chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,605 @@
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from collections import OrderedDict
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from types import MappingProxyType
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from typing import List, Optional
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import numpy as np
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import tree # pip install dm_tree
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from gymnasium.spaces import Discrete, MultiDiscrete
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from ray._common.deprecation import Deprecated
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from ray.rllib.utils.annotations import PublicAPI
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from ray.rllib.utils.framework import try_import_tf, try_import_torch
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from ray.rllib.utils.typing import SpaceStruct, TensorStructType, TensorType, Union
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tf1, tf, tfv = try_import_tf()
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torch, _ = try_import_torch()
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SMALL_NUMBER = 1e-6
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# Some large int number. May be increased here, if needed.
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LARGE_INTEGER = 100000000
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# Min and Max outputs (clipped) from an NN-output layer interpreted as the
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# log(x) of some x (e.g. a stddev of a normal
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# distribution).
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MIN_LOG_NN_OUTPUT = -5
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MAX_LOG_NN_OUTPUT = 2
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@PublicAPI
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@Deprecated(
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help="RLlib itself has no use for this anymore.",
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error=False,
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)
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def aligned_array(size: int, dtype, align: int = 64) -> np.ndarray:
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"""Returns an array of a given size that is 64-byte aligned.
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The returned array can be efficiently copied into GPU memory by TensorFlow.
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Args:
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size: The size (total number of items) of the array. For example,
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array([[0.0, 1.0], [2.0, 3.0]]) would have size=4.
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dtype: The numpy dtype of the array.
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align: The alignment to use.
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Returns:
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A np.ndarray with the given specifications.
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"""
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n = size * dtype.itemsize
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empty = np.empty(n + (align - 1), dtype=np.uint8)
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data_align = empty.ctypes.data % align
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offset = 0 if data_align == 0 else (align - data_align)
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if n == 0:
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# stop np from optimising out empty slice reference
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output = empty[offset : offset + 1][0:0].view(dtype)
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else:
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output = empty[offset : offset + n].view(dtype)
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assert len(output) == size, len(output)
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assert output.ctypes.data % align == 0, output.ctypes.data
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return output
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@PublicAPI
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@Deprecated(
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help="RLlib itself has no use for this anymore.",
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error=False,
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)
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def concat_aligned(
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items: List[np.ndarray], time_major: Optional[bool] = None
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) -> np.ndarray:
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"""Concatenate arrays, ensuring the output is 64-byte aligned.
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We only align float arrays; other arrays are concatenated as normal.
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This should be used instead of np.concatenate() to improve performance
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when the output array is likely to be fed into TensorFlow.
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Args:
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items: The list of items to concatenate and align.
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time_major: Whether the data in items is time-major, in which
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case, we will concatenate along axis=1.
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Returns:
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The concat'd and aligned array.
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"""
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if len(items) == 0:
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return []
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elif len(items) == 1:
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# we assume the input is aligned. In any case, it doesn't help
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# performance to force align it since that incurs a needless copy.
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return items[0]
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elif isinstance(items[0], np.ndarray) and items[0].dtype in [
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np.float32,
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np.float64,
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np.uint8,
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]:
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dtype = items[0].dtype
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flat = aligned_array(sum(s.size for s in items), dtype)
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if time_major is not None:
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if time_major is True:
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batch_dim = sum(s.shape[1] for s in items)
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new_shape = (items[0].shape[0], batch_dim,) + items[
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0
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].shape[2:]
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else:
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batch_dim = sum(s.shape[0] for s in items)
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new_shape = (batch_dim, items[0].shape[1],) + items[
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0
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].shape[2:]
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else:
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batch_dim = sum(s.shape[0] for s in items)
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new_shape = (batch_dim,) + items[0].shape[1:]
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output = flat.reshape(new_shape)
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assert output.ctypes.data % 64 == 0, output.ctypes.data
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np.concatenate(items, out=output, axis=1 if time_major else 0)
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return output
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else:
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return np.concatenate(items, axis=1 if time_major else 0)
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@PublicAPI
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def convert_to_numpy(x: TensorStructType, reduce_type: bool = True) -> TensorStructType:
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"""Converts values in `stats` to non-Tensor numpy or python types.
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Args:
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x: Any (possibly nested) struct, the values in which will be
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converted and returned as a new struct with all torch/tf tensors
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being converted to numpy types.
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reduce_type: Whether to automatically reduce all float64 and int64 data
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into float32 and int32 data, respectively.
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Returns:
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A new struct with the same structure as `x`, but with all
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values converted to numpy arrays (on CPU).
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"""
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# The mapping function used to numpyize torch/tf Tensors (and move them
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# to the CPU beforehand).
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def mapping(item):
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if torch and isinstance(item, torch.Tensor):
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ret = (
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item.cpu().item()
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if len(item.size()) == 0
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else item.detach().cpu().numpy()
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)
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elif (
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tf and isinstance(item, (tf.Tensor, tf.Variable)) and hasattr(item, "numpy")
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):
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assert tf.executing_eagerly()
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ret = item.numpy()
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else:
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ret = item
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if reduce_type and isinstance(ret, np.ndarray):
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if np.issubdtype(ret.dtype, np.floating):
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ret = ret.astype(np.float32)
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elif np.issubdtype(ret.dtype, int):
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ret = ret.astype(np.int32)
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return ret
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return tree.map_structure(mapping, x)
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@PublicAPI
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def fc(
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x: np.ndarray,
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weights: np.ndarray,
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biases: Optional[np.ndarray] = None,
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framework: Optional[str] = None,
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) -> np.ndarray:
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"""Calculates FC (dense) layer outputs given weights/biases and input.
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Args:
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x: The input to the dense layer.
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weights: The weights matrix.
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biases: The biases vector. All 0s if None.
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framework: An optional framework hint (to figure out,
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e.g. whether to transpose torch weight matrices).
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Returns:
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The dense layer's output.
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"""
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def map_(data, transpose=False):
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if torch:
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if isinstance(data, torch.Tensor):
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data = data.cpu().detach().numpy()
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if tf and tf.executing_eagerly():
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if isinstance(data, tf.Variable):
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data = data.numpy()
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if transpose:
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data = np.transpose(data)
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return data
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x = map_(x)
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# Torch stores matrices in transpose (faster for backprop).
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transpose = framework == "torch" and (
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x.shape[1] != weights.shape[0] and x.shape[1] == weights.shape[1]
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)
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weights = map_(weights, transpose=transpose)
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biases = map_(biases)
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return np.matmul(x, weights) + (0.0 if biases is None else biases)
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@PublicAPI
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def flatten_inputs_to_1d_tensor(
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inputs: TensorStructType,
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spaces_struct: Optional[SpaceStruct] = None,
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time_axis: bool = False,
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batch_axis: bool = True,
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) -> TensorType:
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"""Flattens arbitrary input structs according to the given spaces struct.
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Returns a single 1D tensor resulting from the different input
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components' values.
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Thereby:
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- Boxes (any shape) get flattened to (B, [T]?, -1). Note that image boxes
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are not treated differently from other types of Boxes and get
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flattened as well.
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- Discrete (int) values are one-hot'd, e.g. a batch of [1, 0, 3] (B=3 with
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Discrete(4) space) results in [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1]].
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- MultiDiscrete values are multi-one-hot'd, e.g. a batch of
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[[0, 2], [1, 4]] (B=2 with MultiDiscrete([2, 5]) space) results in
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[[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 1]].
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Args:
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inputs: The inputs to be flattened.
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spaces_struct: The (possibly nested) structure of the spaces that `inputs`
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belongs to.
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time_axis: Whether all inputs have a time-axis (after the batch axis).
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If True, will keep not only the batch axis (0th), but the time axis
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(1st) as-is and flatten everything from the 2nd axis up.
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batch_axis: Whether all inputs have a batch axis.
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If True, will keep that batch axis as-is and flatten everything from the
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other dims up.
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Returns:
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A single 1D tensor resulting from concatenating all
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flattened/one-hot'd input components. Depending on the time_axis flag,
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the shape is (B, n) or (B, T, n).
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.. testcode::
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:skipif: True
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# B=2
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from ray.rllib.utils.tf_utils import flatten_inputs_to_1d_tensor
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from gymnasium.spaces import Discrete, Box
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out = flatten_inputs_to_1d_tensor(
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{"a": [1, 0], "b": [[[0.0], [0.1]], [1.0], [1.1]]},
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spaces_struct=dict(a=Discrete(2), b=Box(shape=(2, 1)))
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)
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print(out)
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# B=2; T=2
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out = flatten_inputs_to_1d_tensor(
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([[1, 0], [0, 1]],
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[[[0.0, 0.1], [1.0, 1.1]], [[2.0, 2.1], [3.0, 3.1]]]),
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spaces_struct=tuple([Discrete(2), Box(shape=(2, ))]),
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time_axis=True
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)
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print(out)
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.. testoutput::
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[[0.0, 1.0, 0.0, 0.1], [1.0, 0.0, 1.0, 1.1]] # B=2 n=4
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[[[0.0, 1.0, 0.0, 0.1], [1.0, 0.0, 1.0, 1.1]],
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[[1.0, 0.0, 2.0, 2.1], [0.0, 1.0, 3.0, 3.1]]] # B=2 T=2 n=4
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"""
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# `time_axis` must not be True if `batch_axis` is False.
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assert not (time_axis and not batch_axis)
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flat_inputs = tree.flatten(inputs)
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flat_spaces = (
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tree.flatten(spaces_struct)
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if spaces_struct is not None
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else [None] * len(flat_inputs)
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)
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B = None
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T = None
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out = []
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for input_, space in zip(flat_inputs, flat_spaces):
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# Store batch and (if applicable) time dimension.
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if B is None and batch_axis:
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B = input_.shape[0]
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if time_axis:
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T = input_.shape[1]
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# One-hot encoding.
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if isinstance(space, Discrete):
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if time_axis:
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input_ = np.reshape(input_, [B * T])
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out.append(one_hot(input_, depth=space.n).astype(np.float32))
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# Multi one-hot encoding.
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elif isinstance(space, MultiDiscrete):
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if time_axis:
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input_ = np.reshape(input_, [B * T, -1])
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if batch_axis:
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out.append(
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np.concatenate(
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[
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one_hot(input_[:, i], depth=n).astype(np.float32)
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for i, n in enumerate(space.nvec)
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],
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axis=-1,
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)
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)
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else:
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out.append(
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np.concatenate(
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[
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one_hot(input_[i], depth=n).astype(np.float32)
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for i, n in enumerate(space.nvec)
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],
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axis=-1,
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)
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)
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# Box: Flatten.
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else:
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# Special case for spaces: Box(.., shape=(), ..)
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if isinstance(input_, float):
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input_ = np.array([input_])
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if time_axis:
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input_ = np.reshape(input_, [B * T, -1])
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elif batch_axis:
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input_ = np.reshape(input_, [B, -1])
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else:
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input_ = np.reshape(input_, [-1])
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out.append(input_.astype(np.float32))
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merged = np.concatenate(out, axis=-1)
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# Restore the time-dimension, if applicable.
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if time_axis:
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merged = np.reshape(merged, [B, T, -1])
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return merged
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@PublicAPI
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def make_action_immutable(obj):
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"""Flags actions immutable to notify users when trying to change them.
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Can also be used with any tree-like structure containing either
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dictionaries, numpy arrays or already immutable objects per se.
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Note, however that `tree.map_structure()` will in general not
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include the shallow object containing all others and therefore
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immutability will hold only for all objects contained in it.
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Use `tree.traverse(fun, action, top_down=False)` to include
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also the containing object.
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Args:
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obj: The object to be made immutable.
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Returns:
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The immutable object.
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.. testcode::
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:skipif: True
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import tree
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import numpy as np
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from ray.rllib.utils.numpy import make_action_immutable
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arr = np.arange(1,10)
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d = dict(a = 1, b = (arr, arr))
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tree.traverse(make_action_immutable, d, top_down=False)
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"""
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if isinstance(obj, np.ndarray):
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obj.setflags(write=False)
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return obj
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elif isinstance(obj, OrderedDict):
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return MappingProxyType(dict(obj))
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elif isinstance(obj, dict):
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return MappingProxyType(obj)
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else:
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return obj
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@PublicAPI
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def huber_loss(x: np.ndarray, delta: float = 1.0) -> np.ndarray:
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"""Reference: https://en.wikipedia.org/wiki/Huber_loss."""
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return np.where(
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np.abs(x) < delta, np.power(x, 2.0) * 0.5, delta * (np.abs(x) - 0.5 * delta)
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)
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@PublicAPI
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def l2_loss(x: np.ndarray) -> np.ndarray:
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"""Computes half the L2 norm of a tensor (w/o the sqrt): sum(x**2) / 2.
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Args:
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x: The input tensor.
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Returns:
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The l2-loss output according to the above formula given `x`.
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"""
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return np.sum(np.square(x)) / 2.0
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@PublicAPI
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def lstm(
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x,
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weights: np.ndarray,
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biases: Optional[np.ndarray] = None,
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initial_internal_states: Optional[np.ndarray] = None,
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time_major: bool = False,
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forget_bias: float = 1.0,
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):
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"""Calculates LSTM layer output given weights/biases, states, and input.
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Args:
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x: The inputs to the LSTM layer including time-rank
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(0th if time-major, else 1st) and the batch-rank
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(1st if time-major, else 0th).
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weights: The weights matrix.
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biases: The biases vector. All 0s if None.
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initial_internal_states: The initial internal
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states to pass into the layer. All 0s if None.
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time_major: Whether to use time-major or not. Default: False.
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forget_bias: Gets added to first sigmoid (forget gate) output.
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Default: 1.0.
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Returns:
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Tuple consisting of 1) The LSTM layer's output and
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2) Tuple: Last (c-state, h-state).
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"""
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sequence_length = x.shape[0 if time_major else 1]
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batch_size = x.shape[1 if time_major else 0]
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units = weights.shape[1] // 4 # 4 internal layers (3x sigmoid, 1x tanh)
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if initial_internal_states is None:
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c_states = np.zeros(shape=(batch_size, units))
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h_states = np.zeros(shape=(batch_size, units))
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else:
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c_states = initial_internal_states[0]
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h_states = initial_internal_states[1]
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# Create a placeholder for all n-time step outputs.
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if time_major:
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unrolled_outputs = np.zeros(shape=(sequence_length, batch_size, units))
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else:
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unrolled_outputs = np.zeros(shape=(batch_size, sequence_length, units))
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# Push the batch 4 times through the LSTM cell and capture the outputs plus
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# the final h- and c-states.
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for t in range(sequence_length):
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input_matrix = x[t, :, :] if time_major else x[:, t, :]
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input_matrix = np.concatenate((input_matrix, h_states), axis=1)
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input_matmul_matrix = np.matmul(input_matrix, weights) + biases
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# Forget gate (3rd slot in tf output matrix). Add static forget bias.
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sigmoid_1 = sigmoid(input_matmul_matrix[:, units * 2 : units * 3] + forget_bias)
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c_states = np.multiply(c_states, sigmoid_1)
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# Add gate (1st and 2nd slots in tf output matrix).
|
||||
sigmoid_2 = sigmoid(input_matmul_matrix[:, 0:units])
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||||
tanh_3 = np.tanh(input_matmul_matrix[:, units : units * 2])
|
||||
c_states = np.add(c_states, np.multiply(sigmoid_2, tanh_3))
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||||
# Output gate (last slot in tf output matrix).
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||||
sigmoid_4 = sigmoid(input_matmul_matrix[:, units * 3 : units * 4])
|
||||
h_states = np.multiply(sigmoid_4, np.tanh(c_states))
|
||||
|
||||
# Store this output time-slice.
|
||||
if time_major:
|
||||
unrolled_outputs[t, :, :] = h_states
|
||||
else:
|
||||
unrolled_outputs[:, t, :] = h_states
|
||||
|
||||
return unrolled_outputs, (c_states, h_states)
|
||||
|
||||
|
||||
@PublicAPI
|
||||
def one_hot(
|
||||
x: Union[TensorType, int],
|
||||
depth: int = 0,
|
||||
on_value: float = 1.0,
|
||||
off_value: float = 0.0,
|
||||
dtype: type = np.float32,
|
||||
) -> np.ndarray:
|
||||
"""One-hot utility function for numpy.
|
||||
|
||||
Thanks to qianyizhang:
|
||||
https://gist.github.com/qianyizhang/07ee1c15cad08afb03f5de69349efc30.
|
||||
|
||||
Args:
|
||||
x: The input to be one-hot encoded.
|
||||
depth: The max. number to be one-hot encoded (size of last rank).
|
||||
on_value: The value to use for on. Default: 1.0.
|
||||
off_value: The value to use for off. Default: 0.0.
|
||||
|
||||
Returns:
|
||||
The one-hot encoded equivalent of the input array.
|
||||
"""
|
||||
|
||||
# Handle simple ints properly.
|
||||
if isinstance(x, int):
|
||||
x = np.array(x, dtype=np.int32)
|
||||
# Handle torch arrays properly.
|
||||
elif torch and isinstance(x, torch.Tensor):
|
||||
x = x.numpy()
|
||||
|
||||
# Handle bool arrays correctly.
|
||||
if x.dtype == np.bool_:
|
||||
x = x.astype(np.int_)
|
||||
depth = 2
|
||||
|
||||
# If depth is not given, try to infer it from the values in the array.
|
||||
if depth == 0:
|
||||
depth = np.max(x) + 1
|
||||
assert (
|
||||
np.max(x) < depth
|
||||
), "ERROR: The max. index of `x` ({}) is larger than depth ({})!".format(
|
||||
np.max(x), depth
|
||||
)
|
||||
shape = x.shape
|
||||
|
||||
out = np.ones(shape=(*shape, depth)) * off_value
|
||||
indices = []
|
||||
for i in range(x.ndim):
|
||||
tiles = [1] * x.ndim
|
||||
s = [1] * x.ndim
|
||||
s[i] = -1
|
||||
r = np.arange(shape[i]).reshape(s)
|
||||
if i > 0:
|
||||
tiles[i - 1] = shape[i - 1]
|
||||
r = np.tile(r, tiles)
|
||||
indices.append(r)
|
||||
indices.append(x)
|
||||
out[tuple(indices)] = on_value
|
||||
return out.astype(dtype)
|
||||
|
||||
|
||||
@PublicAPI
|
||||
def one_hot_multidiscrete(x, depths=List[int]):
|
||||
# Handle torch arrays properly.
|
||||
if torch and isinstance(x, torch.Tensor):
|
||||
x = x.numpy()
|
||||
|
||||
shape = x.shape
|
||||
return np.concatenate(
|
||||
[
|
||||
one_hot(x[i] if len(shape) == 1 else x[:, i], depth=n).astype(np.float32)
|
||||
for i, n in enumerate(depths)
|
||||
],
|
||||
axis=-1,
|
||||
)
|
||||
|
||||
|
||||
@PublicAPI
|
||||
def relu(x: np.ndarray, alpha: float = 0.0) -> np.ndarray:
|
||||
"""Implementation of the leaky ReLU function.
|
||||
|
||||
y = x * alpha if x < 0 else x
|
||||
|
||||
Args:
|
||||
x: The input values.
|
||||
alpha: A scaling ("leak") factor to use for negative x.
|
||||
|
||||
Returns:
|
||||
The leaky ReLU output for x.
|
||||
"""
|
||||
return np.maximum(x, x * alpha, x)
|
||||
|
||||
|
||||
@PublicAPI
|
||||
def sigmoid(x: np.ndarray, derivative: bool = False) -> np.ndarray:
|
||||
"""
|
||||
Returns the sigmoid function applied to x.
|
||||
Alternatively, can return the derivative or the sigmoid function.
|
||||
|
||||
Args:
|
||||
x: The input to the sigmoid function.
|
||||
derivative: Whether to return the derivative or not.
|
||||
Default: False.
|
||||
|
||||
Returns:
|
||||
The sigmoid function (or its derivative) applied to x.
|
||||
"""
|
||||
if derivative:
|
||||
return x * (1 - x)
|
||||
else:
|
||||
return 1 / (1 + np.exp(-x))
|
||||
|
||||
|
||||
@PublicAPI
|
||||
def softmax(
|
||||
x: Union[np.ndarray, list], axis: int = -1, epsilon: Optional[float] = None
|
||||
) -> np.ndarray:
|
||||
"""Returns the softmax values for x.
|
||||
|
||||
The exact formula used is:
|
||||
S(xi) = e^xi / SUMj(e^xj), where j goes over all elements in x.
|
||||
|
||||
Args:
|
||||
x: The input to the softmax function.
|
||||
axis: The axis along which to softmax.
|
||||
epsilon: Optional epsilon as a minimum value. If None, use
|
||||
`SMALL_NUMBER`.
|
||||
|
||||
Returns:
|
||||
The softmax over x.
|
||||
"""
|
||||
epsilon = epsilon or SMALL_NUMBER
|
||||
# x_exp = np.maximum(np.exp(x), SMALL_NUMBER)
|
||||
x_exp = np.exp(x)
|
||||
# return x_exp /
|
||||
# np.maximum(np.sum(x_exp, axis, keepdims=True), SMALL_NUMBER)
|
||||
return np.maximum(x_exp / np.sum(x_exp, axis, keepdims=True), epsilon)
|
||||
Reference in New Issue
Block a user