Added forward/backward tests; removed bias.

bitsandbytes/autograd/_functions.py

@@ -395,15 +395,14 @@ class MatMulFP8(torch.autograd.Function):
     # backward is mostly the same, but adds one extra clause (see "elif state.CxB is not None")
 
     @staticmethod
-    def forward(ctx, A, B, out=None, bias=None, fw_code=None, bw_code=None):
+    def forward(ctx, A, B, out=None, fw_code=None, bw_code=None):
         # default of 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
-            B_shape = state[1]
+            B_shape = B.shape
             if A.shape[-1] == B_shape[0]:
                 return torch.empty(A.shape[:-1] + B_shape[1:], dtype=A.dtype, device=A.device)
             else:
@@ -414,17 +413,17 @@ class MatMulFP8(torch.autograd.Function):
         # 2. MatmulnN
 
         cA, state = F.quantize_blockwise(A, code=fw_code, blocksize=1024)
-        fp8A = F.dequantize_blockwise(cA, state)
+        fp8A = F.dequantize_blockwise(cA, state).to(A.dtype)
 
         cB, state = F.quantize_blockwise(B, code=fw_code, blocksize=1024)
-        fp8B = F.dequantize_blockwise(cB, state)
+        fp8B = F.dequantize_blockwise(cB, state).to(B.dtype)
 
-        output = torch.nn.functional.linear(fp8A, fp8B)
+        output = torch.matmul(fp8A, fp8B)
 
 
         # 3. Save state
         ctx.bw_code = bw_code
-        ctx.dtype_A, ctx.dtype_B, ctx.dtype_bias = A.dtype, B.dtype, None if bias is None else bias.dtype
+        ctx.dtype_A, ctx.dtype_B = A.dtype, B.dtype
 
         if any(ctx.needs_input_grad[:2]):
             ctx.tensors = (fp8A, fp8B)
@@ -436,21 +435,15 @@ class MatMulFP8(torch.autograd.Function):
     @staticmethod
     def backward(ctx, grad_output):
         if ctx.is_empty:
-            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
+            return torch.zeros_like(ctx.A), torch.zeros_like(ctx.B), None, None, None
 
-        req_gradA, _, _, req_gradBias, _= ctx.needs_input_grad
+        req_gradA, req_gradB, _, _, _ = ctx.needs_input_grad
         fp8A, B = ctx.tensors
-        state = ctx.state
 
-        grad_A, grad_B, grad_bias = None, None, None
+        grad_A, grad_B = None, None
 
-        cgrad_out, state = F.quantize_blockwise(grad_ouput, code=ctx.bw_code, blocksize=1024)
-        fp8out = F.dequantize_blockwise(cgrad_out, state)
-
-        if req_gradBias:
-            # compute grad_bias first before changing grad_output dtype
-            grad_bias = fp8out.sum(0, dtype=ctx.dtype_bias)
+        cgrad_out, state = F.quantize_blockwise(grad_output, code=ctx.bw_code, blocksize=1024)
+        fp8out = F.dequantize_blockwise(cgrad_out, state).to(grad_output.dtype)
 
         # Cast grad_output to fp16
         if len(grad_output.shape) == 3:
@@ -461,7 +454,7 @@ class MatMulFP8(torch.autograd.Function):
         if req_gradA: grad_A = torch.matmul(fp8out, B.t())
         if req_gradB: grad_B = torch.matmul(fp8A.t(), fp8out)
 
-        return grad_A, grad_B, None, grad_bias, None, None
+        return grad_A, grad_B, None, None, None
 
 
 def matmul(
@@ -478,9 +471,8 @@ def matmul(
     return MatMul8bitLt.apply(A, B, out, bias, state)
 
 
-def matmul_fp8(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None, bias=None):
-    assert quant_state is not None
-    return MatMulFP8.apply(A, B, out, bias, fw_code, bw_code)
+def matmul_fp8(A: tensor, B: tensor, fw_code: tensor, bw_code: tensor, out: tensor = None):
+    return MatMulFP8.apply(A, B, out, fw_code, bw_code)
 
 
 def matmul(

bitsandbytes/nn/modules.py

@@ -355,7 +355,9 @@ class LinearFP8(nn.Linear):
             self.bw_code = F.create_fp8_map(True, 5, 2, 8).to(x.device)
             self.fw_code = F.create_fp8_map(True, 4, 3, 8).to(x.device)
 
-        out = bnb.matmul_fp8(x, self.weight.t(), bias=self.bias, fw_code=self.fw_code, code=self.bw_code)
+        out = bnb.matmul_fp8(x, self.weight.t(), fw_code=self.fw_code, code=self.bw_code)
+        if self.bias is not None:
+            out += self.bias
 
         return out
 

tests/test_autograd.py

@@ -456,18 +456,16 @@ transpose = [(False, True), (False, False)]
 str_transpose = ["NT", "NN"]
 dtype = [torch.float16, torch.float32]
 has_fp16_weights = [True, False]
-has_bias = [True, False]
-values = list(product(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias))
-str_values = list(product(dim1, dim2, dim3, dim4, str_funcs, dtype, req_grad_str, str_transpose, has_bias))
-names = ["dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}_has_bias_{}".format(*vals) for vals in str_values]
+values = list(product(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose))
+str_values = list(product(dim1, dim2, dim3, dim4, str_funcs, dtype, req_grad_str, str_transpose))
+names = ["dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}".format(*vals) for vals in str_values]
 @pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
-@pytest.mark.parametrize( "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias", values, ids=names)
-def test_matmul_fp8( dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias):
+@pytest.mark.parametrize( "dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose", values, ids=names)
+def test_matmul_fp8( dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
     dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
     dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
-    if has_bias == False:
-        req_grad = list(req_grad)
-        req_grad[2] = False
+    req_grad = list(req_grad)
+    req_grad[2] = False
 
     for i in range(k):
         # normal multiply
@@ -475,32 +473,24 @@ def test_matmul_fp8( dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose,
             A = torch.randn(size=dimA, device="cuda", requires_grad=req_grad[0], dtype=dtype)
             B = torch.randn(size=dimB, device="cuda", requires_grad=req_grad[1], dtype=dtype)
             target = torch.randn(size=(dim2, dim4), device="cuda", 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, quant_state = bnb.functional.quantize_fp8(B)
+            fw_code = bnb.functional.create_fp8_map(True, 4, 3, 8).to(A.device)
+            bw_code = bnb.functional.create_fp8_map(True, 5, 2, 8).to(A.device)
 
             if not transpose[0] and transpose[1]:
                 out_torch = funcs[0](A, B.t())
-                out_bnb = funcs[1](A, B2.t(), quant_state, bias=bias2)
+                out_bnb = funcs[1](A, B.t(), fw_code, bw_code)
             elif not transpose[0] and not transpose[1]:
                 out_torch = funcs[0](A, B)
-                out_bnb = funcs[1](A, B2, quant_state, bias=bias2)
-
-            if has_bias:
-                out_torch += bias
+                out_bnb = funcs[1](A, B, fw_code, bw_code)
 
             assert out_bnb.dtype == A.dtype, f"bnb matmullt received {A.dtype} but returned {out_bnb.dtype}"
 
             n = out_bnb.numel()
             err = torch.abs(out_bnb - out_torch).float().mean().item()
             if n > 0:
-                assert err < 0.115
-
+                assert err < 0.20
             if any(req_grad):
                 out_bnb.data.copy_(out_torch)
                 torch.cuda.synchronize()
@@ -510,9 +500,6 @@ def test_matmul_fp8( dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose,
                 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 ).mean()
                 loss_torch.backward()
@@ -520,12 +507,26 @@ def test_matmul_fp8( dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose,
                 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( gradA1, gradA2, atol=0.015, rtol=0.1)
 
-                if req_grad[2]:
-                    torch.testing.assert_allclose(gradBias1, gradBias2)
+                if req_grad[1]:
+                    n = gradB1.numel()
+                    if dim2 > 0:
+                        assert torch.abs(gradB1).sum() > 0.0
+                        assert torch.abs(gradB2).sum() > 0.0
+                    else:
+                        assert torch.abs(gradB1).sum() == 0.0
+                        assert torch.abs(gradB2).sum() == 0.0
+                    idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
+
+                    assert (idx == 0).sum().item() <= n * 0.1
+                    idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
+                    assert (idx == 0).sum().item() <= n * 0.02
+                    grad_err = (gradB1-gradB2).abs().mean()
+                    assert grad_err.item() < 0.003
+                    torch.testing.assert_allclose(
+                        gradB1, gradB2, atol=0.18, rtol=0.3
+                    )
+