Merge branch 'main' into fix/libcuda-to-torch

deploy.sh

@@ -139,17 +139,6 @@ if [ ! -f "./bitsandbytes/libbitsandbytes_cuda121.so" ]; then
 fi
 
 
-make clean
-export CUDA_HOME=$BASE_PATH/cuda-10.2
-make cuda10x_nomatmul CUDA_VERSION=102
-
-if [ ! -f "./bitsandbytes/libbitsandbytes_cuda102_nocublaslt.so" ]; then
-  # Control will enter here if $DIRECTORY doesn't exist.
-  echo "Compilation unsuccessul!" 1>&2
-  exit 64
-fi
-
-
 make clean
 export CUDA_HOME=$BASE_PATH/cuda-11.0
 make cuda110_nomatmul CUDA_VERSION=110

examples/int8_inference_huggingface.py

+import torch
+from transformers import AutoModelForCausalLM, AutoTokenizer
+
+MAX_NEW_TOKENS = 128
+model_name = 'decapoda-research/llama-7b-hf'
+
+text = 'Hamburg is in which country?\n'
+tokenizer = AutoTokenizer.from_pretrained(model_name)
+input_ids = tokenizer(text, return_tensors="pt").input_ids
+
+free_in_GB = int(torch.cuda.mem_get_info()[0]/1024**3)
+max_memory = f'{int(torch.cuda.mem_get_info()[0]/1024**3)-2}GB'
+
+n_gpus = torch.cuda.device_count()
+max_memory = {i: max_memory for i in range(n_gpus)}
+
+model = AutoModelForCausalLM.from_pretrained(
+  model_name,
+  device_map='auto',
+  load_in_8bit=True,
+  max_memory=max_memory
+)
+generated_ids = model.generate(input_ids, max_length=MAX_NEW_TOKENS)
+print(tokenizer.decode(generated_ids[0], skip_special_tokens=True))
+
+
+

setup.py

@@ -18,10 +18,10 @@ def read(fname):
 
 setup(
     name=f"bitsandbytes",
-    version=f"0.38.0",
+    version=f"0.39.1",
     author="Tim Dettmers",
     author_email="dettmers@cs.washington.edu",
-    description="8-bit optimizers and matrix multiplication routines.",
+    description="k-bit optimizers and matrix multiplication routines.",
     license="MIT",
     keywords="gpu optimizers optimization 8-bit quantization compression",
     url="https://github.com/TimDettmers/bitsandbytes",

tests/test_autograd.py

@@ -97,7 +97,7 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 B.grad = None
 
             if req_grad[0]:
-                torch.testing.assert_allclose(
+                torch.testing.assert_close(
                     gradA1, gradA2, atol=0.015, rtol=0.1
                 )
             if req_grad[1]:
@@ -106,7 +106,7 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 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
-                torch.testing.assert_allclose(
+                torch.testing.assert_close(
                     gradB1, gradB2, atol=0.18, rtol=0.3
                 )
 
@@ -135,7 +135,7 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
             n = out_bnb.numel()
             idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
             assert (idx == 0).sum().item() < n * 0.01
-            torch.testing.assert_allclose(
+            torch.testing.assert_close(
                 out_bnb, out_torch, atol=0.027, rtol=0.2
             )
 
@@ -159,7 +159,7 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 B.grad = None
 
             if req_grad[0]:
-                torch.testing.assert_allclose(
+                torch.testing.assert_close(
                     gradA1, gradA2, atol=0.015, rtol=0.1
                 )
             if req_grad[1]:
@@ -218,7 +218,7 @@ def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
                 B.grad = None
 
             if req_grad[0]:
-                torch.testing.assert_allclose(
+                torch.testing.assert_close(
                     gradA1, gradA2, atol=0.015, rtol=0.1
                 )
             if req_grad[1]:
@@ -239,8 +239,8 @@ dim4 = torch.randint(32, 96, size=(n,)).tolist()
 dim2.append(0)
 
 decomp = [0.0, 6.0]
-funcs = [(torch.matmul, bnb.matmul)]
-str_funcs = ["matmul"]
+funcs = [(torch.matmul, bnb.matmul), (torch.matmul, bnb.research.switchback_bnb)]
+str_funcs = ["matmullt", 'switchback_bnb']
 req_grad = [(False, False), (True, False), (True, True), (False, True)]
 req_grad = list(product([True, False], repeat=3))
 req_grad_str = []
@@ -407,7 +407,7 @@ def test_matmullt(
                         bias.grad = None
 
                 if req_grad[0]:
-                    torch.testing.assert_allclose(
+                    torch.testing.assert_close(
                         gradA1, gradA2, atol=0.015, rtol=0.1
                     )
                 if req_grad[1]:
@@ -423,9 +423,204 @@ def test_matmullt(
                     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
-                    torch.testing.assert_allclose(
+                    torch.testing.assert_close(
                         gradB1, gradB2, atol=0.18, rtol=0.3
                     )
 
                 if req_grad[2]:
-                    torch.testing.assert_allclose(gradBias1, gradBias2)
+                    torch.testing.assert_close(gradBias1, gradBias2)
+
+
+n = 1
+k = 3
+dim1 = torch.randint(16, 64, size=(n,)).tolist()
+dim2 = torch.randint(32, 96, size=(n,)).tolist()
+dim3 = torch.randint(32, 96, size=(n,)).tolist()
+dim4 = torch.randint(32, 96, size=(n,)).tolist()
+
+dim2.append(0)
+
+funcs = [(torch.matmul, bnb.matmul_4bit)]
+str_funcs = ["matmul"]
+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, torch.float32]
+compress_statistics = [False, True]
+has_fp16_weights = [True, False]
+has_bias = [True, False]
+quant_type = ['fp4', 'nf4']
+values = list(product(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias, compress_statistics, quant_type))
+str_values = list(product(dim1, dim2, dim3, dim4, str_funcs, dtype, req_grad_str, str_transpose, has_bias, compress_statistics, quant_type))
+names = ["dim1_{}_dim2_{}_dim3_{}_dim4_{}_func_{}_dtype_{}_requires_grad_{}_transpose_{}_has_bias_{}_compress_statistics_{}_quant_type_{}".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, compress_statistics, quant_type", values, ids=names)
+def test_matmul_4bit( dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, has_bias, compress_statistics, quant_type):
+    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
+
+    for i in range(k):
+        # normal multiply
+        if funcs[0] in [torch.mm, torch.matmul]:
+            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_4bit(B, compress_statistics=compress_statistics, quant_type=quant_type)
+
+            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)
+            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
+
+            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()
+                loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
+                loss_bnb.backward()
+                gradA1 = A.grad
+                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()
+                gradA2 = A.grad
+                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_close( gradA1, gradA2, atol=0.015, rtol=0.1)
+
+                if req_grad[2]:
+                    torch.testing.assert_close(gradBias1, gradBias2)
+
+
+funcs = [(torch.matmul, bnb.research.matmul_fp8_mixed), (torch.matmul, bnb.research.matmul_fp8_global)]
+str_funcs = ["matmul_fp8_mixed", 'matmul_fp8_global']
+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, torch.float32]
+has_fp16_weights = [True, False]
+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", 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)
+    req_grad = list(req_grad)
+    req_grad[2] = False
+
+    for i in range(k):
+        # normal multiply
+        if funcs[0] in [torch.mm, torch.matmul]:
+            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)
+
+            torch.nn.init.xavier_uniform_(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, 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, 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()
+                loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
+                loss_bnb.backward()
+                gradA1 = A.grad
+                gradB1 = B.grad
+                A.grad = None
+                B.grad = None
+
+                loss_torch = torch.nn.functional.mse_loss( out_torch, target ).mean()
+                loss_torch.backward()
+                gradA2 = A.grad
+                gradB2 = B.grad
+                A.grad = None
+                B.grad = None
+
+                if req_grad[0]:
+                    torch.testing.assert_close( gradA1, gradA2, atol=0.015, rtol=0.1)
+
+                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_close(
+                        gradB1, gradB2, atol=0.18, rtol=0.3
+                    )
+

tests/test_modules.py

@@ -44,7 +44,7 @@ def assert_all_approx_close(a, b, atol=1e-8, rtol=1e-5, count=10):
     sumval = (idx == 0).sum().item()
     if sumval > count:
         print(f"Too many values not close: assert {sumval} < {count}")
-        torch.testing.assert_allclose(a, b, rtol, atol)
+        torch.testing.assert_close(a, b, rtol, atol)
 
 
 class LinearFunction(torch.autograd.Function):
@@ -330,18 +330,15 @@ def test_linear8bitlt_inference(threshold):
 
 
 def test_linear8bitlt_accumulated_gradient():
-    l1 = torch.nn.Sequential(
-        *[bnb.nn.Linear8bitLt(32, 32).cuda().half() for i in range(2)]
-    )
-    l2 = torch.nn.Sequential(
-        *[torch.nn.Linear(32, 32).cuda().half() for i in range(2)]
-    )
-    l2[0].weight = torch.nn.Parameter(l1[0].weight.clone())
-    l2[0].bias = torch.nn.Parameter(l1[0].bias.clone())
-    l2[1].weight = torch.nn.Parameter(l1[1].weight.clone())
-    l2[1].bias = torch.nn.Parameter(l1[1].bias.clone())
-    opt1 = bnb.optim.Adam8bit(l1.parameters(), lr=0.001)
-    opt2 = bnb.optim.Adam8bit(l2.parameters(), lr=0.001)
+    l1 = torch.nn.Sequential(*[bnb.nn.Linear8bitLt(32, 32).cuda().half() for i in range(2)])
+    l2 = torch.nn.Sequential(*[torch.nn.Linear(32, 32).cuda().half() for i in range(2)])
+    l1[0].weight.data.copy_(l2[0].weight.data)
+    l1[1].weight.data.copy_(l2[1].weight.data)
+    l1[0].bias.data.copy_(l2[0].bias.data)
+    l1[1].bias.data.copy_(l2[1].bias.data)
+
+    opt1 = bnb.optim.Adam32bit(l1.parameters(), lr=0.001)
+    opt2 = bnb.optim.Adam32bit(l2.parameters(), lr=0.001)
 
     acc_steps = 10
 
@@ -371,26 +368,17 @@ def test_linear8bitlt_accumulated_gradient():
             # we do this copy because otherwise we have small divergences over time that add up
             l1[0].weight.data.copy_(l2[0].weight.data)
             l1[1].weight.data.copy_(l2[1].weight.data)
+            l1[0].bias.data.copy_(l2[0].bias.data)
+            l1[1].bias.data.copy_(l2[1].bias.data)
         else:
-            torch.testing.assert_allclose(l1[0].weight.grad, l2[0].weight.grad)
-            torch.testing.assert_allclose(l1[1].weight.grad, l2[1].weight.grad)
-
-
-threshold = [0.0, 2.0]
-values = threshold
-names = [f"threshold_{vals}" for vals in values]
+            torch.testing.assert_close(l1[0].weight.grad, l2[0].weight.grad, atol=1e-3, rtol=1e-3)
+            torch.testing.assert_close(l1[1].weight.grad, l2[1].weight.grad, atol=1e-3, rtol=1e-3)
 
 
-@pytest.mark.parametrize("threshold", values, ids=names)
+@pytest.mark.parametrize("threshold", [0.0, 2.0])
 @pytest.mark.parametrize("memory_efficient_backward", [False])
 def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
-    l1 = (
-        bnb.nn.Linear8bitLt(
-            32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward
-        )
-        .cuda()
-        .half()
-    )
+    l1 = (bnb.nn.Linear8bitLt( 32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward).cuda().half())
     assert l1.weight.dtype == torch.int8
 
     l1.eval()
@@ -446,13 +434,7 @@ def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
     assert mlp.fc1.weight.dtype == torch.int8
     assert mlp.fc2.weight.dtype == torch.int8
 
-    mlp = (
-        MLP8bit(
-            32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward
-        )
-        .half()
-        .to("cuda")
-    )
+    mlp = ( MLP8bit( 32, 64, threshold=threshold, has_fp16_weights=False, memory_efficient_backward=memory_efficient_backward).half().to("cuda"))
 
     for i in range(100):
         b1 = torch.randn(16, 8, 32, device="cuda").half()
@@ -499,15 +481,16 @@ def test_linear8bitlt_no_fp16_weights(threshold, memory_efficient_backward):
         grad_ref = grad_proj.flatten(2) @ w2.half() @ w1.half()
         scale = grad_ref.abs().mean()
 
-        torch.testing.assert_allclose(b1.grad, grad_ref, rtol=0, atol=0.05 * scale)
+        torch.testing.assert_close(b1.grad, grad_ref, rtol=0, atol=0.05 * scale)
         idx = torch.isclose(b1.grad, grad_ref, atol=0.01 * scale, rtol=0.1)
         assert (idx == 0).sum().item() <= b1.numel() * 0.005
 
 
-def test_linear8bitlt_fp32_bias():
+@pytest.mark.parametrize("module", [lambda nin, nout, bias=True: bnb.nn.Linear8bitLt(nin, nout, bias=bias, has_fp16_weights=False), bnb.nn.LinearFP4], ids=['Int8Lt', 'FP4'])
+def test_linear_kbit_fp32_bias(module):
     # casts model to fp16 -> int8 automatically
-    l1 = bnb.nn.Linear8bitLt(32, 64, has_fp16_weights=False).cuda()
-    assert l1.weight.dtype == torch.int8
+    l1 = module(32, 64).cuda()
+    assert l1.weight.dtype in [torch.int8, torch.uint8]
     assert l1.bias.dtype == torch.float32
 
     for i in range(100):
@@ -517,11 +500,116 @@ def test_linear8bitlt_fp32_bias():
         assert l1.bias.dtype == torch.float16
 
     # casts model to fp16 -> int8 automatically
-    l1 = bnb.nn.Linear8bitLt(32, 64, has_fp16_weights=False, bias=False).cuda()
-    assert l1.weight.dtype == torch.int8
+    l1 = module(32, 64, bias=False).cuda()
+    assert l1.weight.dtype in [torch.int8, torch.uint8]
     assert l1.bias is None
 
     for i in range(100):
         b1 = torch.randn(16, 8, 32, device="cuda").half()
         o1 = l1(b1)
         assert l1.bias is None
+
+modules = []
+modules.append(bnb.nn.Linear8bitLt)
+modules.append(bnb.nn.Linear4bit)
+modules.append(bnb.nn.LinearFP4)
+modules.append(bnb.nn.LinearNF4)
+modules.append(lambda d1, d2: bnb.nn.LinearFP4(d1, d2, compress_statistics=True))
+modules.append(lambda d1, d2: bnb.nn.LinearNF4(d1, d2, compress_statistics=True))
+names = ['Int8Lt', '4bit', 'FP4', 'NF4', 'FP4+C', 'NF4+C']
+@pytest.mark.skipif(not torch.cuda.is_available(), reason="this test requires a GPU")
+@pytest.mark.parametrize("module", modules, ids=names)
+def test_kbit_backprop(module):
+    b = 17
+    dim1 = 37
+    dim2 = 83
+
+    ref = nn.Sequential(*[torch.nn.Linear(dim1, dim2), torch.nn.Linear(dim2, 10)])
+    ref[1].weight.requires_grad = False
+    torch.nn.init.kaiming_normal_(ref[0].weight)
+    torch.nn.init.kaiming_normal_(ref[1].weight)
+    kbit = nn.Sequential(*[torch.nn.Linear(dim1, dim2), module(dim2, 10)])
+    kbit[0].weight.detach().copy_(ref[0].weight)
+    kbit[1].weight.detach().copy_(ref[1].weight)
+    kbit[0].bias.detach().copy_(ref[0].bias)
+    kbit[1].bias.detach().copy_(ref[1].bias)
+    ref = ref.half().cuda()
+    kbit = kbit.half().cuda()
+
+    errs1 = []
+    errs2 = []
+    relerrs1 = []
+    relerrs2 = []
+    for i in range(100):
+        batch = torch.randn(b, dim1).half().cuda()
+        out1 = ref(batch)
+        out2 = kbit(batch)
+        out1.mean().backward()
+        out2.mean().backward()
+
+        grad1 = ref[0].weight.grad
+        grad2 = kbit[0].weight.grad
+        bgrad1 = ref[0].bias.grad
+        bgrad2 = kbit[0].bias.grad
+
+        err1 = (out1-out2).abs().float()
+        err2 = (grad1-grad2).abs().float()
+        relerr1 = (err1/(out1.abs().float()+1e-9))
+        relerr2 = (err2/(grad1.abs().float()+1e-9))
+        errs1.append(err1.mean().item())
+        errs2.append(err2.mean().item())
+        relerrs1.append(relerr1.mean().item())
+        relerrs2.append(relerr2.mean().item())
+
+        if isinstance(module, bnb.nn.Linear8bitLt):
+            torch.testing.assert_close(grad1, grad2, atol=0.008, rtol=0.05)
+            torch.testing.assert_close(bgrad1, bgrad2, atol=0.008, rtol=0.05)
+        else:
+            torch.testing.assert_close(grad1, grad2, atol=0.015, rtol=0.05)
+            torch.testing.assert_close(bgrad1, bgrad2, atol=0.02, rtol=0.05)
+        ref.zero_grad()
+        kbit.zero_grad()
+
+        assert kbit[0].weight.grad is None or kbit[0].weight.grad.sum().item() == 0
+        assert kbit[0].weight.grad is None or kbit[0].bias.grad.sum().item() == 0
+    print('out', sum(errs1)/len(errs1))
+    print('grad', sum(errs2)/len(errs2))
+    print('rel out', sum(relerrs1)/len(relerrs1))
+    print('rel grad', sum(relerrs2)/len(relerrs2))
+
+def test_fp8linear():
+
+    b = 10
+    h = 1024
+    inp = torch.randn(b, h).cuda()
+    fp32 = torch.nn.Linear(h, h*2).cuda()
+    fp8 = bnb.research.nn.LinearFP8Mixed(h, h*2).cuda()
+    fp32b = torch.nn.Linear(h*2, h).cuda()
+    fp8b = bnb.research.nn.LinearFP8Mixed(h*2, h).cuda()
+
+    fp8.weight.data.copy_(fp32.weight.data)
+    fp8.bias.data.copy_(fp32.bias.data)
+    fp8b.weight.data.copy_(fp32b.weight.data)
+    fp8b.bias.data.copy_(fp32b.bias.data)
+
+    a = fp32b(torch.nn.functional.gelu(fp32(inp)))
+    b = fp8b(torch.nn.functional.gelu(fp8(inp)))
+
+    err = (a-b).abs().mean()
+
+    a.mean().backward()
+    b.mean().backward()
+
+    graderr = (fp8.weight.grad-fp32.weight.grad).abs().mean()
+    bgraderr = (fp8.bias.grad-fp32.bias.grad).abs().mean()
+
+    assert err < 0.05
+    assert graderr < 0.00002
+    assert bgraderr < 0.00002
+
+
+
+
+
+
+

tests/test_triton.py

+import pytest
+import torch
+
+from bitsandbytes.triton.triton_utils import is_triton_available
+from bitsandbytes.nn.triton_based_modules import SwitchBackLinear
+from bitsandbytes.nn import Linear8bitLt
+
+@pytest.mark.skipif(not is_triton_available() or not torch.cuda.is_available() or not torch.cuda.get_device_capability()[0] >= 8,
+                    reason="This test requires triton and a GPU with compute capability 8.0 or higher.")
+@pytest.mark.parametrize("vector_wise_quantization", [False, True])
+def test_switchback(vector_wise_quantization):
+    for dim in [83]:
+        for batch in [13]:
+
+            standard = torch.nn.Linear(dim, 4 * dim).cuda().half()
+            switchback = SwitchBackLinear(dim, 4 * dim, vector_wise_quantization=vector_wise_quantization).cuda().half()
+            baseline = Linear8bitLt(dim, 4 * dim).cuda().half()
+            switchback.weight.data.copy_(standard.weight)
+            switchback.bias.data.copy_(standard.bias)
+            baseline.weight.data.copy_(standard.weight)
+            baseline.bias.data.copy_(standard.bias)
+
+            x1 = torch.randn(batch, dim).cuda().half().requires_grad_(True)
+            x2 = x1.clone().detach().requires_grad_(True)
+            x3 = x1.clone().detach().requires_grad_(True)
+
+            out_standard = standard(x1)
+            (2**10 * out_standard.abs().mean()).backward()
+
+            print(x2.dtype)
+            out_sb = switchback(x2)
+            (2**10 * out_sb.abs().mean()).backward()
+
+            out_baseline = baseline(x3)
+            (2**10 * out_baseline.abs().mean()).backward()
+
+            err_sb = (out_standard - out_sb).abs().mean()
+            err_baseline = (out_standard - out_baseline).abs().mean()
+            print('OUT', err_sb, err_baseline)
+            assert err_sb < 2 * err_baseline
+
+            err_sb = (standard.bias.grad - switchback.bias.grad).abs().mean()
+            err_baseline = (standard.bias.grad - baseline.bias.grad).abs().mean()
+
+            print('GW2', err_sb,  err_baseline)
+            assert err_sb < 2 * err_baseline
+
+            err_sb = (standard.weight.grad - switchback.weight.grad).abs().mean()
+            err_baseline = (standard.weight.grad - baseline.weight.grad).abs().mean()
+
+            print('GW1', err_sb,  err_baseline)
+            assert err_sb < 2 * err_baseline
+
+            err_sb = (x1.grad - x2.grad).abs().mean()
+            err_baseline = (x1.grad - x3.grad).abs().mean()
+
+            print('GX1', err_sb, err_baseline)
+            assert err_sb < 2 * err_baseline
+