Torch backwards compatibility fixes

setup.cfg

@@ -86,7 +86,7 @@ cuda115 =
 datasets =
  ml_datasets>=0.2.0,<0.3.0
 torch =
- torch>=1.5.0
+ torch>=1.6.0
 tensorflow =
  tensorflow>=2.0.0,<2.6.0
 mxnet =

thinc/shims/pytorch.py

@@ -13,6 +13,7 @@
  pass
 
 from ..util import torch2xp, xp2torch, convert_recursive, iterate_recursive
+from ..util import has_torch_amp
 from ..backends import get_current_ops, context_pools, CupyOps
 from ..backends import set_gpu_allocator
 from ..optimizers import Optimizer
@@ -42,6 +43,11 @@ def __init__(
  mixed_precision: bool = False,
  grad_scaler: Optional[PyTorchGradScaler] = None,
  ):
+ if mixed_precision and not has_torch_amp:
+ raise ValueError(
+ "Mixed-precision training is not supported, requires capable GPU and torch>=1.9.0"
+ )
+
  super().__init__(model, config, optimizer)
 
  if grad_scaler is None:

thinc/shims/pytorch_grad_scaler.py

@@ -1,6 +1,6 @@
 from typing import Dict, Iterable, List, Union, cast
 
-from ..util import is_torch_array
+from ..util import has_torch_amp, is_torch_array
 
 try:
  import torch
@@ -23,7 +23,7 @@ class PyTorchGradScaler:
  def __init__(
  self,
  enabled: bool = False,
- init_scale: float = 2.0 ** 16,
+ init_scale: float = 2.0**16,
  backoff_factor: float = 0.5,
  growth_factor: float = 2.0,
  growth_interval: int = 2000,
@@ -50,6 +50,11 @@ def __init__(
  When no overflows were found for this number of steps, the scale will
  be multiplied by "growth_factor".
  """
+ if enabled and not has_torch_amp:
+ raise ValueError(
+ "Gradient scaling is not supported, requires capable GPU and torch>=1.9.0"
+ )
+
  self._enabled = enabled
  self._growth_factor = growth_factor
  self._backoff_factor = backoff_factor

thinc/tests/backends/test_ops.py

@@ -5,9 +5,10 @@
 from hypothesis import given, settings
 from hypothesis.strategies import composite, integers
 from numpy.testing import assert_allclose
+from packaging.version import Version
 from thinc.api import NumpyOps, CupyOps, Ops, get_ops
 from thinc.api import get_current_ops, use_ops
-from thinc.util import has_torch, torch2xp, xp2torch
+from thinc.util import has_torch, torch2xp, xp2torch, torch_version
 from thinc.api import fix_random_seed
 from thinc.api import LSTM
 from thinc.types import Floats2d
@@ -1001,6 +1002,7 @@ def test_ngrams():
 
 
 @pytest.mark.skipif(not has_torch, reason="needs PyTorch")
+@pytest.mark.skipif(torch_version < Version("1.9.0"), reason="needs PyTorch 1.9.0")
 @pytest.mark.parametrize("ops", ALL_OPS)
 @pytest.mark.parametrize("dtype", ["float32", "float64"])
 @pytest.mark.parametrize("torch_func", TORCH_FUNCS)

thinc/tests/layers/test_pytorch_wrapper.py

@@ -80,7 +80,9 @@ def test_pytorch_wrapper_thinc_input(nN, nI, nO, mixed_precision):
  PyTorchWrapper_v2(
  pytorch_layer.cuda(),
  mixed_precision=mixed_precision,
- grad_scaler=PyTorchGradScaler(enabled=True, init_scale=2.0 ** 16),
+ grad_scaler=PyTorchGradScaler(
+ enabled=mixed_precision, init_scale=2.0**16
+ ),
  ).initialize(),
  )
  # pytorch allocator is set in PyTorchShim
@@ -146,3 +148,18 @@ def test_pytorch_convert_inputs(data, n_args, kwargs_keys):
  convert_inputs = model.attrs["convert_inputs"]
  Y, backprop = convert_inputs(model, data, is_train=True)
  check_input_converters(Y, backprop, data, n_args, kwargs_keys, torch.Tensor)
+
+
+@pytest.mark.skipif(not has_torch_gpu, reason="needs PyTorch with CUDA-capable GPU")
+@pytest.mark.skipif(
+ has_torch_amp, reason="needs PyTorch without mixed-precision support"
+)
+def test_raises_on_old_pytorch():
+ import torch.nn
+
+ pytorch_layer = torch.nn.Linear(5, 5)
+ with pytest.raises(ValueError, match=r"not supported.*1.9.0"):
+ PyTorchWrapper_v2(
+ pytorch_layer.cuda(),
+ mixed_precision=True,
+ )

thinc/tests/shims/test_pytorch_grad_scaler.py

@@ -2,7 +2,8 @@
 
 from hypothesis import given, settings
 from hypothesis.strategies import lists, one_of, tuples
-from thinc.util import has_torch, has_torch_gpu, is_torch_array
+from thinc.util import has_torch, has_torch_amp, has_torch_gpu
+from thinc.util import is_torch_array
 from thinc.api import PyTorchGradScaler
 
 from ..strategies import ndarrays
@@ -22,6 +23,9 @@ def tensors():
 
 @pytest.mark.skipif(not has_torch, reason="needs PyTorch")
 @pytest.mark.skipif(not has_torch_gpu, reason="needs a GPU")
+@pytest.mark.skipif(
+ not has_torch_amp, reason="requires PyTorch with mixed-precision support"
+)
 @given(X=one_of(tensors(), lists(tensors()), tuples(tensors())))
 @settings(deadline=None)
 def test_scale_random_inputs(X):
@@ -32,16 +36,19 @@ def test_scale_random_inputs(X):
  scaler.to_(device_id)
 
  if is_torch_array(X):
- assert torch.allclose(scaler.scale(X), X * 2.0 ** 16)
+ assert torch.allclose(scaler.scale(X), X * 2.0**16)
  else:
  scaled1 = scaler.scale(X)
- scaled2 = [t * 2.0 ** 16 for t in X]
+ scaled2 = [t * 2.0**16 for t in X]
  for t1, t2 in zip(scaled1, scaled2):
  assert torch.allclose(t1, t2)
 
 
 @pytest.mark.skipif(not has_torch, reason="needs PyTorch")
 @pytest.mark.skipif(not has_torch_gpu, reason="needs a GPU")
+@pytest.mark.skipif(
+ not has_torch_amp, reason="requires PyTorch with mixed-precision support"
+)
 def test_grad_scaler():
  import torch
 
@@ -53,10 +60,10 @@ def test_grad_scaler():
  # Test that scaling works as expected.
  t = torch.tensor([1.0], device=device_id)
  assert scaler.scale([torch.tensor([1.0], device=device_id)]) == [
- torch.tensor([2.0 ** 16], device=device_id)
+ torch.tensor([2.0**16], device=device_id)
  ]
  assert scaler.scale(torch.tensor([1.0], device=device_id)) == torch.tensor(
- [2.0 ** 16], device=device_id
+ [2.0**16], device=device_id
  )
  with pytest.raises(ValueError):
  scaler.scale("bogus")
@@ -65,7 +72,7 @@ def test_grad_scaler():
 
  # Test infinity detection.
  g = [
- torch.tensor([2.0 ** 16], device=device_id),
+ torch.tensor([2.0**16], device=device_id),
  torch.tensor([float("Inf")], device=device_id),
  ]
 
@@ -80,5 +87,14 @@ def test_grad_scaler():
  # Since infinity was found, the scale should be halved from 2**16
  # to 2**15 for the next step.
  assert scaler.scale([torch.tensor([1.0], device=device_id)]) == [
- torch.tensor([2.0 ** 15], device=device_id)
+ torch.tensor([2.0**15], device=device_id)
  ]
+
+
+@pytest.mark.skipif(not has_torch, reason="needs PyTorch")
+@pytest.mark.skipif(
+ has_torch_amp, reason="needs PyTorch without gradient scaling support"
+)
+def test_raises_on_old_pytorch():
+ with pytest.raises(ValueError, match=r"not supported.*1.9.0"):
+ PyTorchGradScaler(enabled=True)

thinc/util.py

@@ -1,6 +1,7 @@
 from typing import Any, Union, Sequence, cast, Dict, Optional, Callable, TypeVar
 from typing import List, Tuple
 import numpy
+from packaging.version import Version
 import random
 import functools
 from wasabi import table
@@ -31,11 +32,16 @@
 
  has_torch = True
  has_torch_gpu = torch.cuda.device_count() != 0
- has_torch_amp = not torch.cuda.amp.common.amp_definitely_not_available()
+ torch_version = Version(str(torch.__version__))
+ has_torch_amp = (
+ torch_version >= Version("1.9.0")
+ and not torch.cuda.amp.common.amp_definitely_not_available()
+ )
 except ImportError: # pragma: no cover
  has_torch = False
  has_torch_gpu = False
  has_torch_amp = False
+ torch_version = Version("0.0.0")
 
 try: # pragma: no cover
  import tensorflow.experimental.dlpack