Added fused bias to matmullt.

bitsandbytes/autograd/_functions.py

@@ -201,13 +201,14 @@ class MatmulLtState:
 
 class MatMul8bitLt(torch.autograd.Function):
     @staticmethod
-    def forward(ctx, A, B, out=None, state=MatmulLtState()):
+    def forward(ctx, A, B, out=None, bias=None, state=MatmulLtState()):
         # default to pytorch behavior if inputs are empty
         ctx.is_empty = False
         if prod(A.shape) == 0:
             ctx.is_empty = True
             ctx.A = A
             ctx.B = B
+            ctx.bias = bias
             if A.shape[-1] == B.shape[0]:
                 return torch.empty(A.shape[:-1]+B.shape[1:], dtype=torch.float16, device=A.device)
             else:
@@ -220,6 +221,7 @@ class MatMul8bitLt(torch.autograd.Function):
         # 5. Save state
         requires_gradA = A.requires_grad
         requires_gradB = B.requires_grad
+        requires_gradBias = bias is not None and bias.requires_grad
         formatB = state.formatB
         input_shape = A.shape
         if state.outlier_pool is None:
@@ -247,28 +249,7 @@ class MatMul8bitLt(torch.autograd.Function):
                 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
-                    )
-                    # 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
+                    state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
         else:
             if not state.has_fp16_weights and state.CxB is None:
                 state.CxB, state.SB = F.transform(state.CB, to_order=formatB)
@@ -326,7 +307,8 @@ class MatMul8bitLt(torch.autograd.Function):
         # 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)
+        # we apply the fused bias here
+        output = F.mm_dequant(out32, Sout32, SCA, state.SCB, bias=bias)
 
         # 4. Mixed-precision decomposition matmul
         if coo_tensorA is not None and subA is not None:
@@ -337,7 +319,7 @@ class MatMul8bitLt(torch.autograd.Function):
 
         ctx.formatB = formatB
         ctx.grad_shape = input_shape
-        ctx.req_grads = [requires_gradA, requires_gradB]
+        ctx.req_grads = [requires_gradA, requires_gradB, requires_gradBias]
 
         if requires_gradA or requires_gradB:
             ctx.tensors = (CAt, subA)
@@ -347,15 +329,16 @@ class MatMul8bitLt(torch.autograd.Function):
             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
+        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):
         if ctx.is_empty:
-            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None
-        req_gradA, req_gradB = ctx.req_grads
+            bias_grad = (None if ctx.bias is None else torch.zeros_like(ctx.bias))
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, bias_grad, None
+        req_gradA, req_gradB, req_gradBias = ctx.req_grads
         CAt, subA = ctx.tensors
         SCAt, idx = ctx.tensor_states
         formatB = ctx.formatB
@@ -369,7 +352,7 @@ class MatMul8bitLt(torch.autograd.Function):
                 -1, grad_output.shape[-1]
             ).contiguous()
 
-        grad_A = grad_B = None
+        grad_A = grad_B = grad_bias = None
 
         Cgrad, Cgradt, SCgrad, SCgradt, coo_tensor = F.double_quant(grad_output)
         if req_gradB:
@@ -387,11 +370,12 @@ class MatMul8bitLt(torch.autograd.Function):
                     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
-            )
+            grad_A = F.mm_dequant(gradA32, SgradA32, SCgrad, state.SCBt).view(ctx.grad_shape)
+
+        if req_gradBias:
+            grad_bias = grad_output.sum(0)
 
-        return grad_A, grad_B, None, None
+        return grad_A, grad_B, None, grad_bias, None
 
 
 matmul = MatMul8bitLt.apply
@@ -403,8 +387,9 @@ def matmul(
     out: tensor = None,
     state: MatmulLtState = None,
     threshold=0.0,
+    bias=None
 ):
     state = state or MatmulLtState()
     if threshold > 0.0:
         state.threshold = threshold
-    return MatMul8bitLt.apply(A, B, out, state)
+    return MatMul8bitLt.apply(A, B, out, bias, state)

bitsandbytes/nn/modules.py

@@ -235,9 +235,7 @@ class Linear8bitLt(nn.Linear):
         if threshold > 0.0 and not has_fp16_weights:
             self.state.use_pool = True
 
-        self.weight = Int8Params(
-            self.weight.data, has_fp16_weights=has_fp16_weights
-        )
+        self.weight = Int8Params(self.weight.data, has_fp16_weights=has_fp16_weights)
 
     def init_8bit_state(self):
         self.state.CB = self.weight.CB
@@ -250,13 +248,12 @@ class Linear8bitLt(nn.Linear):
 
         if self.weight.CB is not None:
             self.init_8bit_state()
+        if self.bias.dtype != torch.float16:
+            self.bias.data = self.bias.data.half()
         # assert not self.state.has_fp16_weights
         # if not self.state.has_fp16_weights: assert self.state.CB is not None or self.state.CxB is not None
 
-        out = bnb.matmul(x, self.weight, state=self.state)
-
-        if self.bias is not None:
-            out += self.bias.unsqueeze(0).expand_as(out)
+        out = bnb.matmul(x, self.weight, bias=self.bias, state=self.state)
 
         if not self.state.has_fp16_weights and self.state.CB is not None:
             # we converted 8-bit row major to turing/ampere format in the first inference pass

tests/test_autograd.py

-from itertools import product
+from itertools import product, permutations
 
 import pytest
 import torch
@@ -241,11 +241,20 @@ decomp = [0.0, 6.0]
 funcs = [(torch.matmul, bnb.matmul)]
 str_funcs = ["matmul"]
 req_grad = [(False, False), (True, False), (True, True), (False, True)]
-req_grad_str = ["FF", "TF", "TT", "FT"]
+req_grad = list(product([True, False], repeat=3))
+req_grad_str = []
+for c in req_grad:
+    strval = ''
+    for v in c:
+        if v == True: strval += 'T'
+        else: strval += 'F'
+    req_grad_str.append(strval)
+
 transpose = [(False, True), (False, False)]
 str_transpose = ["NT", "NN"]
 dtype = [torch.float16]
 has_fp16_weights = [True, False]
+has_bias = [True, False]
 values = list(
     product(
         dim1,
@@ -258,6 +267,7 @@ values = list(
         transpose,
         decomp,
         has_fp16_weights,
+        has_bias
     )
 )
 str_values = list(
@@ -272,18 +282,14 @@ str_values = list(
         str_transpose,
         decomp,
         has_fp16_weights,
+        has_bias
     )
 )
-names = [
-    "dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}".format(
-        *vals
-    )
-    for vals in str_values
-]
+names = ["dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}_has_bias_{10}".format(*vals) for vals in str_values]
 
 
 @pytest.mark.parametrize(
-    "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights",
+    "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights, has_bias",
     values,
     ids=names,
 )
@@ -298,10 +304,14 @@ def test_matmullt(
     transpose,
     decomp,
     has_fp16_weights,
+    has_bias
 ):
     dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
     dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
     outlier_dim = torch.randint(0, dimA[1], size=(dimA[1] // 8,), device="cuda")
+    if has_bias == False:
+        req_grad = list(req_grad)
+        req_grad[2] = False
 
     for i in range(k):
 
@@ -322,6 +332,11 @@ def test_matmullt(
                 requires_grad=req_grad[1],
                 dtype=dtype,
             )
+            bias = None
+            bias2 = None
+            if has_bias: 
+                bias = torch.randn(dim4, device='cuda', dtype=dtype, requires_grad=req_grad[2])
+                bias2 = bias.clone()
             torch.nn.init.xavier_uniform_(B)
             B2 = B.clone()
 
@@ -342,10 +357,13 @@ def test_matmullt(
 
             if not transpose[0] and transpose[1]:
                 out_torch = funcs[0](A, B.t())
-                out_bnb = funcs[1](A, B2, state=state)
+                out_bnb = funcs[1](A, B2, state=state, bias=bias2)
             elif not transpose[0] and not transpose[1]:
                 out_torch = funcs[0](A, B)
-                out_bnb = funcs[1](A, B2.t(), state=state)
+                out_bnb = funcs[1](A, B2.t(), state=state, bias=bias2)
+
+            if has_bias:
+                out_torch += bias
 
             n = out_bnb.numel()
             err = torch.abs(out_bnb - out_torch).mean().item()
@@ -367,6 +385,9 @@ def test_matmullt(
                     gradB1 = B.grad
                     A.grad = None
                     B.grad = None
+                    if has_bias:
+                        gradBias1 = bias.grad
+                        bias.grad = None
 
                     loss_torch = torch.nn.functional.mse_loss(
                         out_torch, target
@@ -376,6 +397,9 @@ def test_matmullt(
                     gradB2 = B.grad
                     A.grad = None
                     B.grad = None
+                    if has_bias:
+                        gradBias2 = bias.grad
+                        bias.grad = None
 
                 if req_grad[0]:
                     torch.testing.assert_allclose(
@@ -397,3 +421,6 @@ def test_matmullt(
                     torch.testing.assert_allclose(
                         gradB1, gradB2, atol=0.18, rtol=0.3
                     )
+
+                if req_grad[2]:
+                    torch.testing.assert_allclose(gradBias1, gradBias2)