267 lines
11 KiB
Python
267 lines
11 KiB
Python
# Copyright (c) 2021, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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#
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# NVIDIA CORPORATION and its licensors retain all intellectual property
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# and proprietary rights in and to this software, related documentation
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# and any modifications thereto. Any use, reproduction, disclosure or
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# distribution of this software and related documentation without an express
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# license agreement from NVIDIA CORPORATION is strictly prohibited.
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import re
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import contextlib
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import numpy as np
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import torch
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import warnings
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import dnnlib
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#----------------------------------------------------------------------------
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# Cached construction of constant tensors. Avoids CPU=>GPU copy when the
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# same constant is used multiple times.
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_constant_cache = dict()
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def constant(value, shape=None, dtype=None, device=None, memory_format=None):
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value = np.asarray(value)
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if shape is not None:
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shape = tuple(shape)
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if dtype is None:
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dtype = torch.get_default_dtype()
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if device is None:
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device = torch.device('cpu')
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if memory_format is None:
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memory_format = torch.contiguous_format
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key = (value.shape, value.dtype, value.tobytes(), shape, dtype, device, memory_format)
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tensor = _constant_cache.get(key, None)
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if tensor is None:
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tensor = torch.as_tensor(value.copy(), dtype=dtype, device=device)
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if shape is not None:
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tensor, _ = torch.broadcast_tensors(tensor, torch.empty(shape))
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tensor = tensor.contiguous(memory_format=memory_format)
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_constant_cache[key] = tensor
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return tensor
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#----------------------------------------------------------------------------
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# Replace NaN/Inf with specified numerical values.
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try:
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nan_to_num = torch.nan_to_num # 1.8.0a0
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except AttributeError:
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def nan_to_num(input, nan=0.0, posinf=None, neginf=None, *, out=None): # pylint: disable=redefined-builtin
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assert isinstance(input, torch.Tensor)
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if posinf is None:
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posinf = torch.finfo(input.dtype).max
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if neginf is None:
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neginf = torch.finfo(input.dtype).min
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assert nan == 0
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return torch.clamp(input.unsqueeze(0).nansum(0), min=neginf, max=posinf, out=out)
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#----------------------------------------------------------------------------
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# Symbolic assert.
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try:
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symbolic_assert = torch._assert # 1.8.0a0 # pylint: disable=protected-access
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except AttributeError:
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symbolic_assert = torch.Assert # 1.7.0
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#----------------------------------------------------------------------------
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# Context manager to temporarily suppress known warnings in torch.jit.trace().
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# Note: Cannot use catch_warnings because of https://bugs.python.org/issue29672
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@contextlib.contextmanager
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def suppress_tracer_warnings():
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flt = ('ignore', None, torch.jit.TracerWarning, None, 0)
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warnings.filters.insert(0, flt)
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yield
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warnings.filters.remove(flt)
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#----------------------------------------------------------------------------
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# Assert that the shape of a tensor matches the given list of integers.
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# None indicates that the size of a dimension is allowed to vary.
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# Performs symbolic assertion when used in torch.jit.trace().
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def assert_shape(tensor, ref_shape):
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if tensor.ndim != len(ref_shape):
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raise AssertionError(f'Wrong number of dimensions: got {tensor.ndim}, expected {len(ref_shape)}')
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for idx, (size, ref_size) in enumerate(zip(tensor.shape, ref_shape)):
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if ref_size is None:
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pass
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elif isinstance(ref_size, torch.Tensor):
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with suppress_tracer_warnings(): # as_tensor results are registered as constants
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symbolic_assert(torch.equal(torch.as_tensor(size), ref_size), f'Wrong size for dimension {idx}')
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elif isinstance(size, torch.Tensor):
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with suppress_tracer_warnings(): # as_tensor results are registered as constants
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symbolic_assert(torch.equal(size, torch.as_tensor(ref_size)), f'Wrong size for dimension {idx}: expected {ref_size}')
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elif size != ref_size:
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raise AssertionError(f'Wrong size for dimension {idx}: got {size}, expected {ref_size}')
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#----------------------------------------------------------------------------
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# Function decorator that calls torch.autograd.profiler.record_function().
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def profiled_function(fn):
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def decorator(*args, **kwargs):
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with torch.autograd.profiler.record_function(fn.__name__):
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return fn(*args, **kwargs)
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decorator.__name__ = fn.__name__
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return decorator
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#----------------------------------------------------------------------------
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# Sampler for torch.utils.data.DataLoader that loops over the dataset
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# indefinitely, shuffling items as it goes.
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class InfiniteSampler(torch.utils.data.Sampler):
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def __init__(self, dataset, rank=0, num_replicas=1, shuffle=True, seed=0, window_size=0.5):
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assert len(dataset) > 0
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assert num_replicas > 0
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assert 0 <= rank < num_replicas
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assert 0 <= window_size <= 1
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super().__init__(dataset)
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self.dataset = dataset
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self.rank = rank
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self.num_replicas = num_replicas
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self.shuffle = shuffle
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self.seed = seed
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self.window_size = window_size
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def __iter__(self):
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order = np.arange(len(self.dataset))
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rnd = None
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window = 0
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if self.shuffle:
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rnd = np.random.RandomState(self.seed)
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rnd.shuffle(order)
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window = int(np.rint(order.size * self.window_size))
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idx = 0
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while True:
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i = idx % order.size
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if idx % self.num_replicas == self.rank:
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yield order[i]
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if window >= 2:
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j = (i - rnd.randint(window)) % order.size
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order[i], order[j] = order[j], order[i]
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idx += 1
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#----------------------------------------------------------------------------
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# Utilities for operating with torch.nn.Module parameters and buffers.
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def params_and_buffers(module):
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assert isinstance(module, torch.nn.Module)
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return list(module.parameters()) + list(module.buffers())
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def named_params_and_buffers(module):
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assert isinstance(module, torch.nn.Module)
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return list(module.named_parameters()) + list(module.named_buffers())
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def copy_params_and_buffers(src_module, dst_module, require_all=False):
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assert isinstance(src_module, torch.nn.Module)
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assert isinstance(dst_module, torch.nn.Module)
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src_tensors = dict(named_params_and_buffers(src_module))
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for name, tensor in named_params_and_buffers(dst_module):
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assert (name in src_tensors) or (not require_all)
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if name in src_tensors:
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tensor.copy_(src_tensors[name].detach()).requires_grad_(tensor.requires_grad)
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#----------------------------------------------------------------------------
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# Context manager for easily enabling/disabling DistributedDataParallel
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# synchronization.
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@contextlib.contextmanager
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def ddp_sync(module, sync):
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assert isinstance(module, torch.nn.Module)
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if sync or not isinstance(module, torch.nn.parallel.DistributedDataParallel):
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yield
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else:
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with module.no_sync():
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yield
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#----------------------------------------------------------------------------
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# Check DistributedDataParallel consistency across processes.
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def check_ddp_consistency(module, ignore_regex=None):
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assert isinstance(module, torch.nn.Module)
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for name, tensor in named_params_and_buffers(module):
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fullname = type(module).__name__ + '.' + name
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if ignore_regex is not None and re.fullmatch(ignore_regex, fullname):
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continue
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tensor = tensor.detach()
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if tensor.is_floating_point():
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tensor = nan_to_num(tensor)
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other = tensor.clone()
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torch.distributed.broadcast(tensor=other, src=0)
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assert (tensor == other).all(), fullname
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#----------------------------------------------------------------------------
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# Print summary table of module hierarchy.
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def print_module_summary(module, inputs, max_nesting=3, skip_redundant=True):
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assert isinstance(module, torch.nn.Module)
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assert not isinstance(module, torch.jit.ScriptModule)
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assert isinstance(inputs, (tuple, list))
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# Register hooks.
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entries = []
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nesting = [0]
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def pre_hook(_mod, _inputs):
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nesting[0] += 1
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def post_hook(mod, _inputs, outputs):
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nesting[0] -= 1
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if nesting[0] <= max_nesting:
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outputs = list(outputs) if isinstance(outputs, (tuple, list)) else [outputs]
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outputs = [t for t in outputs if isinstance(t, torch.Tensor)]
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entries.append(dnnlib.EasyDict(mod=mod, outputs=outputs))
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hooks = [mod.register_forward_pre_hook(pre_hook) for mod in module.modules()]
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hooks += [mod.register_forward_hook(post_hook) for mod in module.modules()]
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# Run module.
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outputs = module(*inputs)
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for hook in hooks:
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hook.remove()
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# Identify unique outputs, parameters, and buffers.
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tensors_seen = set()
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for e in entries:
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e.unique_params = [t for t in e.mod.parameters() if id(t) not in tensors_seen]
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e.unique_buffers = [t for t in e.mod.buffers() if id(t) not in tensors_seen]
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e.unique_outputs = [t for t in e.outputs if id(t) not in tensors_seen]
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tensors_seen |= {id(t) for t in e.unique_params + e.unique_buffers + e.unique_outputs}
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# Filter out redundant entries.
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if skip_redundant:
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entries = [e for e in entries if len(e.unique_params) or len(e.unique_buffers) or len(e.unique_outputs)]
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# Construct table.
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rows = [[type(module).__name__, 'Parameters', 'Buffers', 'Output shape', 'Datatype']]
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rows += [['---'] * len(rows[0])]
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param_total = 0
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buffer_total = 0
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submodule_names = {mod: name for name, mod in module.named_modules()}
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for e in entries:
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name = '<top-level>' if e.mod is module else submodule_names[e.mod]
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param_size = sum(t.numel() for t in e.unique_params)
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buffer_size = sum(t.numel() for t in e.unique_buffers)
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output_shapes = [str(list(t.shape)) for t in e.outputs]
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output_dtypes = [str(t.dtype).split('.')[-1] for t in e.outputs]
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rows += [[
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name + (':0' if len(e.outputs) >= 2 else ''),
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str(param_size) if param_size else '-',
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str(buffer_size) if buffer_size else '-',
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(output_shapes + ['-'])[0],
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(output_dtypes + ['-'])[0],
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]]
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for idx in range(1, len(e.outputs)):
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rows += [[name + f':{idx}', '-', '-', output_shapes[idx], output_dtypes[idx]]]
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param_total += param_size
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buffer_total += buffer_size
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rows += [['---'] * len(rows[0])]
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rows += [['Total', str(param_total), str(buffer_total), '-', '-']]
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# Print table.
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widths = [max(len(cell) for cell in column) for column in zip(*rows)]
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print()
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for row in rows:
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print(' '.join(cell + ' ' * (width - len(cell)) for cell, width in zip(row, widths)))
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print()
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return outputs
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#----------------------------------------------------------------------------
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