Removed storage() from get_ptr; added boilerplate for bias dequant_mm.

bitsandbytes/functional.py

@@ -218,7 +218,7 @@ def get_ptr(A: Tensor) -> ct.c_void_p:
     if A is None:
         return None
     else:
-        return ct.c_void_p(A.data.storage().data_ptr())
+        return ct.c_void_p(A.data.data_ptr())
 
 
 def pre_call(device):
@@ -1407,8 +1407,10 @@ def mm_dequant(
     out=None,
     new_row_stats=None,
     new_col_stats=None,
+    bias=None
 ):
     assert A.dtype == torch.int32
+    if bias is not None: assert bias.dtype == torch.float16
     out_shape = quant_state[0]
     if len(out_shape) == 3:
         out_shape = (out_shape[0] * out_shape[1], out_shape[2])
@@ -1430,17 +1432,20 @@ def mm_dequant(
         new_col_stats.shape[0] == col_stats.shape[0]
     ), f"{new_col_stats.shape} vs {col_stats.shape}"
 
+    prev_device = pre_call(A.device)
     ptrA = get_ptr(A)
     ptrOut = get_ptr(out)
     ptrRowStats = get_ptr(row_stats)
     ptrColStats = get_ptr(col_stats)
     ptrNewRowStats = get_ptr(new_row_stats)
     ptrNewColStats = get_ptr(new_col_stats)
+    ptrBias = get_ptr(bias)
     numRows = ct.c_int32(out_shape[0])
     numCols = ct.c_int32(out_shape[1])
 
-    is_on_gpu([A, row_stats, col_stats, out, new_row_stats, new_col_stats])
-    lib.cdequant_mm_int32_fp16(ptrA, ptrRowStats, ptrColStats, ptrOut, ptrNewRowStats, ptrNewColStats, numRows, numCols)
+    is_on_gpu([A, row_stats, col_stats, out, new_row_stats, new_col_stats, bias])
+    lib.cdequant_mm_int32_fp16(ptrA, ptrRowStats, ptrColStats, ptrOut, ptrNewRowStats, ptrNewColStats, ptrBias, numRows, numCols)
+    post_call(prev_device)
 
     return out
 

csrc/kernels.cu

@@ -1889,7 +1889,7 @@ template __global__ void kgetColRowStats<half, 64, 4, 16, 64*4, 1>(half * __rest
 
 #define MM_DEQUANT_CONST 6.200012e-05f //1.0f/(127.0f*127.0f)
 
-template <int ITEMS_PER_THREAD, int SUBTILE_ROWS, int THREADS>__global__ void kdequant_mm_int32_fp16(int *__restrict__ const A, float *__restrict__ const rowStats, float *__restrict__ const colStats, half *out, float* newRowStats, float* newcolStats, const int numRows, const int numCols, const int tileCols, const int n)
+template <int ITEMS_PER_THREAD, int SUBTILE_ROWS, int THREADS>__global__ void kdequant_mm_int32_fp16(int *__restrict__ const A, float *__restrict__ const rowStats, float *__restrict__ const colStats, half *out, float* newRowStats, float* newcolStats, half *__restrict__ const bias, const int numRows, const int numCols, const int tileCols, const int n)
 {
 
   // Strategy: To dequantize we need to load col/row statistics. This can be very expensive
@@ -2675,7 +2675,7 @@ template __global__ void kTransformRowToFormat<256, 8, 32, 32*8, 1, COL_TURING>(
 template __global__ void kTransformRowToFormat<256, 8, 32, 32*8, 0, COL_AMPERE>(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, COL_AMPERE>(char *__restrict__ const A, char *out, int rows, int cols, int tiledCols, int outRows, int outCols);
 
-template __global__ void kdequant_mm_int32_fp16<4, 128, 512>(int *__restrict__ const A, float *__restrict__ const rowStats, float *__restrict__ const colStats, half *out, float* newRowStats, float* newcolStats, const int numRows, const int numCols, const int tileCols, const int n);
+template __global__ void kdequant_mm_int32_fp16<4, 128, 512>(int *__restrict__ const A, float *__restrict__ const rowStats, float *__restrict__ const colStats, half *out, float* newRowStats, float* newcolStats, half * __restrict__ const bias, const int numRows, const int numCols, const int tileCols, const int n);
 
 template __global__ void kDoubleRowColQuant<64, 4, 16, 64*4, 0>(half *__restrict__ const A, float *__restrict__ const rowStats, float * __restrict__ const colStats, char *out_col_normed, char *out_row_normed, int *rowidx, int *colidx, half *val, int * __restrict__ nnz_block_ptr, float threshold, int rows, int cols, int tiledCols);
 template __global__ void kDoubleRowColQuant<64, 4, 16, 64*4, 1>(half *__restrict__ const A, float *__restrict__ const rowStats, float * __restrict__ const colStats, char *out_col_normed, char *out_row_normed, int *rowidx, int *colidx, half *val, int * __restrict__ nnz_block_ptr, float threshold, int rows, int cols, int tiledCols);

csrc/kernels.cuh

@@ -111,7 +111,7 @@ template <typename T, int SPMM_ITEMS, int BITS> __global__ void kspmm_coo_very_s
 
 template <int ITEMS_PER_THREAD, int SUBTILE_ROWS, int THREADS>__global__ void kdequant_mm_int32_fp16(
   int *__restrict__ const A, float *__restrict__ const rowStats, float *__restrict__ const colStats,
-  half *out, float* newRowStats, float* newcolStats, const int numRows, const int numCols, const int tileCols, const int n);
+  half *out, float* newRowStats, float* newcolStats, half * __restrict__ const bias, const int numRows, const int numCols, const int tileCols, const int n);
 
 template<typename T, int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int SPARSE_DECOMP> __global__ void kgetColRowStats(T * __restrict__ A, float *rowStats, float *colStats, int * nnz_count_row, float nnz_threshold, int rows, int cols, int tiledRows, int tiledCols);
 template <int THREADS, int ITEMS_PER_THREAD, int TILE_ROWS, int TILE_COLS, int SPARSE_DECOMP> __global__ void kDoubleRowColQuant(half *__restrict__ const A, float *__restrict__ const rowStats, float * __restrict__ const colStats, char *out_col_normed, char *out_row_normed, int *rowidx, int *colidx, half *val, int * __restrict__ nnz_block_ptr, float threshold, int rows, int cols, int tiledCols);

csrc/ops.cu

@@ -435,7 +435,7 @@ int fill_up_to_nearest_multiple(int value, int multiple)
   return value + (value % multiple == 0 ? 0 : (multiple - (value % multiple)));
 }
 
-void dequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, float* newRowStats, float* newcolStats, int numRows, int numCols)
+void dequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, float* newRowStats, float* newcolStats, half *bias, int numRows, int numCols)
 {
   int threads = 512;
   int tileCols = fill_up_to_nearest_multiple(numCols, 32);
@@ -447,7 +447,7 @@ void dequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out,
   num_blocks = num_blocks*(tileCols/32);
   assert(threads <= tilesize);
 
-  kdequant_mm_int32_fp16<4, 128, 512><<<num_blocks, threads>>>(A, rowStats, colStats, out, newRowStats, newcolStats, numRows, numCols, tileCols, n);
+  kdequant_mm_int32_fp16<4, 128, 512><<<num_blocks, threads>>>(A, rowStats, colStats, out, newRowStats, newcolStats, bias, numRows, numCols, tileCols, n);
   CUDA_CHECK_RETURN(cudaPeekAtLastError());
 }
 
@@ -465,7 +465,6 @@ void getColRowStats(half * A, float *rowStats, float *colStats, int *nnz_count_r
 	col_tiles = col_tiles > 0 ? col_tiles : 1;
   int num_blocks = row_tiles * col_tiles;
 
-
   if(nnz_threshold == 0.0)
     kgetColRowStats<half, STATS_THREADS, STATS_ITEMS, STATS_ROWS, STATS_THREADS*STATS_ITEMS, 0><<<num_blocks, STATS_THREADS>>>(A, rowStats, colStats, nnz_count_row, nnz_threshold, rows, cols, tiledRows, tiledCols);
   else if(nnz_threshold != 0.0)

csrc/ops.cuh

@@ -163,7 +163,7 @@ template <int FORMATB, int DTYPE_OUT, int SCALE_ROWS> int igemmlt(cublasLtHandle
 
 template <typename T, int SRC, int TARGET, bool transpose, int DTYPE> void transform(cublasLtHandle_t ltHandle, T *A, T *out, int dim1, int dim2);
 void cutlass_igemm(bool transposeA, bool transposeB, int m, int n, int k, void *A, void *B, void *C, int lda, int ldb, int ldc);
-void dequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, float* newRowStats, float* newcolStats, int numRows, int numCols);
+void dequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, float* newRowStats, float* newcolStats, half* bias, int numRows, int numCols);
 void getColRowStats(half * A, float *rowStats, float *colStats, int *nnz_count_row, float nnz_threshold, int rows, int cols);
 void doubleRowColQuant(half * A, float *rowStats, float *colStats, char *out_col_normed, char *out_row_normed,
                        int *rowidx, int *colidx, half *val, int *nnz_block_ptr, float threshold, int rows, int cols);

csrc/pythonInterface.c

@@ -248,8 +248,8 @@ extern "C"
 	MAKE_FUNC_CTRANSFORM(8, col32, row, n, int8_t, COL32, ROW, false, 8)
 	MAKE_FUNC_CTRANSFORM(32, col32, row, n, int32_t, COL32, ROW, false, 32)
 
-	void cdequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, float* newRowStats, float* newcolStats, int numRows, int numCols)
-	{ dequant_mm_int32_fp16(A, rowStats, colStats, out, newRowStats, newcolStats, numRows, numCols); }
+	void cdequant_mm_int32_fp16(int *A, float *rowStats, float *colStats, half *out, float* newRowStats, float* newcolStats, half* bias, int numRows, int numCols)
+	{ dequant_mm_int32_fp16(A, rowStats, colStats, out, newRowStats, newcolStats, bias, numRows, numCols); }
 	void cget_col_row_stats(half * A, float *rowStats, float *colStats, int *nnz_count_row, float nnz_threshold, int rows, int cols)
 	{ getColRowStats(A, rowStats, colStats, nnz_count_row, nnz_threshold, rows, cols); }
 

tests/test_functional.py

@@ -961,20 +961,24 @@ dim4 = torch.randint(64, 1024, size=(n,)).tolist()
 dims = (2,)
 # ldb = list(range(256, 1*1024, 256))
 formatB = ["col_turing", "col_ampere"]
-values = list(product(dim1, dim4, dims, formatB))
+has_bias = [True, False]
+values = list(product(dim1, dim4, dims, formatB, has_bias))
 names = [
-    "dim1_{0}_dim4_{1}_dims_{2}_formatB_{3}".format(*vals) for vals in values
+    "dim1_{0}_dim4_{1}_dims_{2}_formatB_{3}_has_bias_{4}".format(*vals) for vals in values
 ]
 
 
-@pytest.mark.parametrize("dim1, dim4, dims, formatB", values, ids=names)
-def test_dequant_mm(dim1, dim4, dims, formatB):
+@pytest.mark.parametrize("dim1, dim4, dims, formatB, has_bias", values, ids=names)
+def test_dequant_mm(dim1, dim4, dims, formatB, has_bias):
     inner = torch.randint(1, 128, size=(1,)).item()
+    bias = None
+    if has_bias: bias = torch.randn(dim4, device='cuda', dtype=torch.float16)
     formatB = F.get_special_format_str()
     for i in range(k):
         A = torch.randn(dim1, inner, device="cuda")
         B = torch.randn(dim4, inner, device="cuda")
         C1 = torch.matmul(A.half(), B.t().half())
+        if has_bias: C1 += bias
 
         A1, maxA = F.vectorwise_quant(A, dim=1)
         B1, maxB = F.vectorwise_quant(B, dim=1)
@@ -985,17 +989,15 @@ def test_dequant_mm(dim1, dim4, dims, formatB):
 
         C3, S = F.nvidia_transform(C2, "row", state=SC)
         C4 = F.vectorwise_mm_dequant(C3.float(), maxA, maxB.t())
+        if has_bias: C4 += bias
 
         count = (torch.isclose(C1, C4, atol=0.01, rtol=0.1) == 0).sum().item()
         n = C1.numel()
         p = 0.06
-        assert (
-            count / n < p
-        ), f"error in more than {p} of elements: {count}/{n}={count/n}"
+        assert (count / n < p), f"error in more than {p} of elements: {count}/{n}={count/n}"
 
-        C5 = F.mm_dequant(C2, SC, maxA.flatten(), maxB.flatten())
+        C5 = F.mm_dequant(C2, SC, maxA.flatten(), maxB.flatten(), bias=bias)
         torch.testing.assert_allclose(C5, C4)
-        # print(C2)
 
 
 n = 2