404 lines
13 KiB
Python
404 lines
13 KiB
Python
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import os
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import math
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from typing import Any, Mapping, Text, Tuple, Union, NamedTuple
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from functools import partial
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import re
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import dataclasses
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import random
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from ml_collections import ConfigDict
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from ml_collections.config_dict.config_dict import placeholder
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import flax
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import jax
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import jax.numpy as jnp
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from jax.sharding import PartitionSpec as PS
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from jax.sharding import Mesh
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from jax.experimental import mesh_utils
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from jax.experimental.pjit import with_sharding_constraint as _with_sharding_constraint
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from jax.experimental.pjit import pjit
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from jax.interpreters import pxla
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import numpy as np
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from transformers import FlaxLogitsWarper
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class JaxRNG(object):
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""" A convenient stateful Jax RNG wrapper. Can be used to wrap RNG inside
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pure function.
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"""
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@classmethod
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def from_seed(cls, seed):
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return cls(jax.random.PRNGKey(seed))
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def __init__(self, rng):
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self.rng = rng
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def __call__(self, keys=None):
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if keys is None:
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self.rng, split_rng = jax.random.split(self.rng)
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return split_rng
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elif isinstance(keys, int):
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split_rngs = jax.random.split(self.rng, num=keys + 1)
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self.rng = split_rngs[0]
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return tuple(split_rngs[1:])
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else:
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split_rngs = jax.random.split(self.rng, num=len(keys) + 1)
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self.rng = split_rngs[0]
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return {key: val for key, val in zip(keys, split_rngs[1:])}
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class JaxDistributedConfig(object):
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""" Utility class for initializing JAX distributed. """
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@staticmethod
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def get_default_config(updates=None):
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config = ConfigDict()
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config.initialize_jax_distributed = False
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config.coordinator_address = placeholder(str)
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config.num_processes = placeholder(int)
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config.process_id = placeholder(int)
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config.local_device_ids = placeholder(str)
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if updates is not None:
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config.update(ConfigDict(updates).copy_and_resolve_references())
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return config
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@classmethod
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def initialize(cls, config):
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config = cls.get_default_config(config)
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if config.initialize_jax_distributed:
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if config.local_device_ids is not None:
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local_device_ids = [int(x) for x in config.local_device_ids.split(',')]
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else:
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local_device_ids = None
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jax.distributed.initialize(
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coordinator_address=config.coordinator_address,
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num_processes=config.num_processes,
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process_id=config.process_id,
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local_device_ids=local_device_ids,
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)
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class FlaxTemperatureLogitsWarper(FlaxLogitsWarper):
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""" JIT traceable version of FlaxLogitsWarper that performs temperature scaling."""
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def __init__(self, temperature):
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self.temperature = temperature
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def __call__(self, input_ids, scores, cur_len):
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return scores / jnp.clip(self.temperature, a_min=1e-8)
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def make_shard_and_gather_fns(partition_specs, dtype_specs=None):
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""" Create pytree of sharding and gathering functions from pytree of
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partition specs.
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"""
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float_dtypes = (jnp.bfloat16, jnp.float16, jnp.float32, jnp.float64)
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def make_to_dtype_fn(dtype_spec):
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def to_dtype(tensor):
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if dtype_specs in float_dtypes and getattr(tensor, 'dtype', None) in float_dtypes:
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# Convert all float tensors to the same dtype
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return tensor.astype(dtype_specs)
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elif hasattr(dtype_spec, 'dtype') and hasattr(tensor, 'dtype'):
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return tensor.astype(dtype_spec.dtype)
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return tensor
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return to_dtype
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def make_shard_fn(partition_spec, dtype_spec=None):
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jax_shard_function = pjit(
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make_to_dtype_fn(dtype_spec),
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in_shardings=None,
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out_shardings=partition_spec
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)
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def shard_fn(tensor):
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return jax_shard_function(tensor).block_until_ready()
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return shard_fn
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def make_gather_fn(partition_spec, dtype_spec=None):
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jax_gather_fn = pjit(
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make_to_dtype_fn(dtype_spec),
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in_shardings=partition_spec,
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out_shardings=None
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)
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def gather_fn(tensor):
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return jax.device_get(jax_gather_fn(tensor))
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return gather_fn
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if dtype_specs is None or dtype_specs in float_dtypes:
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shard_fns = jax.tree_util.tree_map(make_shard_fn, partition_specs)
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gather_fns = jax.tree_util.tree_map(make_gather_fn, partition_specs)
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else:
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shard_fns = jax.tree_util.tree_map(
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make_shard_fn, partition_specs, dtype_specs
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)
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gather_fns = jax.tree_util.tree_map(
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make_gather_fn, partition_specs, dtype_specs
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)
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return shard_fns, gather_fns
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def set_random_seed(seed):
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np.random.seed(seed)
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random.seed(seed)
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init_rng(seed)
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def get_jax_mesh(axis_dims, names):
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if axis_dims.startswith('!'):
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# Allow splitting a physical mesh axis if needed
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mesh_axis_splitting = True
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axis_dims = axis_dims[1:]
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else:
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mesh_axis_splitting = False
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if ':' in axis_dims:
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dims = []
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dim_names = []
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for axis in axis_dims.split(','):
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name, dim = axis.split(':')
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assert name in names
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dims.append(int(dim))
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dim_names.append(name)
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assert(set(dim_names) == set(names))
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else:
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dims = [int(x) for x in axis_dims.split(',')]
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dim_names = names
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assert len(dims) == len(names)
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mesh_shape = np.arange(jax.device_count()).reshape(dims).shape
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if mesh_axis_splitting:
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physical_mesh = np.array(jax.devices()).reshape(mesh_shape)
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else:
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physical_mesh = mesh_utils.create_device_mesh(mesh_shape)
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return Mesh(physical_mesh, dim_names)
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def names_in_current_mesh(*names):
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""" Check if current mesh axes contain these names. """
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mesh_axis_names = pxla.thread_resources.env.physical_mesh.axis_names
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return set(names) <= set(mesh_axis_names)
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def get_names_from_parition_spec(partition_specs):
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""" Return axis names from partition specs. """
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names = set()
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if isinstance(partition_specs, dict):
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partition_specs = partition_specs.values()
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for item in partition_specs:
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if item is None:
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continue
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elif isinstance(item, str):
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names.add(item)
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else:
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names.update(get_names_from_parition_spec(item))
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return list(names)
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def with_sharding_constraint(x, partition_specs):
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""" A smarter version of with_sharding_constraint that only applies the
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constraint if the current mesh contains the axes in the partition specs.
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"""
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axis_names = get_names_from_parition_spec(partition_specs)
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if names_in_current_mesh(*axis_names):
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x = _with_sharding_constraint(x, partition_specs)
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return x
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def wrap_function_with_rng(rng):
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""" To be used as decorator, automatically bookkeep a RNG for the wrapped function. """
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def wrap_function(function):
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def wrapped(*args, **kwargs):
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nonlocal rng
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rng, split_rng = jax.random.split(rng)
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return function(split_rng, *args, **kwargs)
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return wrapped
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return wrap_function
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def init_rng(seed):
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global jax_utils_rng
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jax_utils_rng = JaxRNG.from_seed(seed)
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def next_rng(*args, **kwargs):
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global jax_utils_rng
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return jax_utils_rng(*args, **kwargs)
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def get_metrics(metrics, unreplicate=False, stack=False):
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if unreplicate:
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metrics = flax.jax_utils.unreplicate(metrics)
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metrics = jax.device_get(metrics)
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if stack:
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return jax.tree_map(lambda *args: np.stack(args), *metrics)
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else:
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return {key: float(val) for key, val in metrics.items()}
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def mse_loss(val, target, valid=None):
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if valid is None:
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valid = jnp.ones((*target.shape[:2], 1))
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valid = valid.astype(jnp.float32)
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loss = jnp.mean(
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jnp.where(
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valid > 0.0,
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jnp.square(val - target),
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0.0
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)
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)
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return loss
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def cross_entropy_loss_and_accuracy(logits, tokens, valid=None):
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if valid is None:
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valid = jnp.ones(tokens.shape[:2])
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valid = valid.astype(jnp.float32)
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valid_text_length = jnp.maximum(jnp.sum(valid, axis=-1), 1e-10)
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logits = logits.astype(jnp.float32) # for numerical stability
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token_log_prob = jnp.squeeze(
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jnp.take_along_axis(
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jax.nn.log_softmax(logits, axis=-1),
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jnp.expand_dims(tokens, -1),
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axis=-1,
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),
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-1,
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)
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token_log_prob = jnp.where(valid > 0.0, token_log_prob, jnp.array(0.0))
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loss = -jnp.mean(jnp.sum(token_log_prob, axis=-1) / valid_text_length)
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correct = jnp.where(
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valid > 0.0,
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jnp.argmax(logits, axis=-1) == tokens,
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jnp.array(False)
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)
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accuracy = jnp.mean(jnp.sum(correct, axis=-1) / valid_text_length)
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return loss, accuracy
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def global_norm(tree):
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""" Return the global L2 norm of a pytree. """
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squared = jax.tree_util.tree_map(lambda x: jnp.sum(jnp.square(x)), tree)
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flattened, _ = jax.flatten_util.ravel_pytree(squared)
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return jnp.sqrt(jnp.sum(flattened))
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def average_metrics(metrics):
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with jax.spmd_mode("allow_all"):
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return jax.tree_map(
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lambda *args: jnp.mean(jnp.stack(args)),
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*metrics
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)
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def get_float_dtype_by_name(dtype):
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return {
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'bf16': jnp.bfloat16,
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'bfloat16': jnp.bfloat16,
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'fp16': jnp.float16,
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'float16': jnp.float16,
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'fp32': jnp.float32,
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'float32': jnp.float32,
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'fp64': jnp.float64,
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'float64': jnp.float64,
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}[dtype]
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def float_tensor_to_dtype(tensor, dtype):
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if dtype is None or dtype == '':
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return tensor
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if isinstance(dtype, str):
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dtype = get_float_dtype_by_name(dtype)
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float_dtypes = (jnp.bfloat16, jnp.float16, jnp.float32, jnp.float64)
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if getattr(tensor, 'dtype', None) in float_dtypes:
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tensor = tensor.astype(dtype)
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return tensor
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def float_to_dtype(tree, dtype):
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return jax.tree_util.tree_map(
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partial(float_tensor_to_dtype, dtype=dtype), tree
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)
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def get_gradient_checkpoint_policy(name):
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return {
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'everything_saveable': jax.checkpoint_policies.everything_saveable,
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'nothing_saveable': jax.checkpoint_policies.nothing_saveable,
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'checkpoint_dots': jax.checkpoint_policies.checkpoint_dots,
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'checkpoint_dots_with_no_batch_dims': jax.checkpoint_policies.checkpoint_dots_with_no_batch_dims,
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}[name]
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def tree_path_to_string(path, sep=None):
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keys = []
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for key in path:
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if isinstance(key, jax.tree_util.SequenceKey):
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keys.append(str(key.idx))
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elif isinstance(key, jax.tree_util.DictKey):
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keys.append(str(key.key))
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elif isinstance(key, jax.tree_util.GetAttrKey):
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keys.append(str(key.name))
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elif isinstance(key, jax.tree_util.FlattenedIndexKey):
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keys.append(str(key.key))
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else:
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keys.append(str(key))
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if sep is None:
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return tuple(keys)
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return sep.join(keys)
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def flatten_tree(xs, is_leaf=None, sep=None):
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flattened, _ = jax.tree_util.tree_flatten_with_path(xs, is_leaf=is_leaf)
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output = {}
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for key, val in flattened:
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output[tree_path_to_string(key, sep=sep)] = val
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return output
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def named_tree_map(f, tree, *rest, is_leaf=None, sep=None):
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""" An extended version of jax.tree_util.tree_map, where the mapped function
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f takes both the name (path) and the tree leaf as input.
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"""
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||
|
|
return jax.tree_util.tree_map_with_path(
|
||
|
|
lambda path, x, *r: f(tree_path_to_string(path, sep=sep), x, *r),
|
||
|
|
tree, *rest,
|
||
|
|
is_leaf=is_leaf
|
||
|
|
)
|
||
|
|
|
||
|
|
|
||
|
|
def match_partition_rules(rules, params):
|
||
|
|
""" Returns a pytree of PartitionSpec according to rules. Supports handling
|
||
|
|
Flax TrainState and Optax optimizer state.
|
||
|
|
"""
|
||
|
|
def get_partition_spec(name, leaf):
|
||
|
|
if len(leaf.shape) == 0 or np.prod(leaf.shape) == 1:
|
||
|
|
""" Don't partition scalar values. """
|
||
|
|
return PS()
|
||
|
|
for rule, ps in rules:
|
||
|
|
if re.search(rule, name) is not None:
|
||
|
|
return ps
|
||
|
|
raise ValueError(f'Partition rule not found for param: {name}')
|
||
|
|
return named_tree_map(get_partition_spec, params, sep='/')
|
||
|
|
|
||
|
|
|
||
|
|
def get_weight_decay_mask(exclusions):
|
||
|
|
""" Return a weight decay mask function that computes the pytree masks
|
||
|
|
according to the given exclusion rules.
|
||
|
|
"""
|
||
|
|
def decay(name, _):
|
||
|
|
for rule in exclusions:
|
||
|
|
if re.search(rule, name) is not None:
|
||
|
|
return False
|
||
|
|
return True
|
||
|
|
|
||
|
|
def weight_decay_mask(params):
|
||
|
|
return named_tree_map(decay, params, sep='/')
|
||
|
|
|
||
|
|
return weight_decay_mask
|
||
|
|
|
||
|
|
|
||
|
|
def tree_apply(fns, tree):
|
||
|
|
""" Apply a pytree of functions to the pytree. """
|
||
|
|
return jax.tree_util.tree_map(lambda fn, x: fn(x), fns, tree)
|
||
|
|
|