Note: related JAX test
func, mlir, jaxpr = None, None, None
Note: related JAX test
func, mlir, jaxpr = None, None, None
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) |
@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)
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 |
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>> |
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` |
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: |