Merge branch 'debug' into cuda-bin-switch-and-cli

2 files changed, 96 insertions, 138 deletions

diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py
index b56b2ee..14f2660 100644
--- a/bitsandbytes/autograd/_functions.py
+++ b/bitsandbytes/autograd/_functions.py
@@ -1,7 +1,7 @@
 from dataclasses import dataclass
 
 import torch
-
+import math
 import bitsandbytes as bnb
 import bitsandbytes.functional as F
 
@@ -199,6 +199,17 @@ class MatmulLtState:
 class MatMul8bitLt(torch.autograd.Function):
     @staticmethod
     def forward(ctx, A, B, out=None, state=MatmulLtState()):
+        # default to pytorch behavior if inputs are empty
+        ctx.is_empty = False
+        if math.prod(A.shape) == 0:
+            ctx.is_empty = True
+            ctx.A = A
+            ctx.B = B
+            if A.shape[-1] == B.shape[0]:
+                return torch.empty(A.shape[:-1]+B.shape[1:], dtype=torch.float16, device=A.device)
+            else:
+                return torch.empty(A.shape[:-1]+B.shape[:1], dtype=torch.float16, device=A.device)
+
         # 1. Quantize A
         # 2. Quantize B
         # 3. Matmul
@@ -339,6 +350,8 @@ class MatMul8bitLt(torch.autograd.Function):
 
     @staticmethod
     def backward(ctx, grad_output):
+        if ctx.is_empty:
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None
         req_gradA, req_gradB = ctx.req_grads
         CAt, subA = ctx.tensors
         SCAt, idx = ctx.tensor_states
@@ -375,7 +388,7 @@ class MatMul8bitLt(torch.autograd.Function):
                 ctx.grad_shape
             )
 
-        return grad_A, grad_B, None, None, None, None, None
+        return grad_A, grad_B, None, None
 
 
 matmul = MatMul8bitLt.apply
diff --git a/bitsandbytes/functional.py b/bitsandbytes/functional.py
index 236ef39..b4409e4 100644
--- a/bitsandbytes/functional.py
+++ b/bitsandbytes/functional.py
@@ -4,9 +4,10 @@
 # LICENSE file in the root directory of this source tree.
 import ctypes as ct
 import random
-from typing import Tuple
-
+import math
 import torch
+
+from typing import Tuple
 from torch import Tensor
 
 from .cextension import COMPILED_WITH_CUDA, lib
@@ -193,6 +194,14 @@ def get_special_format_str():
         return "col_turing"
 
 
+
+def is_on_gpu(tensors):
+    on_gpu = True
+    for t in tensors:
+        if t is None: continue # NULL pointers are fine
+        on_gpu &= t.device.type == 'cuda'
+    return on_gpu
+
 def get_ptr(A: Tensor) -> ct.c_void_p:
     """
     Get the ctypes pointer from a PyTorch Tensor.
@@ -336,7 +345,7 @@ def nvidia_transform(
 def estimate_quantiles(
     A: Tensor, out: Tensor = None, offset: float = 1 / 512
 ) -> Tensor:
-    """
+    '''
     Estimates 256 equidistant quantiles on the input tensor eCDF.
 
     Uses SRAM-Quantiles algorithm to quickly estimate 256 equidistant quantiles
@@ -361,9 +370,9 @@ def estimate_quantiles(
     -------
     torch.Tensor:
         The 256 quantiles in float32 datatype.
-    """
-    if out is None:
-        out = torch.zeros((256,), dtype=torch.float32, device=A.device)
+    '''
+    if out is None: out = torch.zeros((256,), dtype=torch.float32, device=A.device)
+    is_on_gpu([A, out])
     if A.dtype == torch.float32:
         lib.cestimate_quantiles_fp32(
             get_ptr(A), get_ptr(out), ct.c_float(offset), ct.c_int(A.numel())
@@ -428,7 +437,8 @@ def quantize_blockwise(
     if out is None:
         out = torch.zeros_like(A, dtype=torch.uint8)
 
-    if A.device.type != "cpu":
+    if A.device.type != 'cpu':
+        is_on_gpu([code, A, absmax, out, rand])
         if rand is not None:
             assert rand.numel() >= 1024
             rand_offset = random.randint(0, 1023)
@@ -541,7 +551,8 @@ def dequantize_blockwise(
             f"The blockwise of {blocksize} is not supported. Supported values: [2048 4096]"
         )
 
-    if A.device.type != "cpu":
+    if A.device.type != 'cpu':
+        is_on_gpu([A, out])
         if out.dtype == torch.float32:
             lib.cdequantize_blockwise_fp32(
                 get_ptr(quant_state[1]),
@@ -610,7 +621,7 @@ def dequantize(
 
 
 def quantize_no_absmax(A: Tensor, code: Tensor, out: Tensor = None) -> Tensor:
-    """
+    '''
     Quantizes input tensor to 8-bit.
 
     Quantizes the 32-bit input tensor `A` to the 8-bit output tensor
@@ -629,15 +640,15 @@ def quantize_no_absmax(A: Tensor, code: Tensor, out: Tensor = None) -> Tensor:
     -------
     torch.Tensor:
         Quantized 8-bit tensor.
-    """
-    if out is None:
-        out = torch.zeros_like(A, dtype=torch.uint8)
+    '''
+    if out is None: out = torch.zeros_like(A, dtype=torch.uint8)
+    is_on_gpu([A, out])
     lib.cquantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel()))
     return out
 
 
 def dequantize_no_absmax(A: Tensor, code: Tensor, out: Tensor = None) -> Tensor:
-    """
+    '''
     Dequantizes the 8-bit tensor to 32-bit.
 
     Dequantizes the 8-bit tensor `A` to the 32-bit tensor `out` via
@@ -656,12 +667,10 @@ def dequantize_no_absmax(A: Tensor, code: Tensor, out: Tensor = None) -> Tensor:
     -------
     torch.Tensor:
         32-bit output tensor.
-    """
-    if out is None:
-        out = torch.zeros_like(A, dtype=torch.float32)
-    lib.cdequantize(
-        get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel())
-    )
+    '''
+    if out is None: out = torch.zeros_like(A, dtype=torch.float32)
+    is_on_gpu([code, A, out])
+    lib.cdequantize(get_ptr(code), get_ptr(A), get_ptr(out), ct.c_int(A.numel()))
     return out
 
 
@@ -983,6 +992,7 @@ def percentile_clipping(
         The current optimiation steps (number of past gradient norms).
 
     """
+    is_on_gpu([grad, gnorm_vec])
     if grad.dtype == torch.float32:
         lib.cpercentile_clipping_g32(
             get_ptr(grad),
@@ -1027,21 +1037,11 @@ def histogram_scatter_add_2d(
 
     maxdim1 = ct.c_int32(histogram.shape[0])
     n = ct.c_int32(index1.numel())
-    lib.chistogram_scatter_add_2d(
-        get_ptr(histogram),
-        get_ptr(index1),
-        get_ptr(index2),
-        get_ptr(source),
-        maxdim1,
-        n,
-    )
+    is_on_gpu([histogram, index1, index2d, source])
+    lib.chistogram_scatter_add_2d(get_ptr(histogram), get_ptr(index1), get_ptr(index2), get_ptr(source), maxdim1, n)
 
-
-def check_matmul(
-    A, B, out, transposed_A, transposed_B, expected_type=torch.int8
-):
-    if not torch.cuda.is_initialized():
-        torch.cuda.init()
+def check_matmul(A, B, out, transposed_A, transposed_B, expected_type=torch.int8):
+    if not torch.cuda.is_initialized(): torch.cuda.init()
     if A.dtype != expected_type or B.dtype != expected_type:
         raise TypeError(
             f"Expected torch.int8 input tensors A and B, but got {A.dtype} and {B.dtype}"
@@ -1212,21 +1212,10 @@ def igemm(
     ptr = CUBLAS_Context.get_instance().get_context(A.device)
 
     # B^T @ A^T = C^T
-    # [km, nk -> mn]
-    lib.cigemm(
-        ptr,
-        ct.c_bool(transposed_B),
-        ct.c_bool(transposed_A),
-        ct.c_int32(m),
-        ct.c_int32(n),
-        ct.c_int32(k),
-        get_ptr(B),
-        get_ptr(A),
-        get_ptr(out),
-        ct.c_int32(lda),
-        ct.c_int32(ldb),
-        ct.c_int32(ldc),
-    )
+    # [km, nk -> mn] 
+    is_on_gpu([B, A, out])
+    lib.cigemm(ptr, ct.c_bool(transposed_B), ct.c_bool(transposed_A), ct.c_int32(m), ct.c_int32(n), ct.c_int32(k),
+               get_ptr(B), get_ptr(A), get_ptr(out), ct.c_int32(lda), ct.c_int32(ldb), ct.c_int32(ldc))
     return out
 
 
@@ -1306,24 +1295,10 @@ def batched_igemm(
 
     ptr = CUBLAS_Context.get_instance().get_context(A.device)
 
-    lib.cbatched_igemm(
-        ptr,
-        ct.c_bool(transposed_B),
-        ct.c_bool(transposed_A),
-        ct.c_int32(m),
-        ct.c_int32(n),
-        ct.c_int32(k),
-        get_ptr(B),
-        get_ptr(A),
-        get_ptr(out),
-        ct.c_int32(lda),
-        ct.c_int32(ldb),
-        ct.c_int32(ldc),
-        ct.c_long(strideA),
-        ct.c_long(strideB),
-        ct.c_long(strideC),
-        ct.c_uint32(num_batch),
-    )
+    is_on_gpu([B, A, out])
+    lib.cbatched_igemm(ptr, ct.c_bool(transposed_B), ct.c_bool(transposed_A), ct.c_int32(m), ct.c_int32(n), ct.c_int32(k),
+               get_ptr(B), get_ptr(A), get_ptr(out), ct.c_int32(lda), ct.c_int32(ldb), ct.c_int32(ldc),
+               ct.c_long(strideA), ct.c_long(strideB), ct.c_long(strideC), ct.c_uint32(num_batch))
     return out
 
 
@@ -1332,15 +1307,20 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32):
     shapeB = SB[0]
     dimsA = len(shapeA)
     dimsB = len(shapeB)
+    assert dimsB == 2, 'Only two dimensional matrices are supported for argument B'
     if dimsA == 2:
         m = shapeA[0]
     elif dimsA == 3:
         m = shapeA[0] * shapeA[1]
 
-    if dimsB == 2:
-        rows = n = shapeB[0]
-    elif dimsB == 3:
-        rows = n = shapeB[0] * shapeB[1]
+    rows = n = shapeB[0]
+    assert math.prod(list(shapeA)) > 0, f'Input tensor dimensions need to be > 0: {shapeA}'
+
+    # if the tensor is empty, return a transformed empty tensor with the right dimensions
+    if shapeA[0] == 0 and dimsA == 2:
+        return torch.empty((0, shapeB[0]), device=A.device, dtype=torch.float16)
+    elif shapeA[1] == 0 and dimsA == 3:
+        return torch.empty(tuple(shapeA[:2] + [shapeB[0]]), device=A.device, dtype=torch.float16)
 
     if dimsA == 2 and out is None:
         out, Sout = get_transform_buffer(
@@ -1390,7 +1370,8 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32):
 
     has_error = 0
     ptrRowScale = get_ptr(None)
-    if formatB == "col_turing":
+    is_on_gpu([A, B, out])
+    if formatB == 'col_turing':
         if dtype == torch.int32:
             has_error = lib.cigemmlt_turing_32(
                 ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc
@@ -1410,7 +1391,8 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32):
             )
 
     if has_error == 1:
-        raise Exception("cublasLt ran into an error!")
+        print(f'A: {shapeA}, B: {shapeB}, C: {Sout[0]}; (lda, ldb, ldc): {(lda, ldb, ldc)}; (m, n, k): {(m, n, k)}')
+        raise Exception('cublasLt ran into an error!')
 
     torch.cuda.set_device(prev_device)
 
@@ -1457,16 +1439,8 @@ def mm_dequant(
     numRows = ct.c_int32(out_shape[0])
     numCols = ct.c_int32(out_shape[1])
 
-    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])
+    lib.cdequant_mm_int32_fp16(ptrA, ptrRowStats, ptrColStats, ptrOut, ptrNewRowStats, ptrNewColStats, numRows, numCols)
 
     return out
 
@@ -1507,15 +1481,8 @@ def get_colrow_absmax(
     cols = ct.c_int32(cols)
 
     prev_device = pre_call(A.device)
-    lib.cget_col_row_stats(
-        ptrA,
-        ptrRowStats,
-        ptrColStats,
-        ptrNnzrows,
-        ct.c_float(threshold),
-        rows,
-        cols,
-    )
+    is_on_gpu([A, row_stats, col_stats, nnz_block_ptr])
+    lib.cget_col_row_stats(ptrA, ptrRowStats, ptrColStats, ptrNnzrows, ct.c_float(threshold), rows, cols)
     post_call(prev_device)
 
     if threshold > 0.0:
@@ -1642,6 +1609,7 @@ def double_quant(
     ptrOutCol = get_ptr(out_col)
     ptrOutRow = get_ptr(out_row)
 
+    is_on_gpu([A, col_stats, row_stats, out_col, out_row])
     if threshold > 0.0:
         nnz = nnz_row_ptr[-1].item()
         if nnz > 0:
@@ -1714,33 +1682,19 @@ def get_special_format_str():
         )
         assert major >= 7
 
-    if major == 7:
-        return "col_turing"
-    elif major == 8:
-        return "col_ampere"
-    else:
-        return "col_turing"
+    if major == 7: return 'col_turing'
+    elif major == 8: return 'col_ampere'
+    else: return 'col_turing'
 
 
-def transform(
-    A,
-    to_order,
-    from_order="row",
-    out=None,
-    transpose=False,
-    state=None,
-    ld=None,
-):
-    if state is None:
-        state = (A.shape, from_order)
-    else:
-        from_order = state[1]
-    if out is None:
-        out, new_state = get_transform_buffer(
-            state[0], A.dtype, A.device, to_order, state[1], transpose
-        )
-    else:
-        new_state = (state[0], to_order)  # (shape, order)
+
+
+def transform(A, to_order, from_order='row', out=None, transpose=False, state=None, ld=None):
+    prev_device = pre_call(A.device)
+    if state is None: state = (A.shape, from_order)
+    else: from_order = state[1]
+    if out is None: out, new_state = get_transform_buffer(state[0], A.dtype, A.device, to_order, state[1], transpose)
+    else: new_state = (state[0], to_order) # (shape, order)
 
     shape = state[0]
     if len(shape) == 2:
@@ -1752,7 +1706,8 @@ def transform(
 
     ptrA = get_ptr(A)
     ptrOut = get_ptr(out)
-    if to_order == "col32":
+    is_on_gpu([A, out])
+    if to_order == 'col32':
         if transpose:
             lib.ctransform_row2col32T(get_ptr(A), get_ptr(out), dim1, dim2)
         else:
@@ -1773,9 +1728,9 @@ def transform(
         elif from_order == "col_ampere":
             lib.ctransform_ampere2row(get_ptr(A), get_ptr(out), dim1, dim2)
     else:
-        raise NotImplementedError(
-            f"Transform function not implemented: From {from_order} to {to_order}"
-        )
+        raise NotImplementedError(f'Transform function not implemented: From {from_order} to {to_order}')
+
+    post_call(prev_device)
 
     return out, new_state
 
@@ -1810,21 +1765,8 @@ def spmm_coo(cooA, B, out=None):
     cldb = ct.c_int32(ldb)
     cldc = ct.c_int32(ldc)
 
-    lib.cspmm_coo(
-        ptr,
-        ptrRowidx,
-        ptrColidx,
-        ptrValues,
-        cnnz,
-        crowsA,
-        ccolsA,
-        ccolsB,
-        cldb,
-        ptrB,
-        cldc,
-        ptrC,
-        ct.c_bool(transposed_B),
-    )
+    is_on_gpu([cooA.rowidx, cooA.colidx, cooA.values, B, out])
+    lib.cspmm_coo(ptr, ptrRowidx, ptrColidx, ptrValues, cnnz, crowsA, ccolsA, ccolsB, cldb, ptrB, cldc, ptrC, ct.c_bool(transposed_B))
 
     return out
 
@@ -1875,6 +1817,7 @@ def spmm_coo_very_sparse(cooA, B, dequant_stats=None, out=None):
     # print(cooA.rowidx[:64])
     # print(cooA.colidx[:64].sort()[0])
 
+    is_on_gpu([cooA.rowidx, cooA.colidx, cooA.values, B, out, dequant_stats])
     if B.dtype == torch.float16:
         lib.cspmm_coo_very_sparse_naive_fp16(
             ptrMaxCount,
@@ -2061,9 +2004,11 @@ def extract_outliers(A, SA, idx):
     ptrIdx = get_ptr(idx)
     ptrOut = get_ptr(out)
 
-    if formatA == "col_turing":
+    prev_device = pre_call(A.device)
+    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)
+    post_call(prev_device)
 
     return out