diff options
Diffstat (limited to 'bitsandbytes')
| -rw-r--r-- | bitsandbytes/autograd/_functions.py | 16 | ||||
| -rw-r--r-- | bitsandbytes/functional.py | 21 |
2 files changed, 30 insertions, 7 deletions
diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py index 815a4f1..370ca83 100644 --- a/bitsandbytes/autograd/_functions.py +++ b/bitsandbytes/autograd/_functions.py @@ -1,4 +1,5 @@ import torch +import math import bitsandbytes as bnb import bitsandbytes.functional as F @@ -162,6 +163,17 @@ 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 @@ -265,6 +277,8 @@ 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 @@ -293,7 +307,7 @@ class MatMul8bitLt(torch.autograd.Function): gradA32, SgradA32 = F.igemmlt(C32grad, state.CxBt, Sgrad, state.SBt) grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(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 0a2d557..494de1b 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 lib, COMPILED_WITH_CUDA @@ -919,15 +920,22 @@ 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}' + print(shapeA, math.prod(shapeA), math.prod(list(shapeA))) + print('aaa') + + # 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((shapeA[0], shapeB[0]), dtype, A.device, 'col32', 'row') @@ -984,6 +992,7 @@ def igemmlt(A, B, SA, SB, out=None, Sout=None, dtype=torch.int32): has_error = lib.cigemmlt_ampere_8(ptr, m, n, k, ptrA, ptrB, ptrC, ptrRowScale, lda, ldb, ldc) if has_error == 1: + 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) |