mirror of
https://github.com/vale981/ray
synced 2025-03-06 10:31:39 -05:00
386 lines
13 KiB
Python
386 lines
13 KiB
Python
import numpy as np
|
|
import tree # pip install dm_tree
|
|
from typing import List, Optional
|
|
|
|
from ray.rllib.utils.deprecation import DEPRECATED_VALUE, deprecation_warning
|
|
from ray.rllib.utils.framework import try_import_tf, try_import_torch
|
|
from ray.rllib.utils.typing import TensorType, TensorStructType, Union
|
|
|
|
tf1, tf, tfv = try_import_tf()
|
|
torch, _ = try_import_torch()
|
|
|
|
SMALL_NUMBER = 1e-6
|
|
# Some large int number. May be increased here, if needed.
|
|
LARGE_INTEGER = 100000000
|
|
# Min and Max outputs (clipped) from an NN-output layer interpreted as the
|
|
# log(x) of some x (e.g. a stddev of a normal
|
|
# distribution).
|
|
MIN_LOG_NN_OUTPUT = -5
|
|
MAX_LOG_NN_OUTPUT = 2
|
|
|
|
|
|
def aligned_array(size: int, dtype, align: int = 64) -> np.ndarray:
|
|
"""Returns an array of a given size that is 64-byte aligned.
|
|
|
|
The returned array can be efficiently copied into GPU memory by TensorFlow.
|
|
|
|
Args:
|
|
size: The size (total number of items) of the array. For example,
|
|
array([[0.0, 1.0], [2.0, 3.0]]) would have size=4.
|
|
dtype: The numpy dtype of the array.
|
|
align: The alignment to use.
|
|
|
|
Returns:
|
|
A np.ndarray with the given specifications.
|
|
"""
|
|
n = size * dtype.itemsize
|
|
empty = np.empty(n + (align - 1), dtype=np.uint8)
|
|
data_align = empty.ctypes.data % align
|
|
offset = 0 if data_align == 0 else (align - data_align)
|
|
if n == 0:
|
|
# stop np from optimising out empty slice reference
|
|
output = empty[offset:offset + 1][0:0].view(dtype)
|
|
else:
|
|
output = empty[offset:offset + n].view(dtype)
|
|
|
|
assert len(output) == size, len(output)
|
|
assert output.ctypes.data % align == 0, output.ctypes.data
|
|
return output
|
|
|
|
|
|
def concat_aligned(items: List[np.ndarray],
|
|
time_major: Optional[bool] = None) -> np.ndarray:
|
|
"""Concatenate arrays, ensuring the output is 64-byte aligned.
|
|
|
|
We only align float arrays; other arrays are concatenated as normal.
|
|
|
|
This should be used instead of np.concatenate() to improve performance
|
|
when the output array is likely to be fed into TensorFlow.
|
|
|
|
Args:
|
|
items: The list of items to concatenate and align.
|
|
time_major: Whether the data in items is time-major, in which
|
|
case, we will concatenate along axis=1.
|
|
|
|
Returns:
|
|
The concat'd and aligned array.
|
|
"""
|
|
|
|
if len(items) == 0:
|
|
return []
|
|
elif len(items) == 1:
|
|
# we assume the input is aligned. In any case, it doesn't help
|
|
# performance to force align it since that incurs a needless copy.
|
|
return items[0]
|
|
elif (isinstance(items[0], np.ndarray)
|
|
and items[0].dtype in [np.float32, np.float64, np.uint8]):
|
|
dtype = items[0].dtype
|
|
flat = aligned_array(sum(s.size for s in items), dtype)
|
|
if time_major is not None:
|
|
if time_major is True:
|
|
batch_dim = sum(s.shape[1] for s in items)
|
|
new_shape = (
|
|
items[0].shape[0],
|
|
batch_dim,
|
|
) + items[0].shape[2:]
|
|
else:
|
|
batch_dim = sum(s.shape[0] for s in items)
|
|
new_shape = (
|
|
batch_dim,
|
|
items[0].shape[1],
|
|
) + items[0].shape[2:]
|
|
else:
|
|
batch_dim = sum(s.shape[0] for s in items)
|
|
new_shape = (batch_dim, ) + items[0].shape[1:]
|
|
output = flat.reshape(new_shape)
|
|
assert output.ctypes.data % 64 == 0, output.ctypes.data
|
|
np.concatenate(items, out=output, axis=1 if time_major else 0)
|
|
return output
|
|
else:
|
|
return np.concatenate(items, axis=1 if time_major else 0)
|
|
|
|
|
|
def convert_to_numpy(x: TensorStructType,
|
|
reduce_type: bool = True,
|
|
reduce_floats=DEPRECATED_VALUE):
|
|
"""Converts values in `stats` to non-Tensor numpy or python types.
|
|
|
|
Args:
|
|
x: Any (possibly nested) struct, the values in which will be
|
|
converted and returned as a new struct with all torch/tf tensors
|
|
being converted to numpy types.
|
|
reduce_type: Whether to automatically reduce all float64 and int64 data
|
|
into float32 and int32 data, respectively.
|
|
|
|
Returns:
|
|
A new struct with the same structure as `x`, but with all
|
|
values converted to numpy arrays (on CPU).
|
|
"""
|
|
|
|
if reduce_floats != DEPRECATED_VALUE:
|
|
deprecation_warning(
|
|
old="reduce_floats", new="reduce_types", error=False)
|
|
reduce_type = reduce_floats
|
|
|
|
# The mapping function used to numpyize torch/tf Tensors (and move them
|
|
# to the CPU beforehand).
|
|
def mapping(item):
|
|
if torch and isinstance(item, torch.Tensor):
|
|
ret = item.cpu().item() if len(item.size()) == 0 else \
|
|
item.detach().cpu().numpy()
|
|
elif tf and isinstance(item, (tf.Tensor, tf.Variable)) and \
|
|
hasattr(item, "numpy"):
|
|
assert tf.executing_eagerly()
|
|
ret = item.numpy()
|
|
else:
|
|
ret = item
|
|
if reduce_type and isinstance(ret, np.ndarray):
|
|
if np.issubdtype(ret.dtype, np.floating):
|
|
ret = ret.astype(np.float32)
|
|
elif np.issubdtype(ret.dtype, int):
|
|
ret = ret.astype(np.int32)
|
|
return ret
|
|
return ret
|
|
|
|
return tree.map_structure(mapping, x)
|
|
|
|
|
|
def fc(x: np.ndarray,
|
|
weights: np.ndarray,
|
|
biases: Optional[np.ndarray] = None,
|
|
framework: Optional[str] = None) -> np.ndarray:
|
|
"""Calculates FC (dense) layer outputs given weights/biases and input.
|
|
|
|
Args:
|
|
x: The input to the dense layer.
|
|
weights: The weights matrix.
|
|
biases: The biases vector. All 0s if None.
|
|
framework: An optional framework hint (to figure out,
|
|
e.g. whether to transpose torch weight matrices).
|
|
|
|
Returns:
|
|
The dense layer's output.
|
|
"""
|
|
|
|
def map_(data, transpose=False):
|
|
if torch:
|
|
if isinstance(data, torch.Tensor):
|
|
data = data.cpu().detach().numpy()
|
|
if tf and tf.executing_eagerly():
|
|
if isinstance(data, tf.Variable):
|
|
data = data.numpy()
|
|
if transpose:
|
|
data = np.transpose(data)
|
|
return data
|
|
|
|
x = map_(x)
|
|
# Torch stores matrices in transpose (faster for backprop).
|
|
transpose = (framework == "torch" and (x.shape[1] != weights.shape[0]
|
|
and x.shape[1] == weights.shape[1]))
|
|
weights = map_(weights, transpose=transpose)
|
|
biases = map_(biases)
|
|
|
|
return np.matmul(x, weights) + (0.0 if biases is None else biases)
|
|
|
|
|
|
def huber_loss(x: np.ndarray, delta: float = 1.0) -> np.ndarray:
|
|
"""Reference: https://en.wikipedia.org/wiki/Huber_loss."""
|
|
return np.where(
|
|
np.abs(x) < delta,
|
|
np.power(x, 2.0) * 0.5, delta * (np.abs(x) - 0.5 * delta))
|
|
|
|
|
|
def l2_loss(x: np.ndarray) -> np.ndarray:
|
|
"""Computes half the L2 norm of a tensor (w/o the sqrt): sum(x**2) / 2.
|
|
|
|
Args:
|
|
x: The input tensor.
|
|
|
|
Returns:
|
|
The l2-loss output according to the above formula given `x`.
|
|
"""
|
|
return np.sum(np.square(x)) / 2.0
|
|
|
|
|
|
def lstm(x,
|
|
weights: np.ndarray,
|
|
biases: Optional[np.ndarray] = None,
|
|
initial_internal_states: Optional[np.ndarray] = None,
|
|
time_major: bool = False,
|
|
forget_bias: float = 1.0):
|
|
"""Calculates LSTM layer output given weights/biases, states, and input.
|
|
|
|
Args:
|
|
x: The inputs to the LSTM layer including time-rank
|
|
(0th if time-major, else 1st) and the batch-rank
|
|
(1st if time-major, else 0th).
|
|
weights: The weights matrix.
|
|
biases: The biases vector. All 0s if None.
|
|
initial_internal_states: The initial internal
|
|
states to pass into the layer. All 0s if None.
|
|
time_major: Whether to use time-major or not. Default: False.
|
|
forget_bias: Gets added to first sigmoid (forget gate) output.
|
|
Default: 1.0.
|
|
|
|
Returns:
|
|
Tuple consisting of 1) The LSTM layer's output and
|
|
2) Tuple: Last (c-state, h-state).
|
|
"""
|
|
sequence_length = x.shape[0 if time_major else 1]
|
|
batch_size = x.shape[1 if time_major else 0]
|
|
units = weights.shape[1] // 4 # 4 internal layers (3x sigmoid, 1x tanh)
|
|
|
|
if initial_internal_states is None:
|
|
c_states = np.zeros(shape=(batch_size, units))
|
|
h_states = np.zeros(shape=(batch_size, units))
|
|
else:
|
|
c_states = initial_internal_states[0]
|
|
h_states = initial_internal_states[1]
|
|
|
|
# Create a placeholder for all n-time step outputs.
|
|
if time_major:
|
|
unrolled_outputs = np.zeros(shape=(sequence_length, batch_size, units))
|
|
else:
|
|
unrolled_outputs = np.zeros(shape=(batch_size, sequence_length, units))
|
|
|
|
# Push the batch 4 times through the LSTM cell and capture the outputs plus
|
|
# the final h- and c-states.
|
|
for t in range(sequence_length):
|
|
input_matrix = x[t, :, :] if time_major else x[:, t, :]
|
|
input_matrix = np.concatenate((input_matrix, h_states), axis=1)
|
|
input_matmul_matrix = np.matmul(input_matrix, weights) + biases
|
|
# Forget gate (3rd slot in tf output matrix). Add static forget bias.
|
|
sigmoid_1 = sigmoid(input_matmul_matrix[:, units * 2:units * 3] +
|
|
forget_bias)
|
|
c_states = np.multiply(c_states, sigmoid_1)
|
|
# Add gate (1st and 2nd slots in tf output matrix).
|
|
sigmoid_2 = sigmoid(input_matmul_matrix[:, 0:units])
|
|
tanh_3 = np.tanh(input_matmul_matrix[:, units:units * 2])
|
|
c_states = np.add(c_states, np.multiply(sigmoid_2, tanh_3))
|
|
# Output gate (last slot in tf output matrix).
|
|
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)
|
|
|
|
|
|
def one_hot(x: Union[TensorType, int],
|
|
depth: int = 0,
|
|
on_value: int = 1.0,
|
|
off_value: float = 0.0) -> 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
|
|
|
|
# Python 2.7 compatibility, (*shape, depth) is not allowed.
|
|
shape_list = list(shape[:])
|
|
shape_list.append(depth)
|
|
out = np.ones(shape_list) * 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
|
|
|
|
|
|
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)
|
|
|
|
|
|
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))
|
|
|
|
|
|
def softmax(x: np.ndarray, 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)
|