Add support for PyTorch and TensorFlow in expressions #509
Description
Right now, this does not work:
import blosc2
import numpy as np
import torch
N = 10
shape_a = (N, N, N)
matrix_numpy = np.ones(N ** 3).reshape(shape_a)
matrix_a = blosc2.asarray(matrix_numpy, urlpath="a.b2nd", mode="w")
matrix_b = blosc2.asarray(matrix_numpy, urlpath="b.b2nd", mode="w")
matrix_c = torch.ones(shape_a)
# Create a lazy expression object
sexpr = "matrix_a.sum() + matrix_b" # this works
# lexpr = blosc2.lazyexpr(sexpr) # this works
lexpr = blosc2.lazyexpr(sexpr, operands={"matrix_a": matrix_a, "matrix_b": matrix_c}) # this does not
print(lexpr[:])this errors with:
Traceback (most recent call last):
File "/Users/faltet/blosc/python-blosc2/torch-failure.py", line 15, in <module>
lexpr = blosc2.lazyexpr(sexpr, operands={"matrix_a": matrix_a, "matrix_b": matrix_c}) # this does not
File "/Users/faltet/blosc/python-blosc2/src/blosc2/lazyexpr.py", line 3477, in lazyexpr
return LazyExpr._new_expr(expression, operands, guess=True, out=out, where=where, ne_args=ne_args)
~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/Users/faltet/blosc/python-blosc2/src/blosc2/lazyexpr.py", line 3005, in _new_expr
_operands[op] = blosc2.SimpleProxy(val)
~~~~~~~~~~~~~~~~~~^^^^^
File "/Users/faltet/blosc/python-blosc2/src/blosc2/proxy.py", line 613, in __init__
self.chunks, self.blocks = blosc2.compute_chunks_blocks(
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^
self.shape, chunks, blocks, self.dtype, **{"cparams": cparams}
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/Users/faltet/blosc/python-blosc2/src/blosc2/core.py", line 1512, in compute_chunks_blocks
itemsize = cparams["typesize"] = np.dtype(dtype).itemsize
~~~~~~~~^^^^^^^
TypeError: Cannot interpret 'torch.float32' as a data type
And the same should work for TensorFlow.