applied formatter to tools
This commit is contained in:
NM512
parent
55ed69bdf7
commit
fba87a33e0
228
tools.py
228
tools.py
@ -20,14 +20,16 @@ from torch.utils.tensorboard import SummaryWriter
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to_np = lambda x: x.detach().cpu().numpy()
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def symlog(x):
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return torch.sign(x) * torch.log(torch.abs(x) + 1.0)
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def symexp(x):
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return torch.sign(x) * (torch.exp(torch.abs(x)) - 1.0)
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class RequiresGrad:
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class RequiresGrad:
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def __init__(self, model):
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self._model = model
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@ -39,7 +41,6 @@ class RequiresGrad:
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class TimeRecording:
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def __init__(self, comment):
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self._comment = comment
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@ -55,7 +56,6 @@ class TimeRecording:
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class Logger:
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def __init__(self, logdir, step):
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self._logdir = logdir
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self._writer = SummaryWriter(log_dir=str(logdir), max_queue=1000)
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@ -80,16 +80,16 @@ class Logger:
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step = self.step
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scalars = list(self._scalars.items())
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if fps:
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scalars.append(('fps', self._compute_fps(step)))
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print(f'[{step}]', ' / '.join(f'{k} {v:.1f}' for k, v in scalars))
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with (self._logdir / 'metrics.jsonl').open('a') as f:
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f.write(json.dumps({'step': step, ** dict(scalars)}) + '\n')
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scalars.append(("fps", self._compute_fps(step)))
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print(f"[{step}]", " / ".join(f"{k} {v:.1f}" for k, v in scalars))
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with (self._logdir / "metrics.jsonl").open("a") as f:
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f.write(json.dumps({"step": step, **dict(scalars)}) + "\n")
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for name, value in scalars:
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self._writer.add_scalar('scalars/' + name, value, step)
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self._writer.add_scalar("scalars/" + name, value, step)
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for name, value in self._images.items():
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self._writer.add_image(name, value, step)
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for name, value in self._videos.items():
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name = name if isinstance(name, str) else name.decode('utf-8')
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name = name if isinstance(name, str) else name.decode("utf-8")
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if np.issubdtype(value.dtype, np.floating):
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value = np.clip(255 * value, 0, 255).astype(np.uint8)
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B, T, H, W, C = value.shape
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@ -113,7 +113,7 @@ class Logger:
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return steps / duration
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def offline_scalar(self, name, value, step):
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self._writer.add_scalar('scalars/'+name, value, step)
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self._writer.add_scalar("scalars/" + name, value, step)
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def offline_video(self, name, value, step):
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if np.issubdtype(value.dtype, np.floating):
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@ -148,7 +148,8 @@ def simulate(agent, envs, steps=0, episodes=0, state=None):
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if isinstance(action, dict):
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action = [
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{k: np.array(action[k][i].detach().cpu()) for k in action}
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for i in range(len(envs))]
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for i in range(len(envs))
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]
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else:
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action = np.array(action)
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assert len(action) == len(envs)
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@ -161,7 +162,7 @@ def simulate(agent, envs, steps=0, episodes=0, state=None):
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episode += int(done.sum())
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length += 1
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step += (done * length).sum()
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length *= (1 - done)
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length *= 1 - done
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return (step - steps, episode - episodes, done, length, obs, agent_state, reward)
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@ -169,16 +170,16 @@ def simulate(agent, envs, steps=0, episodes=0, state=None):
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def save_episodes(directory, episodes):
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directory = pathlib.Path(directory).expanduser()
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directory.mkdir(parents=True, exist_ok=True)
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timestamp = datetime.datetime.now().strftime('%Y%m%dT%H%M%S')
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timestamp = datetime.datetime.now().strftime("%Y%m%dT%H%M%S")
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filenames = []
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for episode in episodes:
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identifier = str(uuid.uuid4().hex)
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length = len(episode['reward'])
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filename = directory / f'{timestamp}-{identifier}-{length}.npz'
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length = len(episode["reward"])
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filename = directory / f"{timestamp}-{identifier}-{length}.npz"
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with io.BytesIO() as f1:
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np.savez_compressed(f1, **episode)
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f1.seek(0)
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with filename.open('wb') as f2:
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with filename.open("wb") as f2:
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f2.write(f1.read())
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filenames.append(filename)
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return filenames
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@ -206,7 +207,7 @@ def sample_episodes(episodes, length=None, balance=False, seed=0):
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total = len(next(iter(episode.values())))
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available = total - length
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if available < 1:
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print(f'Skipped short episode of length {available}.')
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print(f"Skipped short episode of length {available}.")
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continue
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if balance:
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index = min(random.randint(0, total), available)
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@ -221,43 +222,42 @@ def load_episodes(directory, limit=None, reverse=True):
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episodes = collections.OrderedDict()
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total = 0
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if reverse:
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for filename in reversed(sorted(directory.glob('*.npz'))):
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for filename in reversed(sorted(directory.glob("*.npz"))):
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try:
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with filename.open('rb') as f:
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with filename.open("rb") as f:
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episode = np.load(f)
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episode = {k: episode[k] for k in episode.keys()}
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except Exception as e:
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print(f'Could not load episode: {e}')
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print(f"Could not load episode: {e}")
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continue
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episodes[str(filename)] = episode
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total += len(episode['reward']) - 1
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total += len(episode["reward"]) - 1
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if limit and total >= limit:
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break
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else:
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for filename in sorted(directory.glob('*.npz')):
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for filename in sorted(directory.glob("*.npz")):
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try:
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with filename.open('rb') as f:
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with filename.open("rb") as f:
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episode = np.load(f)
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episode = {k: episode[k] for k in episode.keys()}
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except Exception as e:
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print(f'Could not load episode: {e}')
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print(f"Could not load episode: {e}")
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continue
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episodes[str(filename)] = episode
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total += len(episode['reward']) - 1
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total += len(episode["reward"]) - 1
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if limit and total >= limit:
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break
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return episodes
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class SampleDist:
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def __init__(self, dist, samples=100):
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self._dist = dist
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self._samples = samples
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@property
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def name(self):
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return 'SampleDist'
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return "SampleDist"
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def __getattr__(self, name):
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return getattr(self._dist, name)
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@ -278,7 +278,6 @@ class SampleDist:
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class OneHotDist(torchd.one_hot_categorical.OneHotCategorical):
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def __init__(self, logits=None, probs=None, unimix_ratio=0.0):
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if logits is not None and unimix_ratio > 0.0:
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probs = F.softmax(logits, dim=-1)
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@ -289,12 +288,14 @@ class OneHotDist(torchd.one_hot_categorical.OneHotCategorical):
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super().__init__(logits=logits, probs=probs)
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def mode(self):
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_mode = F.one_hot(torch.argmax(super().logits, axis=-1), super().logits.shape[-1])
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_mode = F.one_hot(
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torch.argmax(super().logits, axis=-1), super().logits.shape[-1]
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)
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return _mode.detach() + super().logits - super().logits.detach()
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def sample(self, sample_shape=(), seed=None):
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if seed is not None:
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raise ValueError('need to check')
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raise ValueError("need to check")
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sample = super().sample(sample_shape)
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probs = super().probs
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while len(probs.shape) < len(sample.shape):
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@ -303,9 +304,8 @@ class OneHotDist(torchd.one_hot_categorical.OneHotCategorical):
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return sample
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class TwoHotDistSymlog():
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def __init__(self, logits=None, low=-20.0, high=20.0, device='cuda'):
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class TwoHotDistSymlog:
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def __init__(self, logits=None, low=-20.0, high=20.0, device="cuda"):
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self.logits = logits
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self.probs = torch.softmax(logits, -1)
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self.buckets = torch.linspace(low, high, steps=255).to(device)
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@ -325,10 +325,12 @@ class TwoHotDistSymlog():
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x = symlog(x)
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# x(time, batch, 1)
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below = torch.sum((self.buckets <= x[..., None]).to(torch.int32), dim=-1) - 1
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above = len(self.buckets) - torch.sum((self.buckets > x[..., None]).to(torch.int32), dim=-1)
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above = len(self.buckets) - torch.sum(
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(self.buckets > x[..., None]).to(torch.int32), dim=-1
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)
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below = torch.clip(below, 0, len(self.buckets) - 1)
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above = torch.clip(above, 0, len(self.buckets) - 1)
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equal = (below == above)
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equal = below == above
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dist_to_below = torch.where(equal, 1, torch.abs(self.buckets[below] - x))
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dist_to_above = torch.where(equal, 1, torch.abs(self.buckets[above] - x))
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@ -336,8 +338,9 @@ class TwoHotDistSymlog():
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weight_below = dist_to_above / total
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weight_above = dist_to_below / total
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target = (
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F.one_hot(below, num_classes=len(self.buckets)) * weight_below[..., None] +
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F.one_hot(above, num_classes=len(self.buckets)) * weight_above[..., None])
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F.one_hot(below, num_classes=len(self.buckets)) * weight_below[..., None]
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+ F.one_hot(above, num_classes=len(self.buckets)) * weight_above[..., None]
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)
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log_pred = self.logits - torch.logsumexp(self.logits, -1, keepdim=True)
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target = target.squeeze(-2)
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@ -347,8 +350,11 @@ class TwoHotDistSymlog():
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log_pred = super().logits - torch.logsumexp(super().logits, -1, keepdim=True)
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return (target * log_pred).sum(-1)
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class SymlogDist():
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def __init__(self, mode, dist='mse', agg='sum', tol=1e-8, dim_to_reduce=[-1, -2, -3]):
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class SymlogDist:
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def __init__(
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self, mode, dist="mse", agg="sum", tol=1e-8, dim_to_reduce=[-1, -2, -3]
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):
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self._mode = mode
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self._dist = dist
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self._agg = agg
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@ -363,24 +369,24 @@ class SymlogDist():
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def log_prob(self, value):
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assert self._mode.shape == value.shape
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if self._dist == 'mse':
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if self._dist == "mse":
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distance = (self._mode - symlog(value)) ** 2.0
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distance = torch.where(distance < self._tol, 0, distance)
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elif self._dist == 'abs':
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elif self._dist == "abs":
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distance = torch.abs(self._mode - symlog(value))
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distance = torch.where(distance < self._tol, 0, distance)
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else:
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raise NotImplementedError(self._dist)
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if self._agg == 'mean':
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if self._agg == "mean":
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loss = distance.mean(self._dim_to_reduce)
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elif self._agg == 'sum':
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elif self._agg == "sum":
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loss = distance.sum(self._dim_to_reduce)
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else:
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raise NotImplementedError(self._agg)
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return -loss
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class ContDist:
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class ContDist:
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def __init__(self, dist=None):
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super().__init__()
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self._dist = dist
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@ -403,7 +409,6 @@ class ContDist:
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class Bernoulli:
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def __init__(self, dist=None):
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super().__init__()
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self._dist = dist
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@ -431,21 +436,21 @@ class Bernoulli:
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class UnnormalizedHuber(torchd.normal.Normal):
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def __init__(self, loc, scale, threshold=1, **kwargs):
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super().__init__(loc, scale, **kwargs)
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self._threshold = threshold
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def log_prob(self, event):
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return -(torch.sqrt(
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(event - self.mean) ** 2 + self._threshold ** 2) - self._threshold)
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return -(
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torch.sqrt((event - self.mean) ** 2 + self._threshold**2)
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- self._threshold
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)
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def mode(self):
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return self.mean
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class SafeTruncatedNormal(torchd.normal.Normal):
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def __init__(self, loc, scale, low, high, clip=1e-6, mult=1):
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super().__init__(loc, scale)
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self._low = low
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@ -456,8 +461,7 @@ class SafeTruncatedNormal(torchd.normal.Normal):
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def sample(self, sample_shape):
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event = super().sample(sample_shape)
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if self._clip:
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clipped = torch.clip(event, self._low + self._clip,
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self._high - self._clip)
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clipped = torch.clip(event, self._low + self._clip, self._high - self._clip)
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event = event - event.detach() + clipped.detach()
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if self._mult:
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event *= self._mult
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@ -465,8 +469,7 @@ class SafeTruncatedNormal(torchd.normal.Normal):
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class TanhBijector(torchd.Transform):
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def __init__(self, validate_args=False, name='tanh'):
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def __init__(self, validate_args=False, name="tanh"):
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super().__init__()
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def _forward(self, x):
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@ -474,8 +477,8 @@ class TanhBijector(torchd.Transform):
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def _inverse(self, y):
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y = torch.where(
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(torch.abs(y) <= 1.),
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torch.clamp(y, -0.99999997, 0.99999997), y)
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(torch.abs(y) <= 1.0), torch.clamp(y, -0.99999997, 0.99999997), y
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)
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y = torch.atanh(y)
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return y
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@ -504,8 +507,7 @@ def static_scan_for_lambda_return(fn, inputs, start):
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return outputs
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def lambda_return(
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reward, value, pcont, bootstrap, lambda_, axis):
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def lambda_return(reward, value, pcont, bootstrap, lambda_, axis):
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# Setting lambda=1 gives a discounted Monte Carlo return.
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# Setting lambda=0 gives a fixed 1-step return.
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# assert reward.shape.ndims == value.shape.ndims, (reward.shape, value.shape)
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@ -527,18 +529,26 @@ def lambda_return(
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# (inputs, pcont), bootstrap, reverse=True)
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# reimplement to optimize performance
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returns = static_scan_for_lambda_return(
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lambda agg, cur0, cur1: cur0 + cur1 * lambda_ * agg,
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(inputs, pcont), bootstrap)
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lambda agg, cur0, cur1: cur0 + cur1 * lambda_ * agg, (inputs, pcont), bootstrap
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)
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if axis != 0:
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returns = returns.permute(dims)
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return returns
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class Optimizer():
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class Optimizer:
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def __init__(
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self, name, parameters, lr, eps=1e-4, clip=None, wd=None, wd_pattern=r'.*',
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opt='adam', use_amp=False):
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self,
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name,
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parameters,
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lr,
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eps=1e-4,
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clip=None,
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wd=None,
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wd_pattern=r".*",
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opt="adam",
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use_amp=False,
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):
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assert 0 <= wd < 1
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assert not clip or 1 <= clip
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self._name = name
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@ -547,26 +557,18 @@ class Optimizer():
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self._wd = wd
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self._wd_pattern = wd_pattern
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self._opt = {
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'adam': lambda: torch.optim.Adam(parameters,
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lr=lr,
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eps=eps),
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'nadam': lambda: NotImplemented(
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f'{opt} is not implemented'),
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'adamax': lambda: torch.optim.Adamax(parameters,
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lr=lr,
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eps=eps),
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'sgd': lambda: torch.optim.SGD(parameters,
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lr=lr),
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'momentum': lambda: torch.optim.SGD(parameters,
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lr=lr,
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momentum=0.9),
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"adam": lambda: torch.optim.Adam(parameters, lr=lr, eps=eps),
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"nadam": lambda: NotImplemented(f"{opt} is not implemented"),
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"adamax": lambda: torch.optim.Adamax(parameters, lr=lr, eps=eps),
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"sgd": lambda: torch.optim.SGD(parameters, lr=lr),
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"momentum": lambda: torch.optim.SGD(parameters, lr=lr, momentum=0.9),
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}[opt]()
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self._scaler = torch.cuda.amp.GradScaler(enabled=use_amp)
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def __call__(self, loss, params, retain_graph=False):
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assert len(loss.shape) == 0, loss.shape
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metrics = {}
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metrics[f'{self._name}_loss'] = loss.detach().cpu().numpy()
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metrics[f"{self._name}_loss"] = loss.detach().cpu().numpy()
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self._scaler.scale(loss).backward()
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self._scaler.unscale_(self._opt)
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# loss.backward(retain_graph=retain_graph)
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@ -577,11 +579,11 @@ class Optimizer():
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self._scaler.update()
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# self._opt.step()
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self._opt.zero_grad()
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metrics[f'{self._name}_grad_norm'] = norm.item()
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metrics[f"{self._name}_grad_norm"] = norm.item()
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return metrics
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def _apply_weight_decay(self, varibs):
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nontrivial = (self._wd_pattern != r'.*')
|
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nontrivial = self._wd_pattern != r".*"
|
||||
if nontrivial:
|
||||
raise NotImplementedError
|
||||
for var in varibs:
|
||||
@ -593,16 +595,18 @@ def args_type(default):
|
||||
if default is None:
|
||||
return x
|
||||
if isinstance(default, bool):
|
||||
return bool(['False', 'True'].index(x))
|
||||
return bool(["False", "True"].index(x))
|
||||
if isinstance(default, int):
|
||||
return float(x) if ('e' in x or '.' in x) else int(x)
|
||||
return float(x) if ("e" in x or "." in x) else int(x)
|
||||
if isinstance(default, (list, tuple)):
|
||||
return tuple(args_type(default[0])(y) for y in x.split(','))
|
||||
return tuple(args_type(default[0])(y) for y in x.split(","))
|
||||
return type(default)(x)
|
||||
|
||||
def parse_object(x):
|
||||
if isinstance(default, (list, tuple)):
|
||||
return tuple(x)
|
||||
return x
|
||||
|
||||
return lambda x: parse_string(x) if isinstance(x, str) else parse_object(x)
|
||||
|
||||
|
||||
@ -615,27 +619,39 @@ def static_scan(fn, inputs, start):
|
||||
last = fn(last, *inp(index))
|
||||
if flag:
|
||||
if type(last) == type({}):
|
||||
outputs = {key: value.clone().unsqueeze(0) for key, value in last.items()}
|
||||
outputs = {
|
||||
key: value.clone().unsqueeze(0) for key, value in last.items()
|
||||
}
|
||||
else:
|
||||
outputs = []
|
||||
for _last in last:
|
||||
if type(_last) == type({}):
|
||||
outputs.append({key: value.clone().unsqueeze(0) for key, value in _last.items()})
|
||||
outputs.append(
|
||||
{
|
||||
key: value.clone().unsqueeze(0)
|
||||
for key, value in _last.items()
|
||||
}
|
||||
)
|
||||
else:
|
||||
outputs.append(_last.clone().unsqueeze(0))
|
||||
flag = False
|
||||
else:
|
||||
if type(last) == type({}):
|
||||
for key in last.keys():
|
||||
outputs[key] = torch.cat([outputs[key], last[key].unsqueeze(0)], dim=0)
|
||||
outputs[key] = torch.cat(
|
||||
[outputs[key], last[key].unsqueeze(0)], dim=0
|
||||
)
|
||||
else:
|
||||
for j in range(len(outputs)):
|
||||
if type(last[j]) == type({}):
|
||||
for key in last[j].keys():
|
||||
outputs[j][key] = torch.cat([outputs[j][key],
|
||||
last[j][key].unsqueeze(0)], dim=0)
|
||||
outputs[j][key] = torch.cat(
|
||||
[outputs[j][key], last[j][key].unsqueeze(0)], dim=0
|
||||
)
|
||||
else:
|
||||
outputs[j] = torch.cat([outputs[j], last[j].unsqueeze(0)], dim=0)
|
||||
outputs[j] = torch.cat(
|
||||
[outputs[j], last[j].unsqueeze(0)], dim=0
|
||||
)
|
||||
if type(last) == type({}):
|
||||
outputs = [outputs]
|
||||
return outputs
|
||||
@ -673,7 +689,6 @@ def static_scan(fn, inputs, start):
|
||||
|
||||
|
||||
class Every:
|
||||
|
||||
def __init__(self, every):
|
||||
self._every = every
|
||||
self._last = None
|
||||
@ -688,8 +703,8 @@ class Every:
|
||||
self._last += self._every * count
|
||||
return count
|
||||
|
||||
class Once:
|
||||
|
||||
class Once:
|
||||
def __init__(self):
|
||||
self._once = True
|
||||
|
||||
@ -701,7 +716,6 @@ class Once:
|
||||
|
||||
|
||||
class Until:
|
||||
|
||||
def __init__(self, until):
|
||||
self._until = until
|
||||
|
||||
@ -715,21 +729,21 @@ def schedule(string, step):
|
||||
try:
|
||||
return float(string)
|
||||
except ValueError:
|
||||
match = re.match(r'linear\((.+),(.+),(.+)\)', string)
|
||||
match = re.match(r"linear\((.+),(.+),(.+)\)", string)
|
||||
if match:
|
||||
initial, final, duration = [float(group) for group in match.groups()]
|
||||
mix = torch.clip(torch.Tensor([step / duration]), 0, 1)[0]
|
||||
return (1 - mix) * initial + mix * final
|
||||
match = re.match(r'warmup\((.+),(.+)\)', string)
|
||||
match = re.match(r"warmup\((.+),(.+)\)", string)
|
||||
if match:
|
||||
warmup, value = [float(group) for group in match.groups()]
|
||||
scale = torch.clip(step / warmup, 0, 1)
|
||||
return scale * value
|
||||
match = re.match(r'exp\((.+),(.+),(.+)\)', string)
|
||||
match = re.match(r"exp\((.+),(.+),(.+)\)", string)
|
||||
if match:
|
||||
initial, final, halflife = [float(group) for group in match.groups()]
|
||||
return (initial - final) * 0.5 ** (step / halflife) + final
|
||||
match = re.match(r'horizon\((.+),(.+),(.+)\)', string)
|
||||
match = re.match(r"horizon\((.+),(.+),(.+)\)", string)
|
||||
if match:
|
||||
initial, final, duration = [float(group) for group in match.groups()]
|
||||
mix = torch.clip(step / duration, 0, 1)
|
||||
@ -737,6 +751,7 @@ def schedule(string, step):
|
||||
return 1 - 1 / horizon
|
||||
raise NotImplementedError(string)
|
||||
|
||||
|
||||
def weight_init(m):
|
||||
if isinstance(m, nn.Linear):
|
||||
in_num = m.in_features
|
||||
@ -745,7 +760,7 @@ def weight_init(m):
|
||||
scale = 1.0 / denoms
|
||||
std = np.sqrt(scale) / 0.87962566103423978
|
||||
nn.init.trunc_normal_(m.weight.data, mean=0.0, std=std, a=-2.0, b=2.0)
|
||||
if hasattr(m.bias, 'data'):
|
||||
if hasattr(m.bias, "data"):
|
||||
m.bias.data.fill_(0.0)
|
||||
elif isinstance(m, nn.Conv2d) or isinstance(m, nn.ConvTranspose2d):
|
||||
space = m.kernel_size[0] * m.kernel_size[1]
|
||||
@ -755,13 +770,14 @@ def weight_init(m):
|
||||
scale = 1.0 / denoms
|
||||
std = np.sqrt(scale) / 0.87962566103423978
|
||||
nn.init.trunc_normal_(m.weight.data, mean=0.0, std=std, a=-2.0, b=2.0)
|
||||
if hasattr(m.bias, 'data'):
|
||||
if hasattr(m.bias, "data"):
|
||||
m.bias.data.fill_(0.0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
m.weight.data.fill_(1.0)
|
||||
if hasattr(m.bias, 'data'):
|
||||
if hasattr(m.bias, "data"):
|
||||
m.bias.data.fill_(0.0)
|
||||
|
||||
|
||||
def uniform_weight_init(given_scale):
|
||||
def f(m):
|
||||
if isinstance(m, nn.Linear):
|
||||
@ -771,21 +787,23 @@ def uniform_weight_init(given_scale):
|
||||
scale = given_scale / denoms
|
||||
limit = np.sqrt(3 * scale)
|
||||
nn.init.uniform_(m.weight.data, a=-limit, b=limit)
|
||||
if hasattr(m.bias, 'data'):
|
||||
if hasattr(m.bias, "data"):
|
||||
m.bias.data.fill_(0.0)
|
||||
elif isinstance(m, nn.LayerNorm):
|
||||
m.weight.data.fill_(1.0)
|
||||
if hasattr(m.bias, 'data'):
|
||||
if hasattr(m.bias, "data"):
|
||||
m.bias.data.fill_(0.0)
|
||||
|
||||
return f
|
||||
|
||||
|
||||
def tensorstats(tensor, prefix=None):
|
||||
metrics = {
|
||||
'mean': to_np(torch.mean(tensor)),
|
||||
'std': to_np(torch.std(tensor)),
|
||||
'min': to_np(torch.min(tensor)),
|
||||
'max': to_np(torch.max(tensor)),
|
||||
"mean": to_np(torch.mean(tensor)),
|
||||
"std": to_np(torch.std(tensor)),
|
||||
"min": to_np(torch.min(tensor)),
|
||||
"max": to_np(torch.max(tensor)),
|
||||
}
|
||||
if prefix:
|
||||
metrics = {f'{prefix}_{k}': v for k, v in metrics.items()}
|
||||
metrics = {f"{prefix}_{k}": v for k, v in metrics.items()}
|
||||
return metrics
|
||||
|
||||
Loading…
x
Reference in New Issue
Block a user