More NumPy operation implementations #1498
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| @@ -4426,7 +4430,7 @@ def visit_Call(self, node: ast.Call, create_callbacks=False): | |||
| arg = self.scope_vars[modname] | |||
| else: | |||
| # Fallback to (name, object) | |||
| arg = (modname, self.defined[modname]) | |||
| arg = modname | |||
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Is this change handled already in the code somehow, i.e., is self.defined queried on demand?
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It’s a code path that we didn’t cover before in coverage. The rest of the code assumes a string return type rather than a tuple, and crashes somewhere else.
| result = self._visitname(name, node) | ||
| result = self.visit(node.value) | ||
| if isinstance(result, (tuple, list, dict)): | ||
| if len(result) > 1: |
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Is this the fix for attributes on expressions?
| @@ -527,30 +535,43 @@ def _arange(pv: ProgramVisitor, sdfg: SDFG, state: SDFGState, *args, **kwargs): | |||
| else: | |||
| start, stop, step = args | |||
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| if isinstance(start, str): | |||
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The underlying issue here is data-dependent array shape?
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Yes, though generally solvable with a symbol assignment and another state, for now at least symbols and constants should be supported.
| if not isinstance(shape[0], Number) and ('dtype' not in kwargs or kwargs['dtype'] == None): | ||
| raise NotImplementedError("The current implementation of numpy.arange requires that the output dtype is given " | ||
| "when at least one of (start, stop, step) is symbolic.") | ||
| # Infer dtype from input arguments |
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The previous code looks suspicious. There is probably a reason we didn't fall back to using _result_type. Probably arange only works with integers or something. I guess it is not important if the code is "correct" (numpy-wise), but it is good to keep in mind.
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It may have been written before/without knowledge of _result_type
| elif dtype == dace.complex128: | ||
| func = "zomatcopy" | ||
| alpha = "dace::blas::BlasConstants::Get().Complex128Pone()" | ||
| cast = '(double*)' |
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I have the impression that this has been fixed in another PR, but maybe it is stuck in the queue. Just noting it in case merge issues appear later.
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It was fixed for some BLAS operators but not others. Also there is some tensor transpose PR but I think it’s GPU focused.
np.zeros(N))numpy.fft.{fft, ifft}