[WIP] Add measurements-from-samples pass to Python compiler #7620
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Hello. You may have forgotten to update the changelog!
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| # pylint: disable=arguments-renamed,no-self-use | ||
| def apply(self, _ctx: context.Context, module: builtin.ModuleOp) -> None: | ||
| """Apply the measurements-from-samples pass.""" |
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Some comments:
- If your post-processing functions can be generated in the apply method here instead of the pattern, that in my opinion is preferrable. A pattern is inside of a loop. You can just create a
Rewriter()inside theapplymethod. - If they can't be automatically generated (for example, you may generate N different functions which depend on the types of N number of inputs) then you will likely need to implement some sort of namespacing. E.g.,
func.func @postrocessing_expval.tensor.8xf64(%arg0 : tensor<8xf64>, %arg1: int) {
...
But maybe you could do this a bit more generic:
func.func @postrocessing_expval.tensor(%arg0 : tensor<?xf64>, %tensor_size: i64, %wire: i64) {
...
- There's also
rewriter.replace_all_uses_withwhich may help in some areas before deleting operations. - You may want different patterns (one for expval, one for var...).
- If you only care about expval, you can match directly (you don't need to walk through the function)
def match_and_rewrite(self, op_you_care_about: quantum.ExpvalOp, rewriter: pattern_rewriter.PatternRewriter):
- The body of your apply can be more complex, running a set of patterns first and then a second set of patterns. That way you can add some sequential logic.
def apply(self, _ctx: context.Context, module: builtin.ModuleOp) -> None:
"""Apply the measurements-from-samples pass."""
pattern_rewriter.PatternRewriteWalker(
pattern_rewriter.GreedyRewritePatternApplier([OneSetOfPatterns()])
).rewrite_module(module)
pattern_rewriter.PatternRewriteWalker(
pattern_rewriter.GreedyRewritePatternApplier([AnotherSetOfPatterns()])
).rewrite_module(module)
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Also, depending on how comfortable you are writing MLIR by handle, you can use:
@xdsl_from_docstring
def foo():
"""
func.func @postrocessing_expval(%arg0 : tensor<?xf64>, %tensor_size: i64, %wire: i64) ...
"""
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The other function we discussed for possibly keeping it all in jax is jax.jit(f, abstracted_axes=...) but it is undocumented and not well behaved.
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Agree with Erick's comments. In particular, using the dynamic tensor approach would also allow you to use the same piece of IR for all instances of the problem.
@xdsl_from_docstring
def foo():
"""
func.func @postrocessing_expval(%arg0 : tensor<?xf64>, %tensor_size: i64, %wire: i64) ...
"""
Just a note on this last segment. There is no need to pass in a tensor size value (since this isn't C 😅), you can query the size of any dimension of any tensor with the tensor.dim operation.
| from .apply_transform_sequence import register_pass | ||
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| def xdsl_transform(_klass): |
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This has been merged now I believe?
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