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Revert "Support float8_e3m4 and float8_e4m3 in np_to_memref (#186453)" (#186677)

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This reverts commit 57427f84fe.

For some reason mlir-nvidia CI is failing to import `float8_e3m4` from
`ml_dtypes`. See
https://lab.llvm.org/buildbot/#/builders/138/builds/27095.
This commit is contained in:
srcarroll 2026-03-15 11:52:59 -05:00 committed by GitHub
parent 27dd55b2cb
commit 9e22690671
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GPG key ID: B5690EEEBB952194
2 changed files with 6 additions and 109 deletions
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29
mlir/python/mlir/runtime/np_to_memref.py
View file

@ -37,25 +37,12 @@ class BF16(ctypes.Structure):
_fields_ = [("bf16", ctypes.c_int16)]
class F8E5M2(ctypes.Structure):
"""A ctype representation for MLIR's Float8E5M2."""
_fields_ = [("f8E5M2", ctypes.c_int8)]
class F8E3M4(ctypes.Structure):
"""A ctype representation for MLIR's Float8E3M4."""
_fields_ = [("f8E3M4", ctypes.c_int8)]
class F8E4M3(ctypes.Structure):
"""A ctype representation for MLIR's Float8E4M3."""
_fields_ = [("f8E4M3", ctypes.c_int8)]
# https://stackoverflow.com/questions/26921836/correct-way-to-test-for-numpy-dtype
def as_ctype(dtp):
"""Converts dtype to ctype."""
@ -69,10 +56,6 @@ def as_ctype(dtp):
return BF16
if ml_dtypes is not None and dtp == ml_dtypes.float8_e5m2:
return F8E5M2
if ml_dtypes is not None and dtp == ml_dtypes.float8_e3m4:
return F8E3M4
if ml_dtypes is not None and dtp == ml_dtypes.float8_e4m3:
return F8E4M3
return np.ctypeslib.as_ctypes_type(dtp)
@ -85,17 +68,15 @@ def to_numpy(array):
if array.dtype == F16:
return array.view("float16")
assert not (
array.dtype in (BF16, F8E5M2, F8E3M4, F8E4M3) and ml_dtypes is None
), f"{array.dtype=} requires the ml_dtypes package, please run:\n\npip install ml_dtypes\n"
array.dtype == BF16 and ml_dtypes is None
), f"bfloat16 requires the ml_dtypes package, please run:\n\npip install ml_dtypes\n"
if array.dtype == BF16:
return array.view("bfloat16")
assert not (
array.dtype == F8E5M2 and ml_dtypes is None
), f"float8_e5m2 requires the ml_dtypes package, please run:\n\npip install ml_dtypes\n"
if array.dtype == F8E5M2:
return array.view("float8_e5m2")
if array.dtype == F8E3M4:
return array.view("float8_e3m4")
if array.dtype == F8E4M3:
return array.view("float8_e4m3")
return array

86
mlir/test/python/execution_engine.py
View file

@ -8,7 +8,7 @@ from mlir.execution_engine import *
from mlir.runtime import *
try:
from ml_dtypes import bfloat16, float8_e5m2, float8_e3m4, float8_e4m3
from ml_dtypes import bfloat16, float8_e5m2
HAS_ML_DTYPES = True
except ModuleNotFoundError:
@ -623,90 +623,6 @@ else:
log("TEST: testF8E5M2Memref")
# Test f8E3M4 memrefs
# CHECK-LABEL: TEST: testF8E3M4Memref
def testF8E3M4Memref():
with Context():
module = Module.parse(
"""
module {
func.func @main(%arg0: memref<1xf8E3M4>,
%arg1: memref<1xf8E3M4>) attributes { llvm.emit_c_interface } {
%0 = arith.constant 0 : index
%1 = memref.load %arg0[%0] : memref<1xf8E3M4>
memref.store %1, %arg1[%0] : memref<1xf8E3M4>
return
}
} """
)
arg1 = np.array([0.5]).astype(float8_e3m4)
arg2 = np.array([0.0]).astype(float8_e3m4)
arg1_memref_ptr = ctypes.pointer(
ctypes.pointer(get_ranked_memref_descriptor(arg1))
)
arg2_memref_ptr = ctypes.pointer(
ctypes.pointer(get_ranked_memref_descriptor(arg2))
)
execution_engine = ExecutionEngine(lowerToLLVM(module))
execution_engine.invoke("main", arg1_memref_ptr, arg2_memref_ptr)
# test to-numpy utility
x = ranked_memref_to_numpy(arg2_memref_ptr[0])
assert len(x) == 1
assert x[0] == 0.5
if HAS_ML_DTYPES:
run(testF8E3M4Memref)
else:
log("TEST: testF8E3M4Memref")
# Test f8E4M3 memrefs
# CHECK-LABEL: TEST: testF8E4M3Memref
def testF8E4M3Memref():
with Context():
module = Module.parse(
"""
module {
func.func @main(%arg0: memref<1xf8E4M3>,
%arg1: memref<1xf8E4M3>) attributes { llvm.emit_c_interface } {
%0 = arith.constant 0 : index
%1 = memref.load %arg0[%0] : memref<1xf8E4M3>
memref.store %1, %arg1[%0] : memref<1xf8E4M3>
return
}
} """
)
arg1 = np.array([0.5]).astype(float8_e4m3)
arg2 = np.array([0.0]).astype(float8_e4m3)
arg1_memref_ptr = ctypes.pointer(
ctypes.pointer(get_ranked_memref_descriptor(arg1))
)
arg2_memref_ptr = ctypes.pointer(
ctypes.pointer(get_ranked_memref_descriptor(arg2))
)
execution_engine = ExecutionEngine(lowerToLLVM(module))
execution_engine.invoke("main", arg1_memref_ptr, arg2_memref_ptr)
# test to-numpy utility
x = ranked_memref_to_numpy(arg2_memref_ptr[0])
assert len(x) == 1
assert x[0] == 0.5
if HAS_ML_DTYPES:
run(testF8E4M3Memref)
else:
log("TEST: testF8E4M3Memref")
# Test addition of two 2d_memref
# CHECK-LABEL: TEST: testDynamicMemrefAdd2D
def testDynamicMemrefAdd2D():