mirror of
https://github.com/k2-fsa/icefall.git
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337 lines
12 KiB
Python
337 lines
12 KiB
Python
import torch
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from torch import Tensor
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from torch import nn
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import math
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import random
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from typing import Tuple, List
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class TensorDiagnosticOptions(object):
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"""
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Options object for tensor diagnostics:
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Args:
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memory_limit: the maximum number of bytes we store per tensor (limits how many copies
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of the tensor we cache).
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max_eig_dim: the maximum dimension for which we print out eigenvalues
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(limited for speed reasons).
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"""
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def __init__(self,
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memory_limit: int = (2 ** 20),
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max_eig_dim: int = 512):
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self.memory_limit = memory_limit
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self.max_eig_dim = max_eig_dim
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def dim_is_summarized(self, size: int):
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return size > 10 and size != 31
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def get_tensor_stats(x: Tensor, dim: int,
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stats_type: str) -> Tuple[Tensor, int]:
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"""
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Returns the specified transformation of the Tensor (either x or x.abs()
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or (x > 0), summed over all but the index `dim`.
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Args:
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x: Tensor, tensor to be analyzed
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dim: dimension with 0 <= dim < x.ndim
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stats_type:
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"abs" -> take abs() before summing
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"positive" -> take (x > 0) before summing
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"rms" -> square before summing, we'll take sqrt later
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"value -> just sum x itself
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Returns (stats, count)
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where stats is a Tensor of shape (x.shape[dim],), and the count
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is an integer saying how many items were counted in each element
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of stats.
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"""
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count = x.numel() // x.shape[dim]
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if stats_type == "eigs":
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x = x.transpose(dim, -1)
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x = x.reshape(-1, x.shape[-1])
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# shape of returned tensor: (s, s) where s is size of dimension `dim` of original x.
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return torch.matmul(x.transpose(0, 1), x), count
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elif stats_type == "abs":
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x = x.abs()
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elif stats_type == "rms":
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x = x ** 2
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elif stats_type == "positive":
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x = (x > 0).to(dtype=torch.float)
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else:
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assert stats_type == "value"
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sum_dims = [ d for d in range(x.ndim) if d != dim ]
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if len(sum_dims) > 0:
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x = torch.sum(x, dim=sum_dims)
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x = x.flatten()
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return x, count
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def get_diagnostics_for_dim(dim: int, tensors: List[Tensor],
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options: TensorDiagnosticOptions,
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sizes_same: bool,
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stats_type: str):
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"""
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This function gets diagnostics for a dimension of a module.
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Args:
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dim: the dimension to analyze, with 0 <= dim < tensors[0].ndim
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options: options object
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sizes_same: true if all the tensor sizes are the same on this dimension
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stats_type: either "abs" or "positive" or "eigs" or "value",
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imdictates the type of stats
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we accumulate, abs is mean absolute value, "positive"
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is proportion of positive to nonnegative values, "eigs"
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is eigenvalues after doing outer product on this dim, sum
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over all other dimes.
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Returns:
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Diagnostic as a string, either percentiles or the actual values,
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see the code. Will return the empty string if the diagnostics did
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not make sense to print out for this dimension, e.g. dimension
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mismatch and stats_type == "eigs"
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"""
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# stats_and_counts is a list of pair (Tensor, int)
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stats_and_counts = [ get_tensor_stats(x, dim, stats_type) for x in tensors ]
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stats = [ x[0] for x in stats_and_counts ]
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counts = [ x[1] for x in stats_and_counts ]
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if stats_type == "eigs":
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try:
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stats = torch.stack(stats).sum(dim=0)
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except:
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return ''
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count = sum(counts)
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stats = stats / count
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stats, _ = torch.symeig(stats)
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stats = stats.abs().sqrt() # sqrt so it reflects data magnitude, like stddev- not variance
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elif sizes_same:
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stats = torch.stack(stats).sum(dim=0)
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count = sum(counts)
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stats = stats / count
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else:
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stats = [ x[0] / x[1] for x in stats_and_counts ]
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stats = torch.cat(stats, dim=0)
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if stats_type == 'rms':
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stats = stats.sqrt()
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# if `summarize` we print percentiles of the stats; else,
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# we print out individual elements.
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summarize = (not sizes_same) or options.dim_is_summarized(stats.numel())
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if summarize:
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# print out percentiles.
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stats = stats.sort()[0]
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num_percentiles = 10
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size = stats.numel()
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percentiles = []
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for i in range(num_percentiles + 1):
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index = (i * (size - 1)) // num_percentiles
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percentiles.append(stats[index].item())
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percentiles = [ '%.2g' % x for x in percentiles ]
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percentiles = ' '.join(percentiles)
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ans = f'percentiles: [{percentiles}]'
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else:
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ans = stats.tolist()
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ans = [ '%.2g' % x for x in ans ]
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ans = '[' + ' '.join(ans) + ']'
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if stats_type == "value":
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norm = (stats ** 2).sum().sqrt().item()
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mean_abs = stats.abs().mean().item()
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# This norm is useful because it is strictly less than the largest
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# sqrt(eigenvalue) of the variance, which we print out, and shows,
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# speaking in an approximate way, how much of that largest eigenvalue
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# can be attributed to the mean of the distribution.
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ans += f', norm={norm:.2g}, mean_abs={mean_abs:.2g}'
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else:
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mean = stats.mean().item()
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ans += f', mean={mean:.2g}'
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return ans
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def print_diagnostics_for_dim(name: str, dim: int, tensors: List[Tensor],
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options: TensorDiagnosticOptions):
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ndim = tensors[0].ndim
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if ndim > 1:
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stats_types = ["abs", "positive", "value", "rms"]
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if tensors[0].shape[dim] <= options.max_eig_dim:
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stats_types.append("eigs")
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else:
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stats_types = [ "value", "abs" ]
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for stats_type in stats_types:
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sizes = [ x.shape[dim] for x in tensors ]
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sizes_same = all([ x == sizes[0] for x in sizes ])
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s = get_diagnostics_for_dim(dim, tensors,
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options, sizes_same,
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stats_type)
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if s == '':
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continue
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min_size = min(sizes)
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max_size = max(sizes)
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size_str = f"{min_size}" if sizes_same else f"{min_size}..{max_size}"
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# stats_type will be "abs" or "positive".
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print(f"module={name}, dim={dim}, size={size_str}, {stats_type} {s}")
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class TensorDiagnostic(object):
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"""
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This class is not directly used by the user, it is responsible for collecting
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diagnostics for a single parameter tensor of a torch.Module.
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"""
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def __init__(self,
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opts: TensorDiagnosticOptions,
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name: str):
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self.name = name
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self.opts = opts
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self.saved_tensors = []
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def accumulate(self, x):
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if isinstance(x, Tuple):
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x = x[0]
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if not isinstance(x, Tensor):
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return
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if x.device == torch.device('cpu'):
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x = x.detach().clone()
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else:
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x = x.detach().to('cpu', non_blocking=True)
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self.saved_tensors.append(x)
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l = len(self.saved_tensors)
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if l & (l - 1) == 0: # power of 2..
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self._limit_memory()
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def _limit_memory(self):
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if len(self.saved_tensors) > 1024:
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self.saved_tensors = self.saved_tensors[-1024:]
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return
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tot_mem = 0.0
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for i in reversed(range(len(self.saved_tensors))):
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tot_mem += self.saved_tensors[i].numel() * self.saved_tensors[i].element_size()
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if tot_mem > self.opts.memory_limit:
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self.saved_tensors = self.saved_tensors[i:]
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return
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def print_diagnostics(self):
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if len(self.saved_tensors) == 0:
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print("{name}: no stats".format(name=self.name))
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return
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if self.saved_tensors[0].ndim == 0:
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# ensure there is at least one dim.
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self.saved_tensors = [ x.unsqueeze(0) for x in self.saved_tensors ]
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try:
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device = torch.device('cuda')
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torch.ones(1, 1, device)
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except:
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device = torch.device('cpu')
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ndim = self.saved_tensors[0].ndim
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tensors = [x.to(device) for x in self.saved_tensors]
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for dim in range(ndim):
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print_diagnostics_for_dim(self.name, dim,
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tensors,
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self.opts)
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class ModelDiagnostic(object):
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def __init__(self, opts: TensorDiagnosticOptions = TensorDiagnosticOptions()):
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self.diagnostics = dict()
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self.opts = opts
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def __getitem__(self, name: str):
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if name not in self.diagnostics:
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self.diagnostics[name] = TensorDiagnostic(self.opts, name)
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return self.diagnostics[name]
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def print_diagnostics(self):
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for k in sorted(self.diagnostics.keys()):
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self.diagnostics[k].print_diagnostics()
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def attach_diagnostics(model: nn.Module,
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opts: TensorDiagnosticOptions) -> ModelDiagnostic:
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ans = ModelDiagnostic(opts)
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for name, module in model.named_modules():
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if name == '':
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name = "<top-level>"
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forward_diagnostic = TensorDiagnostic(opts, name + ".output")
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backward_diagnostic = TensorDiagnostic(opts, name + ".grad")
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# setting model_diagnostic=ans and n=name below, instead of trying to capture the variables,
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# ensures that we use the current values. (matters for name, since
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# the variable gets overwritten). these closures don't really capture
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# by value, only by "the final value the variable got in the function" :-(
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def forward_hook(_module, _input, _output,
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_model_diagnostic=ans, _name=name):
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if isinstance(_output, Tensor):
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_model_diagnostic[f"{_name}.output"].accumulate(_output)
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elif isinstance(_output, tuple):
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for i, o in enumerate(_output):
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_model_diagnostic[f"{_name}.output[{i}]"].accumulate(o)
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def backward_hook(_module, _input, _output,
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_model_diagnostic=ans, _name=name):
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if isinstance(_output, Tensor):
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_model_diagnostic[f"{_name}.grad"].accumulate(_output)
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elif isinstance(_output, tuple):
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for i, o in enumerate(_output):
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_model_diagnostic[f"{_name}.grad[{i}]"].accumulate(o)
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module.register_forward_hook(forward_hook)
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module.register_backward_hook(backward_hook)
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for name, parameter in model.named_parameters():
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def param_backward_hook(grad,
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_parameter=parameter,
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_model_diagnostic=ans,
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_name=name):
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_model_diagnostic[f"{_name}.param_value"].accumulate(_parameter)
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_model_diagnostic[f"{_name}.param_grad"].accumulate(grad)
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parameter.register_hook(param_backward_hook)
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return ans
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def _test_tensor_diagnostic():
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opts = TensorDiagnosticOptions(2**20, 512)
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diagnostic = TensorDiagnostic(opts, "foo")
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for _ in range(10):
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diagnostic.accumulate(torch.randn(50, 100) * 10.0)
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diagnostic.print_diagnostics()
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model = nn.Sequential(nn.Linear(100, 50), nn.Linear(50, 80))
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diagnostic = attach_diagnostics(model, opts)
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for _ in range(10):
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T = random.randint(200, 300)
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x = torch.randn(T, 100)
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y = model(x)
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y.sum().backward()
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diagnostic.print_diagnostics()
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if __name__ == '__main__':
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_test_tensor_diagnostic()
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def _test_func():
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ans = []
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for i in range(10):
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x = list()
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x.append(i)
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def func():
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return x
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ans.append(func)
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return ans
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