Working outlier extraction for Turing.

4 files changed, 74 insertions, 17 deletions

diff --git a/csrc/kernels.cu b/csrc/kernels.cu
index 78170d0..bb36d9b 100644
--- a/csrc/kernels.cu
+++ b/csrc/kernels.cu
@@ -2592,16 +2592,71 @@ __global__ void kspmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *o
   } 
 }
 
-template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int rowsA, int colsA, int tiledRowsA, int tiledColsA)
+template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA)
 {  
+	int local_colidx = idx[blockIdx.x];
+
+	if(FORMAT==COL_TURING)
+	{
+		// TURING FORMAT:
+		// 8*32 tiles with 4*4 subtiles
+		// the 8*32 subtile has first all 4*4 subtiles of even rows (max 4*4*8 = 128 elements)
+		// the subsequent 4*4 subtiles are for all odd rows if some rows columns are empty the values are zero
+		// the tile repeats again after the 8*32 tile in a major column order, meaning: (next 8 rows are A[8:16, 0:32])
+		// the next tile is the next 8 rows for the same 32 columns. Once all rows are finished, the column
+		// index increases by 32
+		// columns are grouped in increments of 4, meaning that one has the following rows and columns
+		// rows: [0 0 0 0, 2 2 2 2, 4 4 4 4, 6 6 6 6, 0 0 0 0 ...]
+		// cols: [0 1 2 3, 0 1 2 4, 0 1 2 3, 0 1 2 3, 4 5 6 7 ...]
+
+		// each thread reads 1 element = 1 row
+		for(int row = threadIdx.x; row < rowsA; row+= blockDim.x)
+		{
+			int offset_per_col_tile = ((rowsA+7)/8)*32*8;
+			int tile_offset_rows = (row/8)*32*8;
+			int tile_offset_cols = (local_colidx/32)*offset_per_col_tile;
+			int offset = 0;
+			int subtile_col_idx = local_colidx%32;
+			int subtile_row_idx = row % 8;
+			if(row % 2 == 1)
+				offset += 128 + (subtile_col_idx/4)*16 + (subtile_col_idx%4) + ((subtile_row_idx-1)*2);
+			else
+				// even
+				offset += 0   + (subtile_col_idx/4)*16 + (subtile_col_idx%4) + (subtile_row_idx*2);
+
+			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];
+
+			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;
+			}
+		}
+
+	}
 } 
 
 //==============================================================
 //                   TEMPLATE DEFINITIONS
 //==============================================================
 
-template __global__ void kExtractOutliers<COL_TURING>(char *A, int *idx, char *out, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
-template __global__ void kExtractOutliers<COL_AMPERE>(char *A, int *idx, char *out, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
+template __global__ void kExtractOutliers<COL_TURING>(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
+template __global__ void kExtractOutliers<COL_AMPERE>(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
 
 template __global__ void kspmm_coo_very_sparse_naive<half, 8, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
 template __global__ void kspmm_coo_very_sparse_naive<half, 16, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
diff --git a/csrc/kernels.cuh b/csrc/kernels.cuh
index ec2068e..eda5ba0 100644
--- a/csrc/kernels.cuh
+++ b/csrc/kernels.cuh
@@ -118,7 +118,7 @@ template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int S
 
 template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int TRANSPOSE, int FORMAT> __global__ void kTransformRowToFormat(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols);
 
-template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
+template <int FORMAT> __global__ void kExtractOutliers(char *A, int *idx, char *out, int idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
 
 #endif
 
diff --git a/csrc/ops.cu b/csrc/ops.cu
index fe2d7fe..e6227ae 100644
--- a/csrc/ops.cu
+++ b/csrc/ops.cu
@@ -586,8 +586,7 @@ template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int id
   int tiledCols = tiledCols = fill_up_to_nearest_multiple(cols, 32);
   int tiledRows = 0;
 
-	int elements = idx_size*cols; // matrix A is transposed, so we extract columns
-	int num_blocks = (elements+threads-1)/threads;
+	int num_blocks = idx_size;
 
   if(FORMAT == COL_TURING)
   {
@@ -598,7 +597,7 @@ template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int id
       tiledRows = fill_up_to_nearest_multiple(rows, 32);
 	}
 
-  kExtractOutliers<FORMAT><<<num_blocks, threads>>>(A, idx, out, rows, cols, tiledRows, tiledCols);
+  kExtractOutliers<FORMAT><<<num_blocks, threads>>>(A, idx, out, idx_size, rows, cols, tiledRows, tiledCols);
   CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
diff --git a/tests/test_functional.py b/tests/test_functional.py
index b508367..4d06447 100644
--- a/tests/test_functional.py
+++ b/tests/test_functional.py
@@ -1858,18 +1858,21 @@ def test_zp():
 
 
 def test_extract_outliers():
-    shapeA = (128, 128)
-    idx = torch.randint(0, shapeA[1], size=(10,)).int()
-    A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8)
-    outliers1 = A[:, idx.long()]
-
-    CA, SA = F.transform(A, 'col_turing')
+    for i in range(k):
+        shapeA = (4096, 4*4096)
+        idx = torch.unique(torch.randint(0, shapeA[1], size=(10,)).int()).cuda()
+        #idx = torch.Tensor([32]).int().cuda()
+        A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8)
+        outliers1 = A[:, idx.long()]
 
-    outliers2 = F.extract_outliers(CA, SA, idx)
+        CA, SA = F.transform(A, 'col_turing')
 
-    assert outliers2.shape[0] == shapeA[0]
-    assert outliers2.shape[1] == idx.numel()
+        outliers2 = F.extract_outliers(CA, SA, idx)
 
+        assert outliers2.shape[0] == shapeA[0]
+        assert outliers2.shape[1] == idx.numel()
 
+        #print(outliers1)
+        #print(outliers2)
 
-    torch.testing.assert_allclose(outliers1, outliers2)
+        torch.testing.assert_allclose(outliers1, outliers2)