Most tests passing.

1 files changed, 307 insertions, 0 deletions

diff --git a/bitsandbytes/autograd/_functions.py b/bitsandbytes/autograd/_functions.py
new file mode 100644
index 0000000..815a4f1
--- /dev/null
+++ b/bitsandbytes/autograd/_functions.py
@@ -0,0 +1,307 @@
+import torch
+import bitsandbytes as bnb
+import bitsandbytes.functional as F
+
+from dataclasses import dataclass
+
+tensor = torch.Tensor
+
+'''
+    This class pools outlier dimensions across layers.
+    This is particularly important for small models where outlier features 
+    are less systematic and occur with low frequency.
+'''
+class GlobalOutlierPooler(object):
+    _instance = None
+
+    def __init__(self):
+        raise RuntimeError('Call get_instance() instead')
+
+    def initialize(self):
+        self.outliers = set()
+        self.model_dim = None
+
+    @classmethod
+    def get_instance(cls):
+        if cls._instance is None:
+            cls._instance = cls.__new__(cls)
+            cls._instance.initialize()
+        return cls._instance
+
+    def add_outliers(self, outlier_idx, feature_dim):
+        if self.model_dim is None: self.model_dim = feature_dim
+        if feature_dim != self.model_dim: return # we do not encode outliers for the 2nd FFN layer
+
+        self.outliers.update(outlier_idx.tolist())
+
+    def get_current_outlier_idx(self):
+        return torch.Tensor(list(self.outliers)).to(torch.int64)
+
+class MatMul8bit(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, A, B, out=None, quant_type='vector', precision=[8, 8, 8]):
+
+        if precision[0] != 8:
+            with torch.no_grad():
+                output = torch.matmul(A, B)
+        else:
+            if len(B.shape) == 2: dim = 0
+            else: dim = 1
+            qA, SA = F.vectorwise_quant(A, dim=-1, quant_type=quant_type)
+            qB, SB = F.vectorwise_quant(B, dim=dim, quant_type=quant_type)
+            iout = F.igemm(qA, qB)
+            output = F.vectorwise_mm_dequant(iout, SA, SB, A.dtype, quant_type)
+
+        if A.requires_grad or B.requires_grad:
+            ctx.save_for_backward(A, B)
+
+        ctx.quant_type = quant_type
+        ctx.precision = precision
+
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        A, B = ctx.saved_tensors
+        quant_type = ctx.quant_type
+        precision = ctx.precision
+        grad_A = grad_B = None
+
+        if B.requires_grad:
+            if len(A.shape) == 3:
+                dims = [0, 1]
+                # bsi -> ibs
+                permute_dim = [0, 2, 1]
+            else:
+                dims = [0]
+                # bs -> sb
+                permute_dim = [1, 0]
+
+            if precision[1] != 8:
+                with torch.no_grad():
+                    grad_B = torch.matmul(A.permute(permute_dim), grad_output)
+            else:
+                if len(B.shape) == 2 and len(A.shape) == 3:
+                    grad_output = grad_output.contiguous()
+                    if not grad_output.is_contiguous(): grad_output.contiguous()
+                    qgrad_output, S1 = F.vectorwise_quant(grad_output.view(-1, grad_output.shape[2]), dim=0, quant_type=quant_type)
+                    if not A.is_contiguous(): A = A.contiguous()
+                    qA, S2 = F.vectorwise_quant(A.view(-1, A.shape[2]), dim=0, quant_type=quant_type)
+                    igrad_B = F.igemm(qA.t(), qgrad_output)
+                    grad_B = F.vectorwise_mm_dequant(igrad_B, S2.t(), S1, grad_output.dtype, quant_type)
+                else:
+                    qgrad_output, S1 = F.vectorwise_quant(grad_output, dim=dims, quant_type=quant_type)
+                    qA, S2 = F.vectorwise_quant(A, dim=dims, quant_type=quant_type)
+                    igrad_B = F.igemm(qA.permute(permute_dim), qgrad_output)
+                    grad_B = F.vectorwise_mm_dequant(igrad_B, S2.permute(permute_dim), S1, grad_output.dtype, quant_type)
+
+        if A.requires_grad:
+            if len(grad_output.shape) == 3: dims = [2]
+            else: dims = [1]
+
+            if len(B.shape) == 3:
+                # bio -> boi
+                permute_dim = [0, 2, 1]
+                dim_B = dims
+            else:
+                # io -> oi
+                permute_dim = [1, 0]
+                dim_B = [1]
+
+            if precision[2] != 8:
+                with torch.no_grad():
+                    grad_A = torch.matmul(grad_output, B.permute(permute_dim))
+            else:
+                qgrad_output, S1 = F.vectorwise_quant(grad_output, dim=dims, quant_type=quant_type)
+                qB, S3 = F.vectorwise_quant(B, dim=dim_B, quant_type=quant_type)
+                igrad_A = F.igemm(qgrad_output, qB.permute(permute_dim))
+                grad_A = F.vectorwise_mm_dequant(igrad_A, S1, S3.permute(permute_dim), grad_output.dtype, quant_type)
+
+        return grad_A, grad_B, None, None, None
+
+
+mm_cublas = MatMul8bit.apply
+bmm_cublas = MatMul8bit.apply
+matmul_cublas = MatMul8bit.apply
+
+@dataclass
+class MatmulLtState:
+    CB = None
+    CxB = None
+    SB = None
+    SCB = None
+
+    CxBt = None
+    SBt = None
+    CBt = None
+
+    subB = None
+
+    outlier_pool = None
+    has_accumulated_gradients = False
+    threshold = 0.0
+    idx = None
+    is_training = True
+    has_fp16_weights = True
+    use_pool = False
+    formatB = F.get_special_format_str()
+
+    def reset_grads(self):
+        self.CB = None
+        self.CxB = None
+        self.SB = None
+        self.SCB = None
+
+        self.CxBt = None
+        self.SBt = None
+        self.CBt = None
+
+
+class MatMul8bitLt(torch.autograd.Function):
+
+    @staticmethod
+    def forward(ctx, A, B, out=None, state=MatmulLtState()):
+        # 1. Quantize A
+        # 2. Quantize B
+        # 3. Matmul
+        # 4. Mixed-precision decomposition matmul
+        # 5. Save state
+        requires_gradA = A.requires_grad
+        requires_gradB = B.requires_grad
+        formatB = state.formatB
+        input_shape = A.shape
+        if state.outlier_pool is None: state.outlier_pool = GlobalOutlierPooler.get_instance()
+        assert A.dtype == torch.float16, f'The input data type needs to be fp16 but {A.dtype} was found!'
+
+        # 1. Quantize A
+        if len(A.shape) == 3: A = A.view(-1, A.shape[-1]).contiguous()
+        CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A, threshold=state.threshold)
+
+        if state.threshold > 0.0 and coo_tensorA is not None:
+            if state.has_fp16_weights:
+                idx = torch.unique(coo_tensorA.colidx).long()
+                CA[:, idx] = 0
+                CAt[:, idx] = 0
+                subA = A[:, idx]
+                state.subB = B[:, idx].t().contiguous()
+                state.idx = idx
+            else:
+                if state.CxB is None:
+                    # 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
+        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:
+            has_grad = (True if (getattr(B, 'grad', None) is not None) else False)
+            is_transposed = not B.is_contiguous() and B.shape[0] == B.stride(1)
+            if is_transposed: B = B.contiguous()
+
+            if (state.is_training and not has_grad) or state.CxB is None:
+                state.reset_grads()
+                CB, state.CBt, state.SCB, state.SCBt, coo_tensorB = F.double_quant(B)
+                state.CxB, state.SB = F.transform(CB, to_order=formatB)
+        else:
+            has_grad = False
+
+        shapeB = state.SB[0]
+
+        if len(input_shape) == 3:
+            output_shape = (input_shape[0], input_shape[1], shapeB[0])
+        else:
+            output_shape = (input_shape[0], shapeB[0])
+
+        # 3. Matmul
+        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:
+            output += torch.matmul(subA, state.subB)
+
+        # 5. Save state
+        ctx.state = state
+
+        ctx.formatB = formatB
+        ctx.grad_shape = input_shape
+        ctx.req_grads = [requires_gradA, requires_gradB]
+
+        if requires_gradA or requires_gradB:
+            ctx.tensors = (CAt, subA)
+            ctx.tensor_states = (SCAt, state.idx)
+        else:
+            ctx.tensors = [None, None]
+            ctx.tensor_states = (None, None)
+            ctx.save_for_backward(None, None)
+
+        #clone_func = torch.clone if len(output_shape) == 3 else lambda x : x
+        clone_func = torch.clone
+        return clone_func(output.view(output_shape))
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        req_gradA, req_gradB = ctx.req_grads
+        CAt, subA = ctx.tensors
+        SCAt, idx = ctx.tensor_states
+        formatB = ctx.formatB
+        state = ctx.state
+        assert state.has_fp16_weights, 'Backprop only supported for fp16 weights.'
+
+        if len(grad_output.shape) == 3:
+            grad_output = grad_output.view(-1, grad_output.shape[-1]).contiguous()
+
+        grad_A = grad_B = None
+
+        Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output)
+        if req_gradB:
+            CxAt, SAt = F.transform(CAt, formatB, transpose=True)
+            C32grad, Sgrad = F.transform(Cgradt, 'col32', transpose=True)
+            gradB32, SgradB32 = F.igemmlt(C32grad, CxAt, Sgrad, SAt)
+            grad_B = F.mm_dequant(gradB32, SgradB32, SCgradt, SCAt)
+            if state.threshold > 0.0 and subA is not None:
+                grad_B[:, idx] += torch.matmul(grad_output.t(), subA)
+
+        if req_gradA:
+            C32grad, Sgrad = F.transform(Cgrad, 'col32')
+            if state.CxBt is None:
+                state.CxBt, state.SBt = F.transform(state.CBt, to_order=formatB, transpose=True)
+            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
+
+
+matmul = MatMul8bitLt.apply
+
+
+def matmul(A : tensor, B : tensor, out : tensor=None, state : MatmulLtState = None, threshold=0.0):
+    state = state or MatmulLtState()
+    if threshold > 0.0:
+        state.threshold = threshold
+    return MatMul8bitLt.apply(A, B, out, state)
+