-assume-no-undeclared-side-effects : Allow dead code to be eliminated in some situations (e.g. dead while loops) under the assumption that ops are pure unless declared with explicit MLIR `MemoryEffects`. Notably, this means `func.call` ops will be assumed pure.
-fold-op-element-limit             : Folding an op into a constant can sometimes come at the cost of memory overhead. (This occurs if the op's inputs are reused, meaning that they can't be deleted after the op is folded to a constant, or when folding operations like `iota` whose outputs take up more memory than their inputs.) In such cases, this config option sets an upper limit on how many elements an op's result may have before the op is no longer folded.
-optimize-float                    : Allow float optimizations that, though mathematically equivalent, may result in slightly different quantization of floating-point values (e.g. `log(sqrt(x))` -> `0.5 * log(x)`). Float optimizations that can't affect numerical results are always enabled.

- add(cst, X) -> add(X, cst)
- add(X, 0) -> X
- and(cst, X) -> and(X, cst)
- and(X, 0) -> 0
- and(X, 1) -> X
- broadcast_in_dim(broadcast_in_dim(X, [dimsA...]), [dimsB...]) -> broadcast_in_dim(X, merge(dimsA, dimsB))
- broadcast_in_dim(X, [dims...]) -> transpose(X, [dims...]) [if same numel & rank]
- broadcast_in_dim(X, [iota...]) -> X
- broadcast_in_dim(X, [sorted...]) -> reshape(X, [sorted...]) [if same numel]
- compare(cst, X, comparator) -> compare(X, cst, inv(comparator))
- compare(X, X, [EQ,GE,LE]) -> true
- compare(X, X, [NE,GT,LT]) -> false
- complex(real(X), imag(X))) -> X
- concatenate(concatenate(X, Y), Z) -> concatenate(X, Y, Z)
- concatenate(X) -> X
- concatenate(X, Y, []) -> concatenate(X, Y)
- convert(X, [X.type]) -> X
- dynamic_broadcast_in_dim(dynamic_broadcast_in_dim(X, _, [dimsA...]), shape, [dimsB...]) -> dynamic_broadcast_in_dim(X, shape, merge(dimsA, dimsB))
- dynamic_broadcast_in_dim(dynamic_reshape(X, shape), shape) -> dynamic_reshape(X, shape)
- dynamic_broadcast_in_dim(X, _, _, [all_nonexpanding...]) -> convert(X)
- dynamic_broadcast_in_dim(X, shape_of(X)) -> X
- dynamic_gather(x, constant(slice_sizes)) -> gather(x, slice_sizes)
- dynamic_iota(shape, dim) ->
- dynamic_pad(X, low, high, interior) -> pad(X, low, high, interior)
- dynamic_reshape(dynamic_reshape(X, _), shape)) -> dynamic_reshape(X, shape)
- dynamic_reshape(op(dynamic_reshape(X, shape)), shape)
- dynamic_slice(X, begin, slice_sizes) -> slice(X, begin, slice_sizes)
- dynamic_update_slice(X, update, start_indices : zero)) -> update
- dynamic_update_slice(X, update : zero_extent)) -> X
- gather(X, cst_start_indices) -> slice(X, slice_start, slice_end)
- get_dimension_size(X, i) -> X.shape[i]
- get_tuple_element(tuple(X_0, X_1, ...), i) -> X_i
- imag(complex(R,I)) -> I
- iota(dim) : multi_rank
- iota(dim) : type -> constant(0) : type [if type[dim] == 1]
- max(cst, X) -> max(X, cst)
- minimum(cst, X) -> minimum(X, cst)
- multiply(cst, X) -> multiply(X, cst)
- multiply(X, 0i) -> 0i
- multiply(X, 1i) -> X
- op(X : zero_extent_tensor) -> constant([])
- or(cst, X) -> or(X, cst)
- or(X, 0) -> X
- or(X, 1) -> 1
- pad(empty_tensor, _) -> broadcast_in_dim(empty_tensor, _)
- real(complex(R,I)) -> X
- real_dynamic_slice(X, start, limit, strides)
- real_dynamic_slice(X, start, limit, strides)
- reduce[A](_, _, fn:return A) -> A...
- reduce(empty_0, empty_1, ...) -> [broadcast_in_dim(empty_i)...]
- reduce(in_1, in_2, _, _) -> reduce(in_1, _, _) [if unused(in_2)]
- reduce(X..., dims=[], add) -> X...
- reshape(reshape(X, _), [shape]) -> reshape(X, [shape])
- reshape(X, [X.shape]) -> X
- select(broadcast(not(p)), t, f) => select(broadcast(p), f, t)
- select(not(p), t, f) => select(p, f, t)
- shape_of(dynamic_reshape(X, shape)) -> shape
- slice(concat(X,Y,Z,...),...) -> concat(slice(X),slice(Y),slice(Z))
- slice(X, [A:A], [B:B], ...) -> X
- sort(X) -> sort(X, dim = N) [when dim can be inferred]
- sort(X,Y) -> sort(X) [if Y unused and unused in comparator]
- subtract(X, 0) -> X
- subtract(X, X) -> 0
- transpose(X, [iota...]) -> X
- transpose(X, [no_mem_layout_change...]) -> reshape(X)
- tuple(get_tuple_element(X, 0), get_tuple_element(X, 1), ...) -> X
- while -> while (loop invariants as implicit captures)
- xor(cst, X) -> xor(X, cst)
- (+more)

-fold-op-element-limit : Folding an op into a constant can sometimes come at the cost of memory overhead. (This occurs if the op's inputs are reused, meaning that they can't be deleted after the op is folded to a constant, or when folding operations like `iota` whose outputs take up more memory than their inputs.) In such cases, this config option sets an upper limit on how many elements an op's result may have before the op is no longer folded.

-assume-no-undeclared-side-effects : Allow dead code to be eliminated in some situations (e.g. dead while loops) under the assumption that ops are pure unless declared with explicit MLIR `MemoryEffects`. Notably, this means `func.call` ops will be assumed pure.
-fold-op-element-limit             : Folding an op into a constant can sometimes come at the cost of memory overhead. (This occurs if the op's inputs are reused, meaning that they can't be deleted after the op is folded to a constant, or when folding operations like `iota` whose outputs take up more memory than their inputs.) In such cases, this config option sets an upper limit on how many elements an op's result may have before the op is no longer folded.
-optimize-float                    : Allow float optimizations that, though mathematically equivalent, may result in slightly different quantization of floating-point values (e.g. `log(sqrt(x))` -> `0.5 * log(x)`). Float optimizations that can't affect numerical results are always enabled.