 128), device='cuda', dtype=torch.float))
+    x_fp8 = (torch.empty_like(x, dtype=torch.float8_e4m3fn), torch.empty((num_groups, max_m, k // 128), device='cuda', dtype=torch.float))
     y_fp8 = (torch.empty_like(y, dtype=torch.float8_e4m3fn), torch.empty((num_groups, ceil_div(n, 128), k // 128), device='cuda', dtype=torch.float))
     for i in range(num_groups):
         x_fp8[0][i], x_fp8[1][i] = per_token_cast_to_fp8(x[i])
@@ -96,7 +94,12 @@ def construct_masked_grouped(num_groups: int, m: int, k: int, n: int) -> \
 
     # Transpose earlier so that the testing will not trigger transposing kernels
     x_fp8 = (x_fp8[0], get_col_major_tma_aligned_tensor(x_fp8[1]))
-    return x_fp8, y_fp8, out, ref_out
+
+    # Construct mask
+    masked_m = torch.empty((num_groups, ), device='cuda', dtype=torch.int)
+    for j in range(num_groups):
+        masked_m[j] = int(expected_m_per_group * random.uniform(0.7, 1.3))
+    return x_fp8, y_fp8, masked_m, out, ref_out
 
 
 def construct_wgrad(m: int, k: int, n: int) -> \
@@ -172,9 +175,6 @@ def test_gemm() -> None:
             diff = calc_diff(out, ref_out)
             assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}'
 
-            # Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2)
-            x_fp8, y_fp8, out, ref_out = construct(m, k, n)
-
             # noinspection PyShadowingNames
             def test_func():
                 deep_gemm.gemm_fp8_fp8_bf16_nt(x_fp8, y_fp8, out)
@@ -190,63 +190,50 @@ def test_m_grouped_gemm_contiguous() -> None:
     print('Testing grouped contiguous GEMM:')
 
     for num_groups, expected_m_per_group, k, n in ((4, 8192, 7168, 4096), (4, 8192, 2048, 7168), (8, 4096, 7168, 4096), (8, 4096, 2048, 7168)):
-        # TODO: make a stronger test
+        # NOTES: we should mask the unfilled part before calculating difference
         m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n)
         deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
         out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(out), out)
-        ref_out = torch.where((m_indices == -1).unsqueeze(1), torch.zeros_like(ref_out), ref_out)
         diff = calc_diff(out, ref_out)
         assert diff < 0.001, f'{m=}, {k=}, {n=}, {diff:.5f}'
 
-        # Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2)
-        m, x_fp8, y_fp8, m_indices, out, ref_out = construct_contiguous_grouped(num_groups, expected_m_per_group, k, n)
-
         # noinspection PyShadowingNames
         def test_func():
             deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_contiguous(x_fp8, y_fp8, out, m_indices)
 
         t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
-        sum_m = (m_indices != -1).sum().item()
+        valid_m = (m_indices != -1).sum().item()
         print(f' > Performance ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
-              f'throughput: {2 * sum_m * n * k / t / 1e12:4.0f} TFLOPS, '
+              f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
-              f'{(sum_m * k + num_groups * k * n + sum_m * n * 2) / 1e9 / t:4.0f} GB/s')
+              f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
     print()
 
 
 def test_m_grouped_gemm_masked() -> None:
     print('Testing grouped masked GEMM:')
 
-    m = 4096
+    max_m = 4096
     for num_groups, expected_m_per_group in ((1, 1024), (2, 512), (4, 256)):
         for k, n in ((7168, 4096), (2048, 7168), ):
             # Test correctness
             for i in range(10):
-                x_fp8, y_fp8, out, ref_out = construct_masked_grouped(num_groups, m, k, n)
+                x_fp8, y_fp8, masked_m, out, ref_out = construct_masked_grouped(num_groups, max_m, expected_m_per_group, k, n)
-                masked_m = torch.empty((num_groups, ), device='cuda', dtype=torch.int)
+                deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group)
-                for j in range(num_groups):
-                    masked_m[j] = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
-                expected_m = min(int(masked_m.float().mean()) + 1, m)
-                deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m)
                 for j in range(num_groups):
                     diff = calc_diff(out[j, :masked_m[j].item()], ref_out[j, :masked_m[j].item()])
-                    assert diff < 0.001, f'{m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
+                    assert diff < 0.001, f'{max_m=}, {k=}, {n=}, {j=}, masked_m={masked_m[j]}, {num_groups=}, {diff:.5f}'
-
-            # Construct new tensors only once to avoid L2 cache acceleration (creating them puts them in L2)
-            x_fp8, y_fp8, out, ref_out = construct_masked_grouped(num_groups, m, k, n)
-            for j in range(num_groups):
-                masked_m[j] = random.randint(int(expected_m_per_group * 0.7), int(expected_m_per_group * 1.3))
-            expected_m = min(int(masked_m.float().mean()) + 1, m)
-            sum_m = masked_m.sum().item()
 
             # noinspection PyShadowingNames
             def test_func():
-                deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m)
+                deep_gemm.m_grouped_gemm_fp8_fp8_bf16_nt_masked(x_fp8, y_fp8, out, masked_m, expected_m_per_group)
 
             # Test performance with fixed shapes
+            # noinspection PyUnboundLocalVariable
+            valid_m = masked_m.sum().item()
             t = bench_kineto(test_func, 'fp8_gemm', suppress_kineto_output=True)
             print(f' > Performance ({num_groups=}, expected_m_per_group={expected_m_per_group:4}, n={n:4}, k={k:4}): {t * 1e6:4.0f} us | '
-                  f'throughput: {2 * sum_m * n * k / t / 1e12:4.0f} TFLOPS, '
+                  f'throughput: {2 * valid_m * n * k / t / 1e12:4.0f} TFLOPS, '
-                  f'{(sum_m * k + num_groups * k * n + sum_m * n * 2) / 1e9 / t:4.0f} GB/s')
+                  f'{(valid_m * k + num_groups * k * n + valid_m * n * 2) / 1e9 / t:4.0f} GB/s')
     print()