ofnote / tsalib Goto Github PK

tsalib is tested with Python 3.6 but the core API implementation should work with Python 2.x with minor changes.

Can tsalib be compared with datashape? This is assuming there is a basis for comparison.

Error in warp(x, 'b*t,d -> b,t,n,d//n', 'v')

tensorflow: https://github.com/google-research/bert
pytorch: https://github.com/huggingface/pytorch-pretrained-BERT

Should compress the model description significantly.

May expose other shape transformation instances where tsalib can help.

Avoid carrying dim vars in config dictionary. Just look them up wherever needed by name.

update size of dim vars.

Does it make sense to use tsalib for Pandas series and/or dataframes?

Explain different forms of shorthand, and their usage.

This is a great library! I am wondering whether 'b,1,d' should be 'b,l,d' in the following assignment that I found in README.md:
b: 'b,1,d' = alignto((a,'bd'), 'bld')

There is already a setup.py file which defines the requirements. tsalib probably doesn't need a requirements.txt file since it's not used as a standalone application.

Currently, you have to do an expand_dims and then expand_transform.

Is there a crisper way of combining them using shorthand notation?

Old:

x = np.random.randn(10,50,300)
x = x[None, None, None, :].permute(3, 0, 1, 2)

New:

from tsalib import dim_vars, get_dim_vars, alignto, warp
# B, T, D, C = dim_vars('B(b):10 T(t):50 D(d):300 C(c):4') #one time, in __main__

B, T, D, C = get_dim_vars('b t d c')
x: 'btd' = np.random.randn(B,T,D)
x = alignto((x, 'btd'), 'btdc')
x = warp(x, 'btdc -> cbtd', 'p') #'p' for permute

combine last two lines as:

x = warp(x, 'btd -> btdc -> cbtd', 'ap') #'a' for align

Hi,

I'm trying to instantiate OpenaiTransformer class from openai_transformer.py code in transformer toy problem setting to learn your tsalib a bit. I'm curious to learn how useful tensor shape annotations would be in practice.

However, I've noticed some syntax level errors in code, e.g.:

1. in Conv1D.forward() body there is self.nx attribute referenced that is not defined.
2. is "def forward(self, x: (B, T, self.nx)) -> torch.Tensor:" syntax valid? It causes a crash
3. Attention.forward(self, x: (B, T, self.nx)) -> torch.Tensor: causes a crash "NameError: name 'self' is not defined"
4. no TransformerConfig class in source file
5. in class OpenaiTransformer.forward(self, x: (B,T)) -> List[B, T, D]:" last part "[B, T, D]" causes a crash "TypeError: Parameters to generic types must be types. Got Batch."

How would you go about correcting these problems?

Best,
Wojtek

• Update batch dimvar per training iteration.

• user only specifies the shape transformation sequence
• guess 'v' or 'p' or 'e' or 'c' (restricted to patterns over these shorthands?)
• throw error if none of the above transforms.

So I'm declaring global DimVars (without a hardcoded value) and later at runtime want to update their values based on some hyperparams to do shape assertions.
I'm aware I can just manipulate their ._val propery, but that seems a bit hacky to me, what's the intended workflow for this kind of problem?

dvs('Lq(lq):100')

warp(x, ['btd->bthn->btnh', 'btnh -> b*t,n*h'], tfm_names=['vp', 'v'])

Right now join spec consists of a single shape annotation with * or ^

Allow join spec to be in l -> r form, e.g. (b,t,d)* -> (^,b,t,d). Then, a warp sequence can contain a join.