Merge branch 'extract_outliers' into debug

bitsandbytes/autograd/_functions.py

@@ -203,30 +203,30 @@ class MatMul8bitLt(torch.autograd.Function):
                     # B in in 8-bit row-major, we can transform it back to 16-bit to extract outlier dimensions
                     # we also need to convert it to the turing/ampere format
                     state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
-                if state.threshold > 0.0 and coo_tensorA is not None and state.idx is None and state.CB is not None:
-                    # generate outlier index and subB
-                    outlier_idx = torch.unique(coo_tensorA.colidx).long()
-                    state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
-                    if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
-                        # do not use pool for 2nd FFN layer
-                        state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
-                    else:
-                        state.idx = outlier_idx
-                    state.subB = (state.CB[:, state.idx].float().t().contiguous()*(state.SCB/127)).half()
-
-                if state.idx is not None:
-                    # extract outliers
-                    CA[:, state.idx] = 0
-                    CAt[:, state.idx] = 0
-                    subA = A[:, state.idx]
-                else:
-                    subA = None
+                    #state.B = (state.CB.float()*(state.SCB.view(-1, 1)/127)).half()
+                #if state.threshold > 0.0 and coo_tensorA is not None and state.idx is None and state.CB is not None:
+                #    # generate outlier index and subB
+                #    outlier_idx = torch.unique(coo_tensorA.colidx).long()
+                #    state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
+                #    if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
+                #        # do not use pool for 2nd FFN layer
+                #        state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
+                #    else:
+                #        state.idx = outlier_idx
+                #    state.subB = (state.CB[:, state.idx].float().t().contiguous()*(state.SCB/127)).half()
+
+                #if state.idx is not None:
+                #    # extract outliers
+                #    CA[:, state.idx] = 0
+                #    CAt[:, state.idx] = 0
+                #    subA = A[:, state.idx]
+                #else:
+                #    subA = None
         else:
             if not state.has_fp16_weights and state.CxB is None:
                 state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
             subA = None
 
-        C32A, SA = F.transform(CA, 'col32')
 
         # 2. Quantize B
         if state.has_fp16_weights:
@@ -241,6 +241,23 @@ class MatMul8bitLt(torch.autograd.Function):
         else:
             has_grad = False
 
+        if coo_tensorA is not None and not state.has_fp16_weights:
+            # extract outliers
+
+            outlier_idx = torch.unique(coo_tensorA.colidx)
+            state.idx = outlier_idx
+            #state.outlier_pool.add_outliers(outlier_idx, A.shape[-1])
+            #if state.use_pool and state.outlier_pool.model_dim == A.shape[-1]:
+            #    # do not use pool for 2nd FFN layer
+            #    state.idx = state.outlier_pool.get_current_outlier_idx().to(A.device)
+            #else:
+            #    state.idx = outlier_idx
+            outliers = F.extract_outliers(state.CxB, state.SB, state.idx.int())
+            state.subB = (outliers*state.SCB.view(-1, 1)/127.0).t().contiguous().half()
+            CA[:, state.idx.long()] = 0
+            CAt[:, state.idx.long()] = 0
+            subA = A[:, state.idx.long()]
+
         shapeB = state.SB[0]
 
         if len(input_shape) == 3:
@@ -249,11 +266,12 @@ class MatMul8bitLt(torch.autograd.Function):
             output_shape = (input_shape[0], shapeB[0])
 
         # 3. Matmul
+        C32A, SA = F.transform(CA, 'col32')
         out32, Sout32 = F.igemmlt(C32A, state.CxB, SA, state.SB)
         output = F.mm_dequant(out32, Sout32, SCA, state.SCB)
 
         # 4. Mixed-precision decomposition matmul
-        if state.threshold > 0.0 and coo_tensorA is not None and subA is not None:
+        if coo_tensorA is not None and subA is not None:
             output += torch.matmul(subA, state.subB)
 
         # 5. Save state

bitsandbytes/functional.py

@@ -1435,3 +1435,29 @@ def dequant_min_max(xq, A, B, SA, SB, dtype=torch.half):
     x *= SA[1]/127
     x +=offset
     return x.to(dtype)
+
+def extract_outliers(A, SA, idx):
+    shapeA = SA[0]
+    formatA = SA[1]
+    assert formatA in ['col_turing', 'col_ampere']
+    assert A.device.type == 'cuda'
+
+    out = torch.zeros((shapeA[0], idx.numel()), dtype=torch.int8, device=A.device)
+
+    idx_size = ct.c_int32(idx.numel())
+    rows = ct.c_int32(shapeA[0])
+    cols = ct.c_int32(shapeA[1])
+    ptrA = get_ptr(A)
+    ptrIdx = get_ptr(idx)
+    ptrOut = get_ptr(out)
+
+    if formatA == 'col_turing':
+        lib.cextractOutliers_turing(ptrA, ptrIdx, ptrOut, idx_size, rows, cols)
+    elif formatA == 'col_ampere':
+        lib.cextractOutliers_ampere(ptrA, ptrIdx, ptrOut, idx_size, rows, cols)
+
+    return out
+
+
+
+

csrc/kernels.cu

@@ -2591,16 +2591,82 @@ __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 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 = A[offset];
+
+			int out_idx = (row*idx_size) + blockIdx.x;
+			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;
+		}
+	}
+} 
+
 //==============================================================
 //                   TEMPLATE DEFINITIONS
 //==============================================================
 
-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 * __restrict__ const 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 * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
-template __global__ void kspmm_coo_very_sparse_naive<half, 32, 16>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, half *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
-template __global__ void kspmm_coo_very_sparse_naive<signed char, 8, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
-template __global__ void kspmm_coo_very_sparse_naive<signed char, 16, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
-template __global__ void kspmm_coo_very_sparse_naive<signed char, 32, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float * __restrict__ const dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
+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);
+template __global__ void kspmm_coo_very_sparse_naive<half, 32, 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<signed char, 8, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
+template __global__ void kspmm_coo_very_sparse_naive<signed char, 16, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
+template __global__ void kspmm_coo_very_sparse_naive<signed char, 32, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz, int rowsA, int rowsB, int colsB);
 
 template __global__ void kTransformRowToFormat<256, 8, 32, 32*8, 0, COL32>(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols);
 template __global__ void kTransformRowToFormat<256, 8, 32, 32*8, 1, COL32>(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols);

csrc/kernels.cuh

@@ -118,6 +118,8 @@ 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 idx_size, int rowsA, int colsA, int tiledRowsA, int tiledColsA);
+
 #endif
 
 

csrc/ops.cu

@@ -618,10 +618,36 @@ template <typename T, int BITS> void spmm_coo_very_sparse_naive(int *max_count,
   CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
+
+template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int idx_size, int rows, int cols)
+{
+  int threads = 256;
+  // we load 128 column values per warp
+  int tiledCols = tiledCols = fill_up_to_nearest_multiple(cols, 32);
+  int tiledRows = 0;
+
+	int num_blocks = idx_size;
+
+  if(FORMAT == COL_TURING)
+  {
+      tiledRows = fill_up_to_nearest_multiple(rows, 8);
+  }
+  else if(FORMAT == COL_AMPERE)
+  {
+      tiledRows = fill_up_to_nearest_multiple(rows, 32);
+	}
+
+  kExtractOutliers<FORMAT><<<num_blocks, threads>>>(A, idx, out, idx_size, rows, cols, tiledRows, tiledCols);
+  CUDA_CHECK_RETURN(cudaPeekAtLastError());
+}
+
 //==============================================================
 //                   TEMPLATE DEFINITIONS
 //==============================================================
 
+template void extractOutliers<COL_TURING>(char * A, int *idx, char *out, int idx_size, int rows, int cols);
+template void extractOutliers<COL_AMPERE>(char * A, int *idx, char *out, int idx_size, int rows, int cols);
+
 template void spmm_coo_very_sparse_naive<half, 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_rows, int nnz, int rowsA, int rowsB, int colsB);
 template void spmm_coo_very_sparse_naive<signed char, 8>(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz_rows, int nnz, int rowsA, int rowsB, int colsB);
 

csrc/ops.cuh

@@ -174,4 +174,6 @@ void spmm_coo(cusparseHandle_t handle, int *A_rowidx, int *A_colidx, half *A_val
 
 template <typename T, int BITS> void spmm_coo_very_sparse_naive(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, T *B, half *out, float *dequant_stats, int nnz_rows, int nnz, int rowsA, int rowsB, int colsB);
 
+template <int FORMAT> void extractOutliers(char * A, int *idx, char *out, int idx_size, int rows, int cols);
+
 #endif

csrc/pythonInterface.c

@@ -105,6 +105,9 @@ void transform_row2turingT(char * A, char *out, int rows, int cols){ transformRo
 void transform_row2ampere(char * A, char *out, int rows, int cols){ transformRowToFormat<COL_AMPERE, 0>(A, out, rows, cols); }
 void transform_row2ampereT(char * A, char *out, int rows, int cols){ transformRowToFormat<COL_AMPERE, 1>(A, out, rows, cols); }
 
+void extractOutliers_turing(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers<COL_TURING>(A, idx, out, idx_size, rows, cols); }
+void extractOutliers_ampere(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers<COL_AMPERE>(A, idx, out, idx_size, rows, cols); }
+
  int igemmlt_turing_32(cublasLtHandle_t ltHandle, int m, int n, int k, const int8_t *A, const int8_t *B, void *C, float *row_scale, int lda, int ldb, int ldc)
 	{ return igemmlt<COL_TURING, 32, 0>(ltHandle, m, n, k, A, B, C, row_scale, lda, ldb, ldc); }
 
@@ -280,6 +283,9 @@ extern "C"
 	void cspmm_coo_very_sparse_naive_int8(int *max_count, int *max_idx, int *offset_rowidx, int *rowidx, int *colidx, half *values, signed char *B, half *out, float *dequant_stats, int nnz_rows, int nnz, int rowsA, int rowsB, int colsB)
 	{ spmm_coo_very_sparse_naive_int8(max_count, max_idx, offset_rowidx, rowidx, colidx, values, B, out, dequant_stats, nnz_rows, nnz, rowsA, rowsB, colsB); }
 
+	void cextractOutliers_turing(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers_turing(A, idx, out, idx_size, rows, cols); }
+	void cextractOutliers_ampere(char * A, int *idx, char *out, int idx_size, int rows, int cols){ extractOutliers_ampere(A, idx, out, idx_size, rows, cols); }
+
 #endif
 	void cquantize_blockwise_cpu_fp32(float *code, float *A, float *absmax, unsigned char *out, const int n){ quantize_cpu(code, A, absmax, out, n); }
 	void cdequantize_blockwise_cpu_fp32(float *code, unsigned char *A, float *absmax, float *out, const int n){ dequantize_cpu(code, A, absmax, out, n); }

deploy_from_slurm.sh

 #!/bin/bash
 BASE_PATH=$1
 
+echo "MAKE SURE LD_LIBRARY_PATH IS EMPTY!"
+echo $LD_LIBRARY_PATH
+
+if [[ ! -z "${LD_LIBRARY_PATH}" ]]; then
+  echo "Compilation unsuccessul!" 1>&2
+  exit 64
+fi
+
+
 module unload cuda
 module unload gcc
 
-#rm -rf dist build
-#make clean
-#make cleaneggs
-#export CUDA_HOME=
-#make cpuonly
-#
-#if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
-#  # Control will enter here if $DIRECTORY doesn't exist.
-#  echo "Compilation unsuccessul!" 1>&2
-#  exit 64
-#fi
-#CUDA_VERSION=cpu python -m build
-#python -m twine upload dist/* --verbose --repository testpypi
+rm -rf dist build
+make clean
+make cleaneggs
+export CUDA_HOME=
+make cpuonly
+
+if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
+  # Control will enter here if $DIRECTORY doesn't exist.
+  echo "Compilation unsuccessul!" 1>&2
+  exit 64
+fi
+CUDA_VERSION=cpu python -m build
+python -m twine upload dist/* --verbose --repository testpypi
 
 rm -rf dist build
 make clean
 make cleaneggs
 export CUDA_HOME=$BASE_PATH/cuda-11.0
-make cuda110
+make cuda110 
 
 if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
   # Control will enter here if $DIRECTORY doesn't exist.
@@ -102,20 +111,20 @@ fi
 CUDA_VERSION=115 python -m build
 python -m twine upload dist/* --verbose --repository testpypi
 
-#rm -rf dist build
-#make clean
-#make cleaneggs
-#export CUDA_HOME=$BASE_PATH/cuda-11.6
-#
-#make cuda11x
-#if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
-#  # Control will enter here if $DIRECTORY doesn't exist.
-#  echo "Compilation unsuccessul!" 1>&2
-#  exit 64
-#fi
-#CUDA_VERSION=116 python -m build
-#python -m twine upload dist/* --verbose --repository testpypi
-#
+rm -rf dist build
+make clean
+make cleaneggs
+export CUDA_HOME=$BASE_PATH/cuda-11.6
+
+make cuda11x
+if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
+  # Control will enter here if $DIRECTORY doesn't exist.
+  echo "Compilation unsuccessul!" 1>&2
+  exit 64
+fi
+CUDA_VERSION=116 python -m build
+python -m twine upload dist/* --verbose --repository testpypi
+
 rm -rf dist build
 make clean
 make cleaneggs
@@ -257,5 +266,4 @@ if [ ! -f "./bitsandbytes/libbitsandbytes.so" ]; then
   exit 64
 fi
 CUDA_VERSION=117-nomatmul python -m build
-python -m twine upload dist/* --verbose
 python -m twine upload dist/* --verbose --repository testpypi

tests/test_functional.py

@@ -1859,3 +1859,29 @@ def test_zp():
     print(err1, err2, err3, err4, err5, err6)
 
 
+
+def test_extract_outliers():
+    for i in range(k):
+        shapeA = (4096, 4096*4)
+        idx = torch.unique(torch.randint(0, shapeA[1], size=(10,)).int()).cuda()
+        #idx = torch.Tensor([0]).int().cuda()
+        A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8)
+        outliers1 = A[:, idx.long()]
+
+        CA, SA = F.transform(A, 'col_turing')
+
+        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)
+
+        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)