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https://github.com/k2-fsa/icefall.git
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ad62981765
* Adding diagnostics code... * Move diagnostics code from local dir to the shared icefall dir * Remove the diagnostics code in the local dir * Update docs of arguments, and remove stats_types() function in TensorDiagnosticOptions object. * Update docs of arguments. * Add copyright information. * Corrected the time in copyright information. Co-authored-by: Daniel Povey <dpovey@gmail.com>
347 lines
11 KiB
Python
347 lines
11 KiB
Python
# Copyright 2022 Xiaomi Corp. (authors: Daniel Povey
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# Zengwei Yao)
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#
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# See ../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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from typing import List, Tuple
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import torch
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from torch import Tensor, nn
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class TensorDiagnosticOptions(object):
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"""Options object for tensor diagnostics:
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Args:
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memory_limit:
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The maximum number of bytes per tensor (limits how many copies
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of the tensor we cache).
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"""
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def __init__(self, memory_limit: int):
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self.memory_limit = memory_limit
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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_sum_abs_stats(
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x: Tensor, dim: int, stats_type: str
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) -> Tuple[Tensor, int]:
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"""Returns the sum-of-absolute-value of this Tensor, for each index into
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the specified axis/dim of the tensor.
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Args:
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x:
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Tensor, tensor to be analyzed
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dim:
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Dimension with 0 <= dim < x.ndim
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stats_type:
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Either "mean-abs" in which case the stats represent the mean absolute
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value, or "pos-ratio" in which case the stats represent the proportion
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of positive values (actually: the tensor is count of positive values,
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count is the count of all values).
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Returns:
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(sum_abs, count) where sum_abs is a Tensor of shape (x.shape[dim],),
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and the count is an integer saying how many items were counted in
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each element of sum_abs.
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"""
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if stats_type == "mean-abs":
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x = x.abs()
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else:
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assert stats_type == "pos-ratio"
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x = (x > 0).to(dtype=torch.float)
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orig_numel = x.numel()
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sum_dims = [d for d in range(x.ndim) if d != dim]
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x = torch.sum(x, dim=sum_dims)
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count = orig_numel // x.numel()
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x = x.flatten()
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return x, count
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def get_diagnostics_for_dim(
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dim: int,
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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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) -> str:
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"""This function gets diagnostics for a dimension of a module.
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Args:
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dim:
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The dimension to analyze, with 0 <= dim < tensors[0].ndim
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tensors:
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List of cached tensors to get the stats
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options:
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Options object
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sizes_same:
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True if all the tensor sizes are the same on this dimension
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stats_type: either "mean-abs" or "pos-ratio", dictates the type of
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stats we accumulate, mean-abs is mean absolute value, "pos-ratio" is
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proportion of positive to nonnegative values.
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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.
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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_sum_abs_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 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 `summarize` we print percentiles of the stats;
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# else, 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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return f"percentiles: [{percentiles}]"
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else:
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stats = stats.tolist()
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stats = ["%.2g" % x for x in stats]
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stats = "[" + " ".join(stats) + "]"
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return stats
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def print_diagnostics_for_dim(
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name: str, dim: int, tensors: List[Tensor], options: TensorDiagnosticOptions
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):
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"""This function prints diagnostics for a dimension of a tensor.
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Args:
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name:
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The tensor name.
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dim:
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The dimension to analyze, with 0 <= dim < tensors[0].ndim.
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tensors:
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List of cached tensors to get the stats.
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options:
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Options object.
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"""
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for stats_type in ["mean-abs", "pos-ratio"]:
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# stats_type will be "mean-abs" or "pos-ratio".
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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(
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dim, tensors, options, sizes_same, stats_type
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)
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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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print(f"module={name}, dim={dim}, size={size_str}, {stats_type} {s}")
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class TensorDiagnostic(object):
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"""This class is not directly used by the user, it is responsible for
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collecting diagnostics for a single parameter tensor of a torch.nn.Module.
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Args:
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opts:
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Options object.
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name:
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The tensor name.
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"""
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def __init__(self, opts: TensorDiagnosticOptions, name: str):
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self.name = name
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self.opts = opts
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# A list to cache the tensors.
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self.saved_tensors = []
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def accumulate(self, x):
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"""Accumulate tensors."""
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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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num = len(self.saved_tensors)
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if num & (num - 1) == 0: # power of 2..
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self._limit_memory()
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def _limit_memory(self):
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"""Only keep the newly cached tensors to limit memory."""
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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 += (
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self.saved_tensors[i].numel()
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* self.saved_tensors[i].element_size()
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)
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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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"""Print diagnostics for each dimension of the tensor."""
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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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ndim = self.saved_tensors[0].ndim
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for dim in range(ndim):
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print_diagnostics_for_dim(
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self.name, dim, self.saved_tensors, self.opts
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)
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class ModelDiagnostic(object):
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"""This class stores diagnostics for all tensors in the torch.nn.Module.
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Args:
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opts:
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Options object.
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"""
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def __init__(self, opts: TensorDiagnosticOptions):
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# In this dictionary, the keys are tensors names and the values
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# are corresponding TensorDiagnostic objects.
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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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"""Print diagnostics for each tensor."""
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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(
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model: nn.Module, opts: TensorDiagnosticOptions
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) -> ModelDiagnostic:
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"""Attach a ModelDiagnostic object to the model by
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1) registering forward hook and backward hook on each module, to accumulate
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its output tensors and gradient tensors, respectively;
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2) registering backward hook on each module parameter, to accumulate its
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values and gradients.
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Args:
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model:
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the model to be analyzed.
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opts:
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Options object.
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Returns:
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The ModelDiagnostic object attached to the model.
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"""
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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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# Setting model_diagnostic=ans and n=name below, instead of trying to
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# capture the variables, ensures that we use the current values.
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# (matters for name, since the variable gets overwritten).
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# These closures don't really capture by value, only by
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# "the final value the variable got in the function" :-(
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def forward_hook(
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_module, _input, _output, _model_diagnostic=ans, _name=name
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):
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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(
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_module, _input, _output, _model_diagnostic=ans, _name=name
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):
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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(
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grad, _parameter=parameter, _model_diagnostic=ans, _name=name
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):
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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)
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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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