108 lines
3.7 KiB
Python
108 lines
3.7 KiB
Python
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#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The Google Research Authors and The HuggingFace Team All rights reserved.
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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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"""Utilities for constructing PyTrees of PartitionSpecs."""
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# utils adapted from https://github.com/google-research/google-research/blob/master/flax_models/t5x/partitions.py
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import re
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from flax.core.frozen_dict import freeze
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from flax.traverse_util import flatten_dict, unflatten_dict
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from jax.sharding import PartitionSpec as P
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# Sentinels
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_unmatched = object()
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# For specifying empty leaf dict `{}`
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empty_dict = object()
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def _match(qs, ks):
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"""Return True if regexes in qs match any window of strings in tuple ks."""
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# compile regexes and force complete match
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qts = tuple((re.compile(x + "$") for x in qs))
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for i in range(len(ks) - len(qs) + 1):
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matches = [x.match(y) for x, y in zip(qts, ks[i:])]
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if matches and all(matches):
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return True
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return False
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def _replacement_rules(rules):
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def replace(key, val):
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for rule, replacement in rules:
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if _match(rule, key):
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return replacement
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return val
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return replace
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# PartitionSpec for GPTNeo
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# replicate the hidden dim and shard feed-forward and head dim
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# def _get_partition_rules():
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# return [
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# # embeddings
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# (("transformer", "wpe", "embedding"), P("mp", None)),
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# (("transformer", "wte", "embedding"), P("mp", None)),
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# # atention
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# (("attention", "(q_proj|k_proj|v_proj)", "kernel"), P(None, "mp")),
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# (("attention", "out_proj", "kernel"), P("mp", None)),
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# (("attention", "out_proj", "bias"), None),
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# # mlp
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# (("mlp", "c_fc", "kernel"), P(None, "mp")),
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# (("mlp", "c_fc", "bias"), P("mp")),
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# (("mlp", "c_proj", "kernel"), P("mp", None)),
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# (("mlp", "c_proj", "bias"), None),
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# # layer norms
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# ((r"ln_\d+", "bias"), None),
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# ((r"\d+", r"ln_\d+", "scale"), None),
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# (("ln_f", "bias"), None),
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# (("ln_f", "scale"), None),
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# ]
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def _get_partition_rules():
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return [
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# embedding
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(("shared", "embedding"), P("model", None)),
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# SelfAttention
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(("SelfAttention", "(q|k|v)", "kernel"), P(None, "model")),
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(("SelfAttention", "o", "kernel"), P("model", None)),
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(("SelfAttention", "relative_attention_bias", "embedding"), P(None)),
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# EncDecAttention
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(("EncDecAttention", "(q|k|v)", "kernel"), P(None, "model")),
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(("EncDecAttention", "o", "kernel"), P("model", None)),
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# DenseReluDense
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(("DenseReluDense", "wi", "kernel"), P(None, "model")),
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(("DenseReluDense", "wo", "kernel"), P("model", None)),
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# layer norms
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(("final_layer_norm", "weight"), P(None)),
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(("layer_norm", "weight"), P(None)),
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]
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def set_partitions(in_dict):
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rules = _get_partition_rules()
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replace = _replacement_rules(rules)
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initd = {k: _unmatched for k in flatten_dict(in_dict)}
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result = {k: replace(k, v) for k, v in initd.items()}
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assert _unmatched not in result.values(), "Incomplete partition spec."
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# for item in result.values():
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# if item:
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# print(item)
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return freeze(unflatten_dict(result))
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