mirror of https://github.com/Jittor/Jittor
177 lines
8.3 KiB
Python
177 lines
8.3 KiB
Python
from typing import Optional, Dict
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from jittor.einops import EinopsError
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from jittor.einops.parsing import ParsedExpression
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import warnings
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import string
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from jittor.einops.einops import _product
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def _report_axes(axes: set, report_message: str):
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if len(axes) > 0:
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raise EinopsError(report_message.format(axes))
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class _EinmixMixin:
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def __init__(self, pattern, weight_shape, bias_shape=None, **axes_lengths):
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"""
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EinMix - Einstein summation with automated tensor management and axis packing/unpacking.
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EinMix is an advanced tool, helpful tutorial:
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https://github.com/arogozhnikov/einops/blob/master/docs/3-einmix-layer.ipynb
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Imagine taking einsum with two arguments, one of each input, and one - tensor with weights
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>>> einsum('time batch channel_in, channel_in channel_out -> time batch channel_out', input, weight)
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This layer manages weights for you, syntax highlights separate role of weight matrix
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>>> EinMix('time batch channel_in -> time batch channel_out', weight_shape='channel_in channel_out')
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But otherwise it is the same einsum under the hood.
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Simple linear layer with bias term (you have one like that in your framework)
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>>> EinMix('t b cin -> t b cout', weight_shape='cin cout', bias_shape='cout', cin=10, cout=20)
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There is restriction to mix the last axis. Let's mix along height
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>>> EinMix('h w c-> hout w c', weight_shape='h hout', bias_shape='hout', h=32, hout=32)
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Channel-wise multiplication (like one used in normalizations)
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>>> EinMix('t b c -> t b c', weight_shape='c', c=128)
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Separate dense layer within each head, no connection between different heads
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>>> EinMix('t b (head cin) -> t b (head cout)', weight_shape='head cin cout', ...)
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... ah yes, you need to specify all dimensions of weight shape/bias shape in parameters.
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Use cases:
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- when channel dimension is not last, use EinMix, not transposition
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- patch/segment embeddings
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- when need only within-group connections to reduce number of weights and computations
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- perfect as a part of sequential models
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- next-gen MLPs (follow tutorial to learn more)
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Uniform He initialization is applied to weight tensor and encounters for number of elements mixed.
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Parameters
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:param pattern: transformation pattern, left side - dimensions of input, right side - dimensions of output
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:param weight_shape: axes of weight. A tensor of this shape is created, stored, and optimized in a layer
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:param bias_shape: axes of bias added to output. Weights of this shape are created and stored. If `None` (the default), no bias is added.
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:param axes_lengths: dimensions of weight tensor
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"""
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super().__init__()
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self.pattern = pattern
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self.weight_shape = weight_shape
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self.bias_shape = bias_shape
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self.axes_lengths = axes_lengths
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self.initialize_einmix(pattern=pattern, weight_shape=weight_shape, bias_shape=bias_shape, axes_lengths=axes_lengths)
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def initialize_einmix(self, pattern, weight_shape, bias_shape, axes_lengths):
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left_pattern, right_pattern = pattern.split('->')
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left = ParsedExpression(left_pattern)
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right = ParsedExpression(right_pattern)
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weight = ParsedExpression(weight_shape)
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_report_axes(
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set.difference(right.identifiers, {*left.identifiers, *weight.identifiers}),
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'Unrecognized identifiers on the right side of EinMix {}'
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)
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if left.has_ellipsis or right.has_ellipsis or weight.has_ellipsis:
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raise EinopsError('Ellipsis is not supported in EinMix (right now)')
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if any(x.has_non_unitary_anonymous_axes for x in [left, right, weight]):
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raise EinopsError('Anonymous axes (numbers) are not allowed in EinMix')
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if '(' in weight_shape or ')' in weight_shape:
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raise EinopsError(f'Parenthesis is not allowed in weight shape: {weight_shape}')
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pre_reshape_pattern = None
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pre_reshape_lengths = None
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post_reshape_pattern = None
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if any(len(group) != 1 for group in left.composition):
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names = []
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for group in left.composition:
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names += group
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composition = ' '.join(names)
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pre_reshape_pattern = f'{left_pattern}->{composition}'
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pre_reshape_lengths = {name: length for name, length in axes_lengths.items() if name in names}
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if any(len(group) != 1 for group in right.composition):
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names = []
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for group in right.composition:
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names += group
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composition = ' '.join(names)
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post_reshape_pattern = f'{composition}->{right_pattern}'
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self._create_rearrange_layers(pre_reshape_pattern, pre_reshape_lengths, post_reshape_pattern, {})
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for axis in weight.identifiers:
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if axis not in axes_lengths:
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raise EinopsError('Dimension {} of weight should be specified'.format(axis))
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_report_axes(
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set.difference(set(axes_lengths), {*left.identifiers, *weight.identifiers}),
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'Axes {} are not used in pattern',
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)
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_report_axes(
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set.difference(weight.identifiers, {*left.identifiers, *right.identifiers}),
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'Weight axes {} are redundant'
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)
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if len(weight.identifiers) == 0:
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warnings.warn('EinMix: weight has no dimensions (means multiplication by a number)')
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_weight_shape = [axes_lengths[axis] for axis, in weight.composition]
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# single output element is a combination of fan_in input elements
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_fan_in = _product([axes_lengths[axis] for axis, in weight.composition if axis not in right.identifiers])
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if bias_shape is not None:
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if not isinstance(bias_shape, str):
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raise EinopsError('bias shape should be string specifying which axes bias depends on')
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bias = ParsedExpression(bias_shape)
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_report_axes(
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set.difference(bias.identifiers, right.identifiers),
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'Bias axes {} not present in output'
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)
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_report_axes(
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set.difference(bias.identifiers, set(axes_lengths)),
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'Sizes not provided for bias axes {}',
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)
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_bias_shape = []
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for axes in right.composition:
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for axis in axes:
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if axis in bias.identifiers:
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_bias_shape.append(axes_lengths[axis])
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else:
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_bias_shape.append(1)
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else:
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_bias_shape = None
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weight_bound = (3 / _fan_in) ** 0.5
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bias_bound = (1 / _fan_in) ** 0.5
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self._create_parameters(_weight_shape, weight_bound, _bias_shape, bias_bound)
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# rewrite einsum expression with single-letter latin identifiers so that
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# expression will be understood by any framework
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mapping2letters = {*left.identifiers, *right.identifiers, *weight.identifiers}
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mapping2letters = {k: letter for letter, k in zip(string.ascii_lowercase, mapping2letters)}
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def write_flat(axes: list):
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return ''.join(mapping2letters[axis] for axis in axes)
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self.einsum_pattern: str = '{},{}->{}'.format(
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write_flat(left.flat_axes_order()),
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write_flat(weight.flat_axes_order()),
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write_flat(right.flat_axes_order()),
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)
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def _create_rearrange_layers(self,
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pre_reshape_pattern: Optional[str],
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pre_reshape_lengths: Optional[Dict],
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post_reshape_pattern: Optional[str],
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post_reshape_lengths: Optional[Dict]):
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raise NotImplementedError('Should be defined in framework implementations')
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def _create_parameters(self, weight_shape, weight_bound, bias_shape, bias_bound):
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""" Shape and implementations """
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raise NotImplementedError('Should be defined in framework implementations')
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def __repr__(self):
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params = repr(self.pattern)
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params += f", '{self.weight_shape}'"
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if self.bias_shape is not None:
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params += f", '{self.bias_shape}'"
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for axis, length in self.axes_lengths.items():
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params += ', {}={}'.format(axis, length)
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return '{}({})'.format(self.__class__.__name__, params)
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