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- import gym
- from gym.spaces import Discrete, MultiDiscrete
- import numpy as np
- import os
- import tree # pip install dm_tree
- from typing import Dict, List, Optional, TYPE_CHECKING
- import warnings
- from ray.rllib.models.repeated_values import RepeatedValues
- from ray.rllib.utils.deprecation import Deprecated
- from ray.rllib.utils.framework import try_import_torch
- from ray.rllib.utils.numpy import SMALL_NUMBER
- from ray.rllib.utils.typing import LocalOptimizer, SpaceStruct, TensorType, \
- TensorStructType
- if TYPE_CHECKING:
- from ray.rllib.policy.torch_policy import TorchPolicy
- torch, nn = try_import_torch()
- # Limit values suitable for use as close to a -inf logit. These are useful
- # since -inf / inf cause NaNs during backprop.
- FLOAT_MIN = -3.4e38
- FLOAT_MAX = 3.4e38
- def apply_grad_clipping(policy: "TorchPolicy", optimizer: LocalOptimizer,
- loss: TensorType) -> Dict[str, TensorType]:
- """Applies gradient clipping to already computed grads inside `optimizer`.
- Args:
- policy: The TorchPolicy, which calculated `loss`.
- optimizer: A local torch optimizer object.
- loss: The torch loss tensor.
- Returns:
- An info dict containing the "grad_norm" key and the resulting clipped
- gradients.
- """
- info = {}
- if policy.config["grad_clip"]:
- for param_group in optimizer.param_groups:
- # Make sure we only pass params with grad != None into torch
- # clip_grad_norm_. Would fail otherwise.
- params = list(
- filter(lambda p: p.grad is not None, param_group["params"]))
- if params:
- grad_gnorm = nn.utils.clip_grad_norm_(
- params, policy.config["grad_clip"])
- if isinstance(grad_gnorm, torch.Tensor):
- grad_gnorm = grad_gnorm.cpu().numpy()
- info["grad_gnorm"] = grad_gnorm
- return info
- @Deprecated(
- old="ray.rllib.utils.torch_utils.atanh",
- new="torch.math.atanh",
- error=False)
- def atanh(x: TensorType) -> TensorType:
- """Atanh function for PyTorch."""
- return 0.5 * torch.log(
- (1 + x).clamp(min=SMALL_NUMBER) / (1 - x).clamp(min=SMALL_NUMBER))
- def concat_multi_gpu_td_errors(policy: "TorchPolicy") -> Dict[str, TensorType]:
- """Concatenates multi-GPU (per-tower) TD error tensors given TorchPolicy.
- TD-errors are extracted from the TorchPolicy via its tower_stats property.
- Args:
- policy: The TorchPolicy to extract the TD-error values from.
- Returns:
- A dict mapping strings "td_error" and "mean_td_error" to the
- corresponding concatenated and mean-reduced values.
- """
- td_error = torch.cat(
- [
- t.tower_stats.get("td_error", torch.tensor([0.0])).to(
- policy.device) for t in policy.model_gpu_towers
- ],
- dim=0)
- policy.td_error = td_error
- return {
- "td_error": td_error,
- "mean_td_error": torch.mean(td_error),
- }
- @Deprecated(new="ray/rllib/utils/numpy.py::convert_to_numpy", error=False)
- def convert_to_non_torch_type(stats: TensorStructType) -> TensorStructType:
- """Converts values in `stats` to non-Tensor numpy or python types.
- Args:
- stats (any): Any (possibly nested) struct, the values in which will be
- converted and returned as a new struct with all torch tensors
- being converted to numpy types.
- Returns:
- Any: A new struct with the same structure as `stats`, but with all
- values converted to non-torch Tensor types.
- """
- # The mapping function used to numpyize torch Tensors.
- def mapping(item):
- if isinstance(item, torch.Tensor):
- return item.cpu().item() if len(item.size()) == 0 else \
- item.detach().cpu().numpy()
- else:
- return item
- return tree.map_structure(mapping, stats)
- def convert_to_torch_tensor(x: TensorStructType, device: Optional[str] = None):
- """Converts any struct to torch.Tensors.
- x (any): Any (possibly nested) struct, the values in which will be
- converted and returned as a new struct with all leaves converted
- to torch tensors.
- Returns:
- Any: A new struct with the same structure as `stats`, but with all
- values converted to torch Tensor types.
- """
- def mapping(item):
- # Already torch tensor -> make sure it's on right device.
- if torch.is_tensor(item):
- return item if device is None else item.to(device)
- # Special handling of "Repeated" values.
- elif isinstance(item, RepeatedValues):
- return RepeatedValues(
- tree.map_structure(mapping, item.values), item.lengths,
- item.max_len)
- # Numpy arrays.
- if isinstance(item, np.ndarray):
- # Object type (e.g. info dicts in train batch): leave as-is.
- if item.dtype == object:
- return item
- # Non-writable numpy-arrays will cause PyTorch warning.
- elif item.flags.writeable is False:
- with warnings.catch_warnings():
- warnings.simplefilter("ignore")
- tensor = torch.from_numpy(item)
- # Already numpy: Wrap as torch tensor.
- else:
- tensor = torch.from_numpy(item)
- # Everything else: Convert to numpy, then wrap as torch tensor.
- else:
- tensor = torch.from_numpy(np.asarray(item))
- # Floatify all float64 tensors.
- if tensor.dtype == torch.double:
- tensor = tensor.float()
- return tensor if device is None else tensor.to(device)
- return tree.map_structure(mapping, x)
- def explained_variance(y: TensorType, pred: TensorType) -> TensorType:
- """Computes the explained variance for a pair of labels and predictions.
- The formula used is:
- max(-1.0, 1.0 - (std(y - pred)^2 / std(y)^2))
- Args:
- y: The labels.
- pred: The predictions.
- Returns:
- The explained variance given a pair of labels and predictions.
- """
- y_var = torch.var(y, dim=[0])
- diff_var = torch.var(y - pred, dim=[0])
- min_ = torch.tensor([-1.0]).to(pred.device)
- return torch.max(min_, 1 - (diff_var / y_var))[0]
- def flatten_inputs_to_1d_tensor(inputs: TensorStructType,
- spaces_struct: Optional[SpaceStruct] = None,
- time_axis: bool = False) -> TensorType:
- """Flattens arbitrary input structs according to the given spaces struct.
- Returns a single 1D tensor resulting from the different input
- components' values.
- Thereby:
- - Boxes (any shape) get flattened to (B, [T]?, -1). Note that image boxes
- are not treated differently from other types of Boxes and get
- flattened as well.
- - Discrete (int) values are one-hot'd, e.g. a batch of [1, 0, 3] (B=3 with
- Discrete(4) space) results in [[0, 1, 0, 0], [1, 0, 0, 0], [0, 0, 0, 1]].
- - MultiDiscrete values are multi-one-hot'd, e.g. a batch of
- [[0, 2], [1, 4]] (B=2 with MultiDiscrete([2, 5]) space) results in
- [[1, 0, 0, 0, 1, 0, 0], [0, 1, 0, 0, 0, 0, 1]].
- Args:
- inputs: The inputs to be flattened.
- spaces_struct: The structure of the spaces that behind the input
- time_axis: Whether all inputs have a time-axis (after the batch axis).
- If True, will keep not only the batch axis (0th), but the time axis
- (1st) as-is and flatten everything from the 2nd axis up.
- Returns:
- A single 1D tensor resulting from concatenating all
- flattened/one-hot'd input components. Depending on the time_axis flag,
- the shape is (B, n) or (B, T, n).
- Examples:
- >>> # B=2
- >>> out = flatten_inputs_to_1d_tensor(
- ... {"a": [1, 0], "b": [[[0.0], [0.1]], [1.0], [1.1]]},
- ... spaces_struct=dict(a=Discrete(2), b=Box(shape=(2, 1)))
- ... )
- >>> print(out)
- ... [[0.0, 1.0, 0.0, 0.1], [1.0, 0.0, 1.0, 1.1]] # B=2 n=4
- >>> # B=2; T=2
- >>> out = flatten_inputs_to_1d_tensor(
- ... ([[1, 0], [0, 1]],
- ... [[[0.0, 0.1], [1.0, 1.1]], [[2.0, 2.1], [3.0, 3.1]]]),
- ... spaces_struct=tuple([Discrete(2), Box(shape=(2, ))]),
- ... time_axis=True
- ... )
- >>> print(out)
- ... [[[0.0, 1.0, 0.0, 0.1], [1.0, 0.0, 1.0, 1.1]],
- ... [[1.0, 0.0, 2.0, 2.1], [0.0, 1.0, 3.0, 3.1]]] # B=2 T=2 n=4
- """
- flat_inputs = tree.flatten(inputs)
- flat_spaces = tree.flatten(spaces_struct) if spaces_struct is not None \
- else [None] * len(flat_inputs)
- B = None
- T = None
- out = []
- for input_, space in zip(flat_inputs, flat_spaces):
- # Store batch and (if applicable) time dimension.
- if B is None:
- B = input_.shape[0]
- if time_axis:
- T = input_.shape[1]
- # One-hot encoding.
- if isinstance(space, Discrete):
- if time_axis:
- input_ = torch.reshape(input_, [B * T])
- out.append(one_hot(input_, space).float())
- # Multi one-hot encoding.
- elif isinstance(space, MultiDiscrete):
- if time_axis:
- input_ = torch.reshape(input_, [B * T, -1])
- out.append(one_hot(input_, space).float())
- # Box: Flatten.
- else:
- if time_axis:
- input_ = torch.reshape(input_, [B * T, -1])
- else:
- input_ = torch.reshape(input_, [B, -1])
- out.append(input_.float())
- merged = torch.cat(out, dim=-1)
- # Restore the time-dimension, if applicable.
- if time_axis:
- merged = torch.reshape(merged, [B, T, -1])
- return merged
- def global_norm(tensors: List[TensorType]) -> TensorType:
- """Returns the global L2 norm over a list of tensors.
- output = sqrt(SUM(t ** 2 for t in tensors)),
- where SUM reduces over all tensors and over all elements in tensors.
- Args:
- tensors: The list of tensors to calculate the global norm over.
- Returns:
- The global L2 norm over the given tensor list.
- """
- # List of single tensors' L2 norms: SQRT(SUM(xi^2)) over all xi in tensor.
- single_l2s = [
- torch.pow(torch.sum(torch.pow(t, 2.0)), 0.5) for t in tensors
- ]
- # Compute global norm from all single tensors' L2 norms.
- return torch.pow(sum(torch.pow(l2, 2.0) for l2 in single_l2s), 0.5)
- def huber_loss(x: TensorType, delta: float = 1.0) -> TensorType:
- """Computes the huber loss for a given term and delta parameter.
- Reference: https://en.wikipedia.org/wiki/Huber_loss
- Note that the factor of 0.5 is implicitly included in the calculation.
- Formula:
- L = 0.5 * x^2 for small abs x (delta threshold)
- L = delta * (abs(x) - 0.5*delta) for larger abs x (delta threshold)
- Args:
- x: The input term, e.g. a TD error.
- delta: The delta parmameter in the above formula.
- Returns:
- The Huber loss resulting from `x` and `delta`.
- """
- return torch.where(
- torch.abs(x) < delta,
- torch.pow(x, 2.0) * 0.5, delta * (torch.abs(x) - 0.5 * delta))
- def l2_loss(x: TensorType) -> TensorType:
- """Computes half the L2 norm over a tensor's values without the sqrt.
- output = 0.5 * sum(x ** 2)
- Args:
- x: The input tensor.
- Returns:
- 0.5 times the L2 norm over the given tensor's values (w/o sqrt).
- """
- return 0.5 * torch.sum(torch.pow(x, 2.0))
- def minimize_and_clip(optimizer: "torch.optim.Optimizer",
- clip_val: float = 10.0) -> None:
- """Clips grads found in `optimizer.param_groups` to given value in place.
- Ensures the norm of the gradients for each variable is clipped to
- `clip_val`.
- Args:
- optimizer: The torch.optim.Optimizer to get the variables from.
- clip_val: The global norm clip value. Will clip around -clip_val and
- +clip_val.
- """
- # Loop through optimizer's variables and norm per variable.
- for param_group in optimizer.param_groups:
- for p in param_group["params"]:
- if p.grad is not None:
- torch.nn.utils.clip_grad_norm_(p.grad, clip_val)
- def one_hot(x: TensorType, space: gym.Space) -> TensorType:
- """Returns a one-hot tensor, given and int tensor and a space.
- Handles the MultiDiscrete case as well.
- Args:
- x: The input tensor.
- space: The space to use for generating the one-hot tensor.
- Returns:
- The resulting one-hot tensor.
- Raises:
- ValueError: If the given space is not a discrete one.
- Examples:
- >>> x = torch.IntTensor([0, 3]) # batch-dim=2
- >>> # Discrete space with 4 (one-hot) slots per batch item.
- >>> s = gym.spaces.Discrete(4)
- >>> one_hot(x, s)
- tensor([[1, 0, 0, 0], [0, 0, 0, 1]])
- >>> x = torch.IntTensor([[0, 1, 2, 3]]) # batch-dim=1
- >>> # MultiDiscrete space with 5 + 4 + 4 + 7 = 20 (one-hot) slots
- >>> # per batch item.
- >>> s = gym.spaces.MultiDiscrete([5, 4, 4, 7])
- >>> one_hot(x, s)
- tensor([[1, 0, 0, 0, 0,
- 0, 1, 0, 0,
- 0, 0, 1, 0,
- 0, 0, 0, 1, 0, 0, 0]])
- """
- if isinstance(space, Discrete):
- return nn.functional.one_hot(x.long(), space.n)
- elif isinstance(space, MultiDiscrete):
- return torch.cat(
- [
- nn.functional.one_hot(x[:, i].long(), n)
- for i, n in enumerate(space.nvec)
- ],
- dim=-1)
- else:
- raise ValueError("Unsupported space for `one_hot`: {}".format(space))
- def reduce_mean_ignore_inf(x: TensorType,
- axis: Optional[int] = None) -> TensorType:
- """Same as torch.mean() but ignores -inf values.
- Args:
- x: The input tensor to reduce mean over.
- axis: The axis over which to reduce. None for all axes.
- Returns:
- The mean reduced inputs, ignoring inf values.
- """
- mask = torch.ne(x, float("-inf"))
- x_zeroed = torch.where(mask, x, torch.zeros_like(x))
- return torch.sum(x_zeroed, axis) / torch.sum(mask.float(), axis)
- def sequence_mask(
- lengths: TensorType,
- maxlen: Optional[int] = None,
- dtype=None,
- time_major: bool = False,
- ) -> TensorType:
- """Offers same behavior as tf.sequence_mask for torch.
- Thanks to Dimitris Papatheodorou
- (https://discuss.pytorch.org/t/pytorch-equivalent-for-tf-sequence-mask/
- 39036).
- Args:
- lengths: The tensor of individual lengths to mask by.
- maxlen: The maximum length to use for the time axis. If None, use
- the max of `lengths`.
- dtype: The torch dtype to use for the resulting mask.
- time_major: Whether to return the mask as [B, T] (False; default) or
- as [T, B] (True).
- Returns:
- The sequence mask resulting from the given input and parameters.
- """
- # If maxlen not given, use the longest lengths in the `lengths` tensor.
- if maxlen is None:
- maxlen = int(lengths.max())
- mask = ~(torch.ones(
- (len(lengths), maxlen)).to(lengths.device).cumsum(dim=1).t() > lengths)
- # Time major transformation.
- if not time_major:
- mask = mask.t()
- # By default, set the mask to be boolean.
- mask.type(dtype or torch.bool)
- return mask
- def set_torch_seed(seed: Optional[int] = None) -> None:
- """Sets the torch random seed to the given value.
- Args:
- seed: The seed to use or None for no seeding.
- """
- if seed is not None and torch:
- torch.manual_seed(seed)
- # See https://github.com/pytorch/pytorch/issues/47672.
- cuda_version = torch.version.cuda
- if cuda_version is not None and float(torch.version.cuda) >= 10.2:
- os.environ["CUBLAS_WORKSPACE_CONFIG"] = "4096:8"
- else:
- # Not all Operations support this.
- torch.use_deterministic_algorithms(True)
- # This is only for Convolution no problem.
- torch.backends.cudnn.deterministic = True
- def softmax_cross_entropy_with_logits(
- logits: TensorType,
- labels: TensorType,
- ) -> TensorType:
- """Same behavior as tf.nn.softmax_cross_entropy_with_logits.
- Args:
- x: The input predictions.
- labels: The labels corresponding to `x`.
- Returns:
- The resulting softmax cross-entropy given predictions and labels.
- """
- return torch.sum(-labels * nn.functional.log_softmax(logits, -1), -1)
- class Swish(nn.Module):
- def __init__(self):
- super().__init__()
- self._beta = nn.Parameter(torch.tensor(1.0))
- def forward(self, input_tensor):
- return input_tensor * torch.sigmoid(self._beta * input_tensor)
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