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Studying JAX v0.4.19 dynamic API

Indexing with constants (runs, bad results)

Note: related JAX test

func, mlir, jaxpr = None, None, None

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diff --git a/jax/_src/interpreters/mlir.py b/jax/_src/interpreters/mlir.py
index d168d22ab..798d07a6d 100644
--- a/jax/_src/interpreters/mlir.py
+++ b/jax/_src/interpreters/mlir.py
@@ -1461,7 +1461,7 @@ def jaxpr_subcomp(ctx: ModuleContext, jaxpr: core.Jaxpr,
       ans, "lowering function returned a bad output", eqn)
     assert len(ans) == len(eqn.outvars), (ans, eqn)
     map(write, eqn.outvars, out_nodes)
-    core.clean_up_dead_vars(eqn, env, last_used)
+    # core.clean_up_dead_vars(eqn, env, last_used)

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@qjit(autograph=True, verbose=True, keep_intermediate=True)
@qml.qnode(qml.device("lightning.qubit", wires=1))
def fn(x: float):
    qml.RY(x, wires=0)
    return qml.expval(qml.PauliZ(0))

fn(np.pi)

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Traceback (most recent call last):
  File "/workspace/modules/catalyst/frontend/catalyst/compiler.py", line 365, in run_from_ir
    compiler_output = run_compiler_driver(
RuntimeError: Compilation failed:
f:6:3: error: operand #0 does not dominate this use
  func.func private @"<lambda>"(%arg0: tensor<f64>) -> tensor<f64> attributes {llvm.linkage = #llvm.linkage<internal>} {
  ^
f:6:3: note: diagnostic emitted with trace:
 #0 0x00007f08dcb4ac13 llvm::sys::PrintStackTrace(llvm::raw_ostream&, int) (/workspace/_build_dialects/python_packages/quantum/mlir_quantum/compiler_driver.so+0x8b4ac13)
 #1 0x00007f08da98c1b9 emitDiag(mlir::Location, mlir::DiagnosticSeverity, llvm::Twine const&) Diagnostics.cpp:0:0

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module @C_workflow {
  llvm.func @__quantum__rt__finalize()
  llvm.func @__quantum__rt__initialize()
  llvm.mlir.global internal constant @backend_best("best\00") {addr_space = 0 : i32}
  llvm.mlir.global internal constant @backend("backend\00") {addr_space = 0 : i32}
  llvm.func @__quantum__rt__device(!llvm.ptr<i8>, !llvm.ptr<i8>)
  llvm.func @free(!llvm.ptr<i8>)
  llvm.func @malloc(i64) -> !llvm.ptr<i8>
  llvm.mlir.global private constant @__constant_4x3x4xf64(dense<9.9999999999999995E-8> : tensor<4x3x4xf64>) {addr_space = 0 : i32} : !llvm.array<4 x array<3 x array<4 x f64>>>
  llvm.mlir.global private constant @__constant_4x4xf64(dense<9.9999999999999995E-8> : tensor<4x4xf64>) {addr_space = 0 : i32} : !llvm.array<4 x array<4 x f64>>

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from frozendict import frozendict
from typing import List, Any, Callable
from copy import deepcopy


Dim = int                   # Dimention "names"
Index = Dict[Dim, int]      # `Index :: Dimention -> Coordinate`
                            # (To-be implemented as Frozendict to make Python data hashable)
Value = complex             # A value
Tensor = Dict[Index, Value] # `Tensor :: Index -> Value`

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V1

print("with attrs")

with attrs

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                                 enable  prepare  protect                                duty  hardware
   clock                          count    count    count        rate   accuracy phase  cycle    enable
-------------------------------------------------------------------------------------------------------
 iosc                                 1        1        0    16000000  300000000     0  50000         Y
    ar100                             1        1        0    16000000  300000000     0  50000         Y
       ahb0                           1        1        0    16000000  300000000     0  50000         Y
          apb0                        2        2        0    16000000  300000000     0  50000         Y
             apb0-twd                 1        1        0    16000000  300000000     0  50000         Y
             apb0-i2c                 0        0        0    16000000  300000000     0  50000         N
             apb0-uart                0        0        0    16000000  300000000     0  

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def latex2pdf(expr:str,
              out_pdf:Filepath,
              font:str='normalsize',
              font_white:bool=False,
              texname:str='latex'
              )->None:
  makedirs('_tex',exist_ok=True)
  tex=f'_tex/{texname}.tex'
  pdf=f'_tex/{texname}.pdf'
  with open(tex, 'w') as f: