Merge branches 'main' and 'main' of github.com:keras-team/keras-core
This commit is contained in:
Francois Chollet
parent
a9f4ccd0d7
commit
186cbf6b7c
@ -294,7 +294,7 @@ def diagonal(x, offset=0, axis1=0, axis2=1):
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def dot(x, y):
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x, y = convert_to_tensor(x), convert_to_tensor(y)
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if len(x.shape) == 0 or len(y.shape) == 0:
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if x.ndim == 0 or y.ndim == 0:
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return torch.multiply(x, y)
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return torch.matmul(x, y)
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@ -327,7 +327,7 @@ def expm1(x):
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def flip(x, axis=None):
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x = convert_to_tensor(x)
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if axis is None:
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axis = tuple(range(len(x.shape)))
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axis = tuple(range(x.ndim))
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if isinstance(axis, int):
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axis = (axis,)
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return torch.flip(x, dims=axis)
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@ -341,23 +341,21 @@ def floor(x):
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def full(shape, fill_value, dtype=None):
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dtype = to_torch_dtype(dtype)
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if hasattr(fill_value, "__len__"):
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raise NotImplementedError(
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"`torch.full()` only accepts scalars for `fill_value`. "
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f"Received: fill_value={fill_value}"
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)
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# TODO: implement conversion of shape into repetitions for `torch.tile``
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# return torch.tile(fill_value, reps)
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fill_value = convert_to_tensor(fill_value)
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reps = shape[-1] // fill_value.shape[-1] # channels-last reduction
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reps_by_dim = (*shape[:-1], reps)
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return torch.tile(fill_value, reps_by_dim)
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return torch.full(size=shape, fill_value=fill_value, dtype=dtype)
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def full_like(x, fill_value, dtype=None):
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dtype = to_torch_dtype(dtype)
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if hasattr(fill_value, "__len__"):
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raise NotImplementedError(
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"`torch.full()` only accepts scalars for `fill_value`."
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fill_value = convert_to_tensor(fill_value)
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reps_by_dim = tuple(
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[x.shape[i] // fill_value.shape[i] for i in range(x.ndim)]
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)
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# TODO: implement conversion of shape into repetitions for `torch.tile``
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# return torch.tile(fill_value, reps)
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return torch.tile(fill_value, reps_by_dim)
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x = convert_to_tensor(x)
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return torch.full_like(input=x, fill_value=fill_value, dtype=dtype)
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@ -430,15 +428,27 @@ def linspace(
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"torch.linspace does not support an `axis` argument. "
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f"Received axis={axis}"
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)
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dtype = to_torch_dtype(dtype)
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dtype = to_torch_dtype(dtype)
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if endpoint is False:
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stop = stop - ((stop - start) / num)
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linspace = torch.linspace(
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start=start,
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end=stop,
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steps=num,
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dtype=dtype,
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)
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if hasattr(start, "__len__") and hasattr(stop, "__len__"):
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start, stop = convert_to_tensor(start), convert_to_tensor(stop)
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stop = cast(stop, dtype) if endpoint is False and dtype else stop
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steps = torch.arange(num, dtype=dtype) / (num - 1)
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# reshape `steps` to allow for broadcasting
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for i in range(start.ndim):
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steps = steps.unsqueeze(-1)
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# increments from `start` to `stop` in each dimension
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linspace = start[None] + steps * (stop - start)[None]
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else:
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linspace = torch.linspace(
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start=start,
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end=stop,
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steps=num,
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dtype=dtype,
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)
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if retstep is True:
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return (linspace, num)
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return linspace
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@ -495,13 +505,26 @@ def logspace(start, stop, num=50, endpoint=True, base=10, dtype=None, axis=0):
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dtype = to_torch_dtype(dtype)
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if endpoint is False:
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stop = stop - ((stop - start) / num)
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logspace = torch.logspace(
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start=start,
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end=stop,
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steps=num,
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base=base,
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dtype=dtype,
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)
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if hasattr(start, "__len__") and hasattr(stop, "__len__"):
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start, stop = convert_to_tensor(start), convert_to_tensor(stop)
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stop = cast(stop, dtype) if endpoint is False and dtype else stop
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steps = torch.arange(num, dtype=dtype) / (num - 1)
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# reshape `steps` to allow for broadcasting
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for i in range(start.ndim):
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steps = steps.unsqueeze(-1)
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# increments from `start` to `stop` in each dimension
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linspace = start[None] + steps * (stop - start)[None]
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logspace = base**linspace
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else:
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logspace = torch.logspace(
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start=start,
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end=stop,
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steps=num,
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base=base,
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dtype=dtype,
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)
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return logspace
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@ -715,14 +738,7 @@ def tile(x, repeats):
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def trace(x, offset=None, axis1=None, axis2=None):
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x = convert_to_tensor(x)
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# TODO: implement support for these arguments
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# API divergence between `np.trace()` and `torch.trace()`
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if offset or axis1 or axis2:
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raise NotImplementedError(
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"Arguments not supported by `torch.trace: "
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f"offset={offset}, axis1={axis1}, axis2={axis2}"
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)
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return torch.trace(x)
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return torch.sum(torch.diagonal(x, offset, axis1, axis2), dim=-1)
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def tri(N, M=None, k=0, dtype="float32"):
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@ -1713,27 +1713,16 @@ class NumpyTwoInputOpsCorretnessTest(testing.TestCase):
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np.array(knp.full_like(x, 2, dtype="float32")),
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np.full_like(x, 2, dtype="float32"),
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)
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self.assertAllClose(
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np.array(knp.full_like(x, np.ones([2, 3]))),
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np.full_like(x, np.ones([2, 3])),
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)
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self.assertAllClose(np.array(knp.FullLike()(x, 2)), np.full_like(x, 2))
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self.assertAllClose(
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np.array(knp.FullLike()(x, 2, dtype="float32")),
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np.full_like(x, 2, dtype="float32"),
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)
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# TODO: implement conversion of shape into repetitions, pass to
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# `torch.tile`, since `torch.full()` only accepts scalars
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# for `fill_value`."
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@pytest.mark.skipif(
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backend.backend() == "torch",
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reason="`torch.full` only accepts scalars for `fill_value`.",
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)
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def test_full_like_without_torch(self):
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x = np.array([[1, 2, 3], [3, 2, 1]])
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self.assertAllClose(
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np.array(knp.full_like(x, np.ones([2, 3]))),
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np.full_like(x, np.ones([2, 3])),
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)
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self.assertAllClose(
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np.array(knp.FullLike()(x, np.ones([2, 3]))),
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np.full_like(x, np.ones([2, 3])),
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@ -1834,13 +1823,6 @@ class NumpyTwoInputOpsCorretnessTest(testing.TestCase):
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np.linspace(0, 10, 5, endpoint=False),
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)
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# TODO: torch.linspace does not support tensor or array
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# for start/stop, create manual implementation
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@pytest.mark.skipif(
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backend.backend() == "torch",
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reason="`torch.linspace` has no support for array start/stop.",
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)
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def test_linspace_without_torch(self):
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start = np.zeros([2, 3, 4])
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stop = np.ones([2, 3, 4])
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self.assertAllClose(
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@ -1945,15 +1927,9 @@ class NumpyTwoInputOpsCorretnessTest(testing.TestCase):
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np.logspace(0, 10, 5, endpoint=False),
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)
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# TODO: torch.logspace does not support tensor or array
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# for start/stop, create manual implementation
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@pytest.mark.skipif(
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backend.backend() == "torch",
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reason="`torch.logspace` has no support for array start/stop.",
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)
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def test_logspace_without_torch(self):
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start = np.zeros([2, 3, 4])
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stop = np.ones([2, 3, 4])
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self.assertAllClose(
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np.array(knp.logspace(start, stop, 5, base=10)),
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np.logspace(start, stop, 5, base=10),
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@ -3016,13 +2992,7 @@ class NumpyOneInputOpsCorrectnessTest(testing.TestCase):
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self.assertAllClose(np.array(knp.tile(x, [2, 3])), np.tile(x, [2, 3]))
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self.assertAllClose(np.array(knp.Tile([2, 3])(x)), np.tile(x, [2, 3]))
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@pytest.mark.skipif(
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backend.backend() == "torch",
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reason="`torch.split` does not support args `offset`, `axis1`, `axis2`",
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)
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def test_trace(self):
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# TODO: implement `torch.trace` support for arguments `offset`,
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# `axis1`, `axis2` and delete NotImplementedError
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x = np.arange(24).reshape([1, 2, 3, 4])
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self.assertAllClose(np.array(knp.trace(x)), np.trace(x))
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self.assertAllClose(
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@ -3092,25 +3062,15 @@ class NumpyArrayCreateOpsCorrectnessTest(testing.TestCase):
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self.assertAllClose(
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np.array(knp.full([2, 3], 0.1)), np.full([2, 3], 0.1)
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)
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self.assertAllClose(np.array(knp.Full()([2, 3], 0)), np.full([2, 3], 0))
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self.assertAllClose(
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np.array(knp.Full()([2, 3], 0.1)), np.full([2, 3], 0.1)
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)
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# TODO: implement conversion of shape into repetitions, pass to
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# `torch.tile`, since `torch.full()` only accepts scalars
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# for `fill_value`."
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@pytest.mark.skipif(
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backend.backend() == "torch",
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reason="`torch.full` only accepts scalars for `fill_value`.",
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)
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def test_full_without_torch(self):
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self.assertAllClose(
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np.array(knp.full([2, 3], np.array([1, 4, 5]))),
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np.full([2, 3], np.array([1, 4, 5])),
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)
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self.assertAllClose(np.array(knp.Full()([2, 3], 0)), np.full([2, 3], 0))
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self.assertAllClose(
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np.array(knp.Full()([2, 3], 0.1)), np.full([2, 3], 0.1)
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)
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self.assertAllClose(
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np.array(knp.Full()([2, 3], np.array([1, 4, 5]))),
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np.full([2, 3], np.array([1, 4, 5])),
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