Fix edge cases in (de)serialize_torch_tensor

.github/workflows/run-benchmarks.yml

@@ -28,7 +28,7 @@ jobs:
           pip install -r requirements-dev.txt
       - name: Build bitsandbytes
         run: |
-          pip install bitsandbytes==0.37.0
+          pip install bitsandbytes==0.41.1
       - name: Build hivemind
         run: |
           pip install .

.github/workflows/run-tests.yml

@@ -31,7 +31,7 @@ jobs:
           pip install -r requirements-dev.txt
       - name: Build bitsandbytes
         run: |
-          pip install bitsandbytes==0.37.0
+          pip install bitsandbytes==0.41.1
       - name: Build hivemind
         run: |
           pip install .
@@ -93,7 +93,7 @@ jobs:
           pip install -r requirements-dev.txt
       - name: Build bitsandbytes
         run: |
-          pip install bitsandbytes==0.37.0
+          pip install bitsandbytes==0.41.1
       - name: Build hivemind
         run: |
           pip install -e . --no-use-pep517

hivemind/compression/base.py

@@ -82,6 +82,7 @@ class NoCompression(CompressionBase):
     compression_type = runtime_pb2.CompressionType.NONE
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
+        requires_grad = tensor.requires_grad
         tensor = tensor.detach()
         shape = tensor.shape
         dtype_name = str(tensor.dtype).replace("torch.", "")
@@ -98,7 +99,7 @@ def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: b
             buffer=raw_data.numpy().tobytes(),
             size=shape,
             dtype=dtype_name,
-            requires_grad=tensor.requires_grad,
+            requires_grad=requires_grad,
         )
 
     def extract(self, serialized_tensor: runtime_pb2.Tensor) -> torch.Tensor:

hivemind/compression/floating.py

@@ -12,22 +12,29 @@
     FP16_MIN, FP16_MAX = torch.finfo(torch.float16).min, torch.finfo(torch.float16).max
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
+        if not torch.is_floating_point(tensor) or tensor.dtype == torch.bfloat16:
+            raise ValueError(f"{self.__class__.__name__} does not support {tensor.dtype} tensors")
+        requires_grad = tensor.requires_grad
+        tensor = tensor.detach().cpu()
         dtype_name = tensor.numpy().dtype.name
-        tensor = tensor.detach().cpu().float()
-        tensor = tensor if allow_inplace else tensor.clone()
+        tensor = tensor.to(torch.float32, copy=not allow_inplace)
         tensor = tensor.clamp_(self.FP16_MIN, self.FP16_MAX).to(torch.float16)
         return runtime_pb2.Tensor(
             compression=self.compression_type,
             buffer=tensor.numpy().tobytes(),
             size=tensor.shape,
             dtype=dtype_name,
-            requires_grad=tensor.requires_grad,
+            requires_grad=requires_grad,
         )
 
     def extract(self, serialized_tensor: runtime_pb2.Tensor) -> torch.Tensor:
         original_dtype = np.dtype(serialized_tensor.dtype)
         array = np.frombuffer(serialized_tensor.buffer, dtype=np.float16)
-        return torch.as_tensor(np.asarray(array, dtype=original_dtype)).reshape(tuple(serialized_tensor.size))
+        return (
+            torch.as_tensor(np.asarray(array, dtype=original_dtype))
+            .reshape(tuple(serialized_tensor.size))
+            .requires_grad_(serialized_tensor.requires_grad)
+        )
 
     def estimate_compression_ratio(self, info: CompressionInfo) -> float:
         return 16.0 / get_num_bits(info.descriptor.dtype)
@@ -41,9 +48,12 @@
     FP32_EPS = torch.finfo(torch.float32).eps
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
+        if not torch.is_floating_point(tensor) or tensor.dtype == torch.bfloat16:
+            raise ValueError(f"{self.__class__.__name__} does not support {tensor.dtype} tensors")
+        requires_grad = tensor.requires_grad
+        tensor = tensor.detach().cpu()
         dtype_name = tensor.numpy().dtype.name
-        tensor = tensor.detach().cpu().float()
-        tensor = tensor if allow_inplace else tensor.clone()
+        tensor = tensor.to(dtype=torch.float32, copy=not allow_inplace)
         means = torch.mean(tensor, dim=-1, keepdim=True)
         tensor.sub_(means)
         stds = tensor.norm(dim=-1, keepdim=True) / math.sqrt(tensor.shape[-1])
@@ -58,7 +68,7 @@
             buffer=data,
             size=tensor.shape,
             dtype=dtype_name,
-            requires_grad=tensor.requires_grad,
+            requires_grad=requires_grad,
         )
 
     def extract(self, serialized_tensor: runtime_pb2.Tensor) -> torch.Tensor:
@@ -77,7 +87,8 @@
         tensor = torch.as_tensor(np.asarray(array, dtype=serialized_tensor.dtype)).reshape(
             list(serialized_tensor.size)
         )
-        return tensor.mul_(stds).add_(means)
+        dtype = getattr(torch, serialized_tensor.dtype)
+        return tensor.mul_(stds).add_(means).to(dtype).requires_grad_(serialized_tensor.requires_grad)
 
 
 def get_num_bits(dtype: torch.dtype) -> int:

hivemind/compression/quantization.py

@@ -25,12 +25,14 @@
         ...
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
+        if not torch.is_floating_point(tensor):
+            raise ValueError(f"{self.__class__.__name__} does not support {tensor.dtype} tensors")
         quantized, codebook = self.quantize(tensor.detach(), allow_inplace=allow_inplace)
         return runtime_pb2.Tensor(
             compression=self.compression_type,
             buffer=b"".join((np.int64(len(codebook)).tobytes(), codebook.tobytes(), quantized.tobytes())),
             size=tensor.shape,
-            dtype=tensor.numpy().dtype.name,
+            dtype=tensor.data.numpy().dtype.name if tensor.dtype != torch.bfloat16 else "bfloat16",
             requires_grad=tensor.requires_grad,
         )
 
@@ -39,8 +41,8 @@
         codebook = np.frombuffer(serialized_tensor.buffer, offset=8, count=codebook_size, dtype=self.codebook_dtype)
         quantized = np.frombuffer(serialized_tensor.buffer, offset=8 + codebook.nbytes, dtype=self.indices_dtype)
         quantized = torch.as_tensor(quantized, dtype=torch.int64).reshape(tuple(serialized_tensor.size))
-        codebook = torch.as_tensor(np.asarray(codebook, dtype=serialized_tensor.dtype))
-        return codebook[quantized]
+        codebook = torch.as_tensor(codebook).to(dtype=getattr(torch, serialized_tensor.dtype))
+        return codebook[quantized].requires_grad_(serialized_tensor.requires_grad)
 
     def estimate_compression_ratio(self, info: CompressionInfo) -> float:
         return self.n_bits / torch.finfo(info.descriptor.dtype).bits
@@ -59,8 +61,10 @@
     compression_type = runtime_pb2.UNIFORM_8BIT
 
     def quantize(self, tensor: torch.Tensor, allow_inplace: bool = False) -> Tuple[np.ndarray, np.ndarray]:
+        assert torch.is_floating_point(tensor)
         offset = self.n_bins // 2
         shift = tensor.mean()
+        tensor = tensor.to(dtype=torch.float32, copy=not allow_inplace)
         centered_tensor = tensor.sub_(shift) if allow_inplace else tensor - shift
         std_unbiased = centered_tensor.norm() / math.sqrt(centered_tensor.numel() - 1)
         scale = self.RANGE_IN_SIGMAS * std_unbiased / self.n_bins
@@ -125,6 +129,7 @@
 class BlockwiseQuantization(Quantization):
     compression_type = runtime_pb2.BLOCKWISE_8BIT
     codebook_dtype, indices_dtype = np.float32, np.uint8
+    EXTRA_PARAMS = (4096, False, torch.float32, None, None)
 
     def quantize(
         self, tensor: torch.Tensor, allow_inplace: bool = False
@@ -135,14 +140,15 @@
         except ImportError:
             raise ImportError(BNB_MISSING_MESSAGE)
 
-        quantized, (absmax, codebook) = quantize_blockwise(tensor)
+        quantized, (absmax, codebook, *extra_params) = quantize_blockwise(tensor, blocksize=4096, nested=False)
+        assert tuple(extra_params) == self.EXTRA_PARAMS  # blocksize, nested, dtype, offset, state2
         return quantized.numpy(), (absmax.numpy(), codebook.numpy())
 
     def compress(self, tensor: torch.Tensor, info: CompressionInfo, allow_inplace: bool = False) -> runtime_pb2.Tensor:
+        requires_grad = tensor.requires_grad
         tensor = tensor.detach()
         dtype_name = str(tensor.dtype).replace("torch.", "")
-        if tensor.dtype == torch.bfloat16:
-            tensor = tensor.to(torch.float32)
+        tensor = tensor.to(torch.float32)
 
         quantized, (absmax, codebook) = self.quantize(tensor, allow_inplace=allow_inplace)
 
@@ -157,7 +163,7 @@
         return runtime_pb2.Tensor(
             buffer=b"".join(serialized_data),
             size=tensor.shape,
-            requires_grad=tensor.requires_grad,
+            requires_grad=requires_grad,
             dtype=dtype_name,
             compression=self.compression_type,
         )
@@ -181,6 +187,5 @@
         absmax = torch.as_tensor(absmax)
         codebook = torch.as_tensor(codebook)
         quantized = torch.as_tensor(quantized).reshape(tuple(serialized_tensor.size))
-        result = dequantize_blockwise(quantized, (absmax, codebook))  # Always returns a float32 tensor
-        result = result.to(dtype=getattr(torch, serialized_tensor.dtype))
-        return result
+        result = dequantize_blockwise(quantized, (absmax, codebook, *self.EXTRA_PARAMS))
+        return result.to(getattr(torch, serialized_tensor.dtype)).requires_grad_(serialized_tensor.requires_grad)

setup.py

@@ -156,7 +156,7 @@ def run(self):
 with open("requirements-docs.txt") as docs_requirements_file:
     extras["docs"] = list(map(str, parse_requirements(docs_requirements_file)))
 
-extras["bitsandbytes"] = ["bitsandbytes~=0.37.0"]
+extras["bitsandbytes"] = ["bitsandbytes~=0.41.1"]
 
 extras["all"] = extras["dev"] + extras["docs"] + extras["bitsandbytes"]
 

tests/test_compression.py

@@ -52,22 +52,57 @@ def _check(tensor, compression, rtol=1e-5, atol=1e-8, chunk_size=30 * 1024):
     assert len(chunks) == max((len(serialized_tensor.buffer) - 1) // chunk_size + 1, 1)
     restored = combine_from_streaming(chunks)
     result = deserialize_torch_tensor(restored)
+    assert result.dtype == tensor.dtype, compression
+    assert result.requires_grad == tensor.requires_grad
     assert torch.allclose(result, tensor, rtol=rtol, atol=atol)
-    assert result.dtype == tensor.dtype
 
 
 @pytest.mark.forked
 def test_serialize_tensor():
-    tensor = torch.randn(512, 12288)
+    tensor = torch.randn(512, 12288, requires_grad=True)
     for chunk_size in [1024, 64 * 1024, 64 * 1024 + 1, 10**9]:
         _check(tensor, CompressionType.NONE, chunk_size=chunk_size)
 
     _check(tensor, CompressionType.FLOAT16, rtol=0.0, atol=1e-2)
     _check(torch.randint(0, 100, (512, 1, 1)), CompressionType.NONE)
+    _check(torch.randn(10, 20), CompressionType.MEANSTD_16BIT, atol=0.1)
     _check(torch.tensor(1.0), CompressionType.NONE)
     _check(torch.tensor(1.0), CompressionType.FLOAT16)
 
 
+@pytest.mark.parametrize(
+    "dtype",
+    [
+        torch.float32,
+        torch.float16,
+        torch.bfloat16,
+        torch.float64,
+        torch.complex64,
+        torch.int64,
+        torch.int32,
+        torch.uint8,
+        torch.bool,
+    ],
+)
+@pytest.mark.parametrize("requires_grad", [False, True])
+@pytest.mark.forked
+def test_serialize_tensor_properties(dtype: torch.dtype, requires_grad: bool):
+    tensor = torch.randn(123, 45, requires_grad=requires_grad).to(dtype)
+    if dtype == torch.bfloat16:
+        compression_types = [
+            type
+            for type in CompressionType.values()
+            if type not in (CompressionType.FLOAT16, CompressionType.MEANSTD_16BIT)
+        ]
+    elif torch.is_floating_point(tensor):  # nb: complex and qint data types are not is_floating_point
+        compression_types = CompressionType.values()
+    else:
+        compression_types = [CompressionType.NONE]
+
+    for compression_type in compression_types:
+        _check(tensor, compression_type, atol=float("inf"))
+
+
 @pytest.mark.parametrize("use_legacy_bfloat16", [True, False])
 @pytest.mark.parametrize("tensor_size", [(4096, 16), (0, 0)])
 @pytest.mark.forked