mirror of
https://github.com/ROCm/jax.git
synced 2025-04-17 20:36:05 +00:00
2a4a0e8d6f
Shardy custom_partitioning. The parsing of the sharding rule string very closely follows how einops parses their rules in einops/parsing.py. When a SdyShardingRule object is constructed, we check the syntax of the Einsum like notation string and its consistency with the user provided factor_sizes, and report errors accordingly. This is done during f.def_partition. When SdyShardingRule.build is called, during JAX to MLIR lowering, we check the consistency between the Einsum like notation string, the factor_sizes and the MLIR operation, and report errors accordingly. PiperOrigin-RevId: 703187962
381 lines
14 KiB
Python
381 lines
14 KiB
Python
# Copyright 2024 The JAX Authors.
|
|
#
|
|
# Licensed under the Apache License, Version 2.0 (the "License");
|
|
# you may not use this file except in compliance with the License.
|
|
# You may obtain a copy of the License at
|
|
#
|
|
# https://www.apache.org/licenses/LICENSE-2.0
|
|
#
|
|
# Unless required by applicable law or agreed to in writing, software
|
|
# distributed under the License is distributed on an "AS IS" BASIS,
|
|
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
|
# See the License for the specific language governing permissions and
|
|
# limitations under the License.
|
|
|
|
"""Implements SdyShardingRule."""
|
|
|
|
from collections import OrderedDict
|
|
|
|
from jax._src.lib.mlir import ir
|
|
from jax._src.lib.mlir.dialects import sdy
|
|
|
|
|
|
_CompoundFactor = tuple[str, ...]
|
|
_DimMapping = tuple[str | _CompoundFactor, ...]
|
|
|
|
# A single character replacement for ... to simplify parsing.
|
|
_ELLIPSIS: str = "…"
|
|
|
|
# A prefix for names of batching dimension factors, used for expanding the
|
|
# leading ... into factors.
|
|
_BATCHING_DIM_FACTOR_PREFIX = "?"
|
|
|
|
def _get_batching_dim_factor_name(batch_dim_order : int):
|
|
"""Constructs a factor name for a batching dimension.
|
|
|
|
We expand the leading ... into factors representing the batching dimensions
|
|
to support building the MLIR representation for the sharding rule. For this
|
|
reason, we construct a factor name that won't be used by users for the
|
|
batching dimensions.
|
|
"""
|
|
return f"{_BATCHING_DIM_FACTOR_PREFIX}{batch_dim_order}"
|
|
|
|
def _parse_values(
|
|
rule: str,
|
|
) -> tuple[_DimMapping, ...]:
|
|
"""Parses the LHS or RHS of an Einsum notation like string.
|
|
|
|
Converts each operand or result in the Einsum notation like string to a tuple
|
|
of _DimMapping. This very closely follows how einops parses their rules in
|
|
einops/parsing.py.
|
|
|
|
Args:
|
|
rule: The Einsum notation for the operands or results of an operation.
|
|
|
|
Returns:
|
|
The tuple of values.
|
|
|
|
Raises:
|
|
ValueError: If the rule is not balanced or contains unknown characters.
|
|
"""
|
|
|
|
# Remove unnecessary spaces in the rule to simplify the parsing process.
|
|
words = rule.split()
|
|
rule = " ".join(words)
|
|
|
|
# Similar to einops rules, an empty LHS/RHS has a single scalar value.
|
|
if not rule:
|
|
return ((),)
|
|
|
|
all_values = []
|
|
# Represent all dimensions of an value. When an value[0]==_ELLIPSIS, the
|
|
# value may have 0 or more leading dimensions.
|
|
value = []
|
|
current_factor = None
|
|
# A value of None indicates the current dimension is not a compound dimension,
|
|
# while a value of [] indicates that we have just started parsing a compound
|
|
# dimension.
|
|
current_compound_dim: list[str] | None = None
|
|
|
|
def add_factor(x):
|
|
if current_compound_dim is None:
|
|
value.append(x)
|
|
else:
|
|
current_compound_dim.append(x)
|
|
|
|
for char in rule:
|
|
if char == _ELLIPSIS:
|
|
if (current_factor is not None or current_compound_dim is not None
|
|
or value):
|
|
raise ValueError(
|
|
"Ellipsis can only be used at the beginning of a dimension")
|
|
add_factor(_ELLIPSIS)
|
|
continue
|
|
if char in "(), ":
|
|
if current_factor is not None:
|
|
add_factor(current_factor)
|
|
current_factor = None
|
|
if char == "(":
|
|
if current_compound_dim is not None:
|
|
raise ValueError(
|
|
"Compound factors should be one level, nested brackets are not"
|
|
" allowed")
|
|
current_compound_dim = []
|
|
elif char == ")":
|
|
if current_compound_dim is None:
|
|
raise ValueError("Brackets are not balanced")
|
|
if len(current_compound_dim) <= 1:
|
|
raise ValueError("Brackets should contain at least two factors")
|
|
value.append(tuple(current_compound_dim))
|
|
current_compound_dim = None
|
|
elif char == ",":
|
|
all_values.append(tuple(value))
|
|
value = []
|
|
elif char == "_" or char.isdigit() or char.isalpha():
|
|
if current_factor is None:
|
|
if str.isdigit(char):
|
|
raise ValueError(f"Factor names have to start with a letter, but got '{char}'")
|
|
current_factor = char
|
|
else:
|
|
current_factor += char
|
|
else:
|
|
raise ValueError(f"Unknown character '{char}'")
|
|
|
|
if current_compound_dim is not None:
|
|
raise ValueError(f"Brackets are not balanced in rule: '{rule}'")
|
|
if current_factor is not None:
|
|
add_factor(current_factor)
|
|
all_values.append(tuple(value))
|
|
|
|
return tuple(all_values)
|
|
|
|
|
|
class SdyShardingRule:
|
|
"""A representation for Shardy sharding rule.
|
|
|
|
A SdyShardingRule includes an Enisum notation like string and an optional
|
|
list of factor sizes. A factor is a name in the Einsum notation. If a factor
|
|
is only used in compound factors, its size must be specified.
|
|
|
|
SdyShardingRule examples:
|
|
|
|
* Contracting dim matmul AB@BC->AC: SdyShardingRule('i j, j k -> i k')
|
|
* Batching matmul: SdyShardingRule('... i j, ... j k -> ... i k')
|
|
* A reshape (8,) -> (4, 2): SdyShardingRule('(i j) -> i j')
|
|
* Another reshape (4, 2) -> (2, 4): SdyShardingRule('(i j) -> (j i)`, i=4, j=2)
|
|
* An elementwise add of any dimensions x + y -> z: SdyShardingRule('..., ... -> ...')
|
|
"""
|
|
|
|
def __init__(self, rule: str, **factor_sizes):
|
|
"""Constructs a SdyShardingRule object from the Einsum notation like string.
|
|
|
|
This is done by verifying that the input Einsum notation like string and
|
|
with optional factor sizes represents a valid sharding rule and converting
|
|
it to an internal representation.
|
|
|
|
Args:
|
|
rule: The Einsum notation like string for an operation.
|
|
**factor_sizes: The optional factor sizes.
|
|
|
|
Raises:
|
|
ValueError: If there is any problem with the rule or factor_sizes.
|
|
"""
|
|
if not isinstance(rule, str):
|
|
raise TypeError(f"rule must be a str, but got {type(rule)}")
|
|
if not all(isinstance(size, int) for size in factor_sizes.values()):
|
|
raise TypeError(
|
|
f"factor_sizes must be a dict of str to int, but got {factor_sizes}")
|
|
|
|
# Replace ... with a single char to simplify parsing.
|
|
if _ELLIPSIS in rule:
|
|
raise ValueError(f"Unknown character '{_ELLIPSIS}'")
|
|
if "." in rule:
|
|
rule = rule.replace("...", _ELLIPSIS)
|
|
if "." in rule:
|
|
raise ValueError("Character '.' must be used inside ellipsis '...'")
|
|
|
|
try:
|
|
operands, results = rule.split("->")
|
|
except ValueError as e:
|
|
raise ValueError(f"There is no -> in rule: '{rule}'") from e
|
|
|
|
self.operands = _parse_values(operands)
|
|
self.results = _parse_values(results)
|
|
|
|
# Find all factors and mark whether their size can be inferred.
|
|
factors_inferrable = dict()
|
|
for value in self.operands + self.results:
|
|
for dim in value:
|
|
if dim == _ELLIPSIS:
|
|
continue
|
|
if isinstance(dim, str):
|
|
factors_inferrable[dim] = True
|
|
else:
|
|
for factor in dim:
|
|
if factor not in factors_inferrable.keys():
|
|
factors_inferrable[factor] = False
|
|
|
|
# Check that factors in factor_sizes are used in the rule.
|
|
for factor in factor_sizes:
|
|
if factor not in factors_inferrable:
|
|
raise ValueError(
|
|
f"Factor {factor} is not used in the rule, but size is provided")
|
|
|
|
# Check that factors that are used for a whole dimension aren't in
|
|
# factor_sizes and factors that are never used for a whole dimension are
|
|
# in factor_sizes.
|
|
for factor, inferrable in factors_inferrable.items():
|
|
if factor not in factor_sizes and not inferrable:
|
|
raise ValueError(
|
|
f"Factor {factor} is only used in compound factors; must specify"
|
|
" its size")
|
|
if factor in factor_sizes and inferrable:
|
|
raise ValueError(
|
|
f"Factor {factor} represents a whole dimension; do not specify its"
|
|
" size")
|
|
|
|
self.factor_sizes = factor_sizes
|
|
|
|
def __str__(self):
|
|
return f"SdyShardingRule({self.operands}, {self.results}, {self.factor_sizes})"
|
|
|
|
def build(
|
|
self,
|
|
operand_types: list[ir.Type],
|
|
result_types: list[ir.Type],) -> ir.Attribute:
|
|
"""Builds the MLIR representation for the sharding rule.
|
|
|
|
This is done by verifying that the rule is consistent with the types of
|
|
the operation and converting the Einsum notation like string to
|
|
OpShardingRuleAttr.
|
|
"""
|
|
if len(self.operands) != len(operand_types):
|
|
raise ValueError(
|
|
f"Sharding rule has {len(self.operands)} operands, but the operation"
|
|
f" has {len(operand_types)} operands"
|
|
)
|
|
if len(self.results) != len(result_types):
|
|
raise ValueError(
|
|
f"Sharding rule has {len(self.results)} results, but the operation"
|
|
f" has {len(result_types)} results"
|
|
)
|
|
|
|
factors_to_indices_sizes: OrderedDict[str, list[int]] = OrderedDict()
|
|
types = operand_types + result_types
|
|
UNKNOWN = -1 # Representation for unknown factor size or factor index.
|
|
|
|
def get_message_for_value(i):
|
|
if i >= len(operand_types):
|
|
return f"{i - len(operand_types)}th result"
|
|
else:
|
|
return f"{i}th operand"
|
|
|
|
def get_rank_for_value(i):
|
|
return ir.ShapedType(types[i]).rank
|
|
|
|
def get_size_for_value_dim(i, j):
|
|
return ir.ShapedType(types[i]).shape[j]
|
|
|
|
def add_factor(factor, size):
|
|
"""Adds a factor to factors_to_indices_sizes.
|
|
|
|
`size` may be a dimensions size, a user specified factor size, or UNKNOWN
|
|
if a factor is first used as in a compound factor and then used for a
|
|
whole dimension.
|
|
"""
|
|
factor_index, factor_size = factors_to_indices_sizes.get(factor, [UNKNOWN, UNKNOWN])
|
|
if factor_index != UNKNOWN:
|
|
# Not the first time seeing the factor.
|
|
if size != UNKNOWN and factor_size != UNKNOWN and factor_size != size:
|
|
factor_or_batching_dim = (
|
|
f"Factor {factor}" if _BATCHING_DIM_FACTOR_PREFIX not in factor
|
|
else f"Batching dimension {factor[1:]}")
|
|
raise ValueError(
|
|
f"{factor_or_batching_dim} corresponds to two sizes:"
|
|
f" {factor_size} and {size}")
|
|
if size != UNKNOWN and factor_size == UNKNOWN:
|
|
factors_to_indices_sizes[factor] = [factor_index, size]
|
|
else:
|
|
# First time seeing the factor.
|
|
factor_index = len(factors_to_indices_sizes)
|
|
factors_to_indices_sizes[factor] = [factor_index, size]
|
|
|
|
def add_batching_dim_factor(batch_dim_order, factor_size):
|
|
ellipsis_batch_dim_name = _get_batching_dim_factor_name(batch_dim_order)
|
|
add_factor(ellipsis_batch_dim_name, factor_size)
|
|
|
|
def build_dim_mapping_for_compound_factors(i, j, factors):
|
|
accumulated_size = 1
|
|
all_indices = []
|
|
for factor in factors:
|
|
factor_index, factor_size = factors_to_indices_sizes[factor]
|
|
accumulated_size *= factor_size
|
|
all_indices.append(factor_index)
|
|
|
|
dim_size = get_size_for_value_dim(i, j)
|
|
if accumulated_size != dim_size:
|
|
raise ValueError(
|
|
f"{get_message_for_value(i)} actual size {dim_size} doesn't match"
|
|
f" the size {accumulated_size} derived from the compound factors"
|
|
f" {factors}")
|
|
|
|
return sdy.DimMappingAttr.get(factor_indices=all_indices)
|
|
|
|
# Add factors and their sizes in the order they appear in the rule,
|
|
# including the batching dimensions represented by ellipsis.
|
|
ellipsis_rank = None
|
|
for i, value in enumerate(self.operands + self.results):
|
|
if value and value[0] == _ELLIPSIS:
|
|
has_ellipsis = True
|
|
value = value[1:]
|
|
else:
|
|
has_ellipsis = False
|
|
rule_rank = len(value)
|
|
op_rank = get_rank_for_value(i)
|
|
# The number of dimensions represented by ellipsis.
|
|
current_ellipsis_rank = 0
|
|
if has_ellipsis and op_rank > rule_rank:
|
|
current_ellipsis_rank = op_rank - rule_rank
|
|
if has_ellipsis:
|
|
if ellipsis_rank is None:
|
|
ellipsis_rank = current_ellipsis_rank
|
|
elif ellipsis_rank != current_ellipsis_rank:
|
|
raise ValueError(
|
|
"Ellipsis represents different number of leading dimensions"
|
|
f" {ellipsis_rank} and {current_ellipsis_rank}")
|
|
rule_rank += current_ellipsis_rank
|
|
if rule_rank != op_rank:
|
|
msg = get_message_for_value(i)
|
|
raise ValueError(
|
|
f"Sharding rule {msg} has rank {rule_rank}, but the operation"
|
|
f" {msg} has rank {op_rank}")
|
|
|
|
for j in range(current_ellipsis_rank):
|
|
add_batching_dim_factor(j, get_size_for_value_dim(i, j))
|
|
|
|
for j, dim in enumerate(value):
|
|
if isinstance(dim, str):
|
|
add_factor(
|
|
dim, get_size_for_value_dim(i, j + current_ellipsis_rank))
|
|
else:
|
|
for factor in dim:
|
|
add_factor(factor, self.factor_sizes.get(factor, UNKNOWN))
|
|
|
|
# Build the tensor mappings for each operand and result.
|
|
tensor_mappings = []
|
|
for i, value in enumerate(self.operands + self.results):
|
|
dim_mappings = []
|
|
|
|
if value and value[0] == _ELLIPSIS:
|
|
value = value[1:]
|
|
if ellipsis_rank is None:
|
|
current_ellipsis_rank = 0
|
|
else:
|
|
current_ellipsis_rank = ellipsis_rank
|
|
else:
|
|
current_ellipsis_rank = 0
|
|
|
|
for j in range(current_ellipsis_rank):
|
|
dim_mappings.append(
|
|
sdy.DimMappingAttr.get(factor_indices=[
|
|
factors_to_indices_sizes[_get_batching_dim_factor_name(j)][0]]))
|
|
|
|
for j, dim in enumerate(value):
|
|
if isinstance(dim, str):
|
|
dim_mappings.append(
|
|
sdy.DimMappingAttr.get(
|
|
factor_indices=[factors_to_indices_sizes[dim][0]]))
|
|
else:
|
|
dim_mappings.append(
|
|
build_dim_mapping_for_compound_factors(
|
|
i, j + current_ellipsis_rank, dim))
|
|
|
|
tensor_mappings.append(
|
|
sdy.TensorMappingAttr.get(dim_mappings=dim_mappings))
|
|
|
|
op_sharding_rule = sdy.OpShardingRuleAttr.get(
|
|
factor_sizes=[item[1] for item in factors_to_indices_sizes.values()],
|
|
operand_mappings=tensor_mappings[0:len(operand_types)],
|
|
result_mappings=tensor_mappings[len(operand_types):])
|
|
return op_sharding_rule
|