Added col_ampere outlier extraction kernel.

csrc/kernels.cu

@@ -2626,28 +2626,32 @@ template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *
 
 			offset += tile_offset_rows + tile_offset_cols;
 
-
-			char val = 0;
-			//printf("(%i (%i %i) (%i %i))\n", offset, tile_offset_rows, tile_offset_cols, row, local_colidx);
-			if(offset > tiledColsA*tiledRowsA)
-				printf("(%i (%i %i) (%i %i)\n", offset, tile_offset_rows, tile_offset_cols, row, local_colidx);
-		  else
-				val = A[offset];
+			char val = A[offset];
 
 			int out_idx = (row*idx_size) + blockIdx.x;
-
-			//if(out_idx > colsA*idx_size)
-			if(val != 0)
-			{
-				//printf("(%i %i) = (%i) = %i\n", row, local_colidx, out_idx, (int) val);
-				out[out_idx] = val;
-			}
-		  else
-			{
-				out[out_idx] = val;
-			}
+			out[out_idx] = val;
 		}
+	}
+	else if(FORMAT == COL_AMPERE)
+	{
 
+		for(int row = threadIdx.x; row < rowsA; row+= blockDim.x)
+		{
+			// we got 32x32 tiles and we use the magic equation from the cublasLt doc to get the element
+			// within each tile.
+			int offset_per_col_tile = ((rowsA+31)/32)*32*32;
+			int tile_offset_rows = (row/32)*32*32;
+			int tile_offset_cols = (local_colidx/32)*offset_per_col_tile;
+			int subtile_col_idx = local_colidx%32;
+			int subtile_row_idx = row % 32;
+			// this magic is taken from the cublasLt doc (search for COL32)
+			int offset = (((subtile_row_idx%8)/2*4+subtile_row_idx/8)*2+subtile_row_idx%2)*32+subtile_col_idx;
+			offset += tile_offset_cols + tile_offset_rows;
+
+			char val = A[offset];
+			int out_idx = (row*idx_size) + blockIdx.x;
+			out[out_idx] = val;
+		}
 	}
 } 
 

tests/test_functional.py

@@ -1859,9 +1859,9 @@ def test_zp():
 
 def test_extract_outliers():
     for i in range(k):
-        shapeA = (4096, 4*4096)
+        shapeA = (4096, 4096*4)
         idx = torch.unique(torch.randint(0, shapeA[1], size=(10,)).int()).cuda()
-        #idx = torch.Tensor([32]).int().cuda()
+        #idx = torch.Tensor([0]).int().cuda()
         A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8)
         outliers1 = A[:, idx.long()]
 
@@ -1872,7 +1872,13 @@ def test_extract_outliers():
         assert outliers2.shape[0] == shapeA[0]
         assert outliers2.shape[1] == idx.numel()
 
-        #print(outliers1)
-        #print(outliers2)
+        torch.testing.assert_allclose(outliers1, outliers2)
+
+        CA, SA = F.transform(A, 'col_ampere')
+
+        outliers2 = F.extract_outliers(CA, SA, idx)
+
+        assert outliers2.shape[0] == shapeA[0]
+        assert outliers2.shape[1] == idx.numel()
 
         torch.testing.assert_allclose(outliers1, outliers2)