forked from mindspore-Ecosystem/mindspore
update ms_kernel related files
fix import problem update code check update test case update test case rename file
This commit is contained in:
Zichun Ye
parent
b041966ea2
commit
8de24b4d2f
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@ -31,7 +31,7 @@ MindSpore中 `mindspore.ops` 接口与上一版本相比，新增、删除和支
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mindspore.ops.constexpr
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mindspore.ops.custom_info_register
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mindspore.ops.ms_hybrid
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mindspore.ops.ms_kernel
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mindspore.ops.op_info_register
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mindspore.ops.prim_attr_register
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@ -1,7 +1,7 @@
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mindspore.ops.ms_hybrid
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mindspore.ops.ms_kernel
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=======================
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.. py:function:: mindspore.ops.ms_hybrid(fn=None, reg_info=None, compile_attrs=None)
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.. py:function:: mindspore.ops.ms_kernel(fn=None, reg_info=None, compile_attrs=None)
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用于MindSpore Hybrid DSL函数书写的装饰器。
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给用MindSpore Hybrid DSL书写的函数加上此装饰器后，它可以用作一个普通的Python函数。
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@ -31,7 +31,7 @@ Decorators
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mindspore.ops.constexpr
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mindspore.ops.custom_info_register
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mindspore.ops.ms_hybrid
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mindspore.ops.ms_kernel
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mindspore.ops.op_info_register
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mindspore.ops.prim_attr_register
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@ -29,7 +29,7 @@ from ._thor_ops import (CusBatchMatMul, CusCholeskyTrsm, CusFusedAbsMax1, CusImg
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CusMatMulCubeDenseRight, CusMatMulCubeFraczLeftCast, Im2Col, NewIm2Col,
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LoadIm2Col, UpdateThorGradient, Cholesky, CholeskyTrsm,
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DetTriangle, ProdForceSeA)
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from ._ms_hybrid import (ms_hybrid)
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from ._ms_kernel import (ms_kernel, ms_hybrid)
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from .array_ops import (Argmax, Argmin, Cast, Concat, Pack, Stack, Unpack, Unstack,
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Diag, DiagPart, DType, ExpandDims, Eye,
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Fill, Ones, Zeros, GatherNd, GatherV2, Gather, SparseGatherV2, InvertPermutation,
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@ -591,6 +591,7 @@ __sponge__ = [
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]
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__custom__ = [
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"ms_kernel",
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"ms_hybrid",
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]
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@ -12,14 +12,14 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ============================================================================
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"""ms_hybrid decorator and related util functions"""
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"""ms_kernel decorator and related util functions"""
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import ast
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import json
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from functools import wraps
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from itertools import product
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import numpy
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from mindspore import context
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from mindspore import context, log
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def _allocate(shape, dtype='float32', scope='global'):
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@ -86,15 +86,15 @@ def _erf(x):
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p = 0.3275911
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# A&S formula 7.1.26
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t = 1.0/(1.0 + p*x)
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y = 1.0 - (((((a5*t + a4)*t) + a3)*t + a2)*t + a1)*t*numpy.exp(-x*x)
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return sign*y # erf(-x) = -erf(x)
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t = 1.0 / (1.0 + p * x)
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y = 1.0 - (((((a5 * t + a4) * t) + a3) * t + a2) * t + a1) * t * numpy.exp(-x * x)
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return sign * y # erf(-x) = -erf(x)
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def _grid(extents):
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extents_list = []
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for ext in extents:
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extents_list.append(range(ext))
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extents_list.append(list(range(ext)))
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return product(*extents_list)
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@ -120,22 +120,32 @@ class WithStub:
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return self
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INTRIN_BUFFER = {
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class VariableUsage(ast.NodeVisitor):
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"""
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The ast visitor to perform static check for the source code,
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and determine the index of inplace assign outputs
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"""
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intrin_buffer = {
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'allocate': _allocate,
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'output_tensor': _allocate
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}
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}
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INTRIN_LOOP = {
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intrin_loop = {
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'range': range,
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'serial': range,
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'vectorize': range,
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'parallel': range,
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'reduce': range,
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'grid': _grid,
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}
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}
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INTRIN_WITH_SCOPE = {
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intrin_with_scope = {
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'attr': WithStub(),
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'block_realize': WithStub(),
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}
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}
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INTRIN_UNARY_OP = {
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intrin_unary_op = {
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'sqrt': numpy.sqrt,
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'sign': numpy.sign,
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'log': numpy.log,
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@ -145,21 +155,21 @@ INTRIN_UNARY_OP = {
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'int32': numpy.int32,
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'float16': numpy.float16,
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'float32': numpy.float32,
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}
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}
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INTRIN_BINARY_OP = {
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intrin_bin_op = {
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'power': numpy.power,
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}
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}
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INTRIN_GLOBALS = {
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**INTRIN_BUFFER,
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**INTRIN_LOOP,
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**INTRIN_WITH_SCOPE,
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**INTRIN_UNARY_OP,
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**INTRIN_BINARY_OP,
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}
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intrin_globals = {
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**intrin_buffer,
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**intrin_loop,
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**intrin_with_scope,
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**intrin_unary_op,
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**intrin_bin_op,
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}
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INTRIN_GENERAL_UNARY_OP = {
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intrin_general_unary_op = {
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'rsqrt': _rsqrt,
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'erf': _erf,
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'isnan': numpy.isnan,
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@ -178,30 +188,23 @@ INTRIN_GENERAL_UNARY_OP = {
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'ceil': numpy.ceil,
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'trunc': numpy.trunc,
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'round': numpy.round,
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}
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}
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INTRIN_CPU_NOT_SUPPORT = ["atan2", "expm1", "float16"]
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intrin_cpu_not_support = ["atan2", "expm1", "float16"]
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INTRIN_GENERAL_BINARY_OP = {
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intrin_general_bin_op = {
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'ceil_div': lambda a, b: (a + b - 1) // b,
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}
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}
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INTRIN_GENERAL = {
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**INTRIN_GENERAL_UNARY_OP,
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**INTRIN_GENERAL_BINARY_OP
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}
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intrin_general = {
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**intrin_general_unary_op,
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**intrin_general_bin_op
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}
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INTRIN_RUNTIME = {
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**INTRIN_GLOBALS,
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**INTRIN_GENERAL
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}
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class VariableUsage(ast.NodeVisitor):
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"""
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The ast visitor to perform static check for the source code,
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and determine the index of inplace assign outputs
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"""
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intrin_runtime = {
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**intrin_globals,
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**intrin_general
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}
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def __init__(self, func_name):
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self.func_name = func_name
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@ -276,27 +279,27 @@ class VariableUsage(ast.NodeVisitor):
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"""
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Ast visitor for Call
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Check the func call used in the DSL. Only those in INTRIN_RUNTIME are supported for now.
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Check the func call used in the DSL. Only those in intrin_runtime are supported for now.
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"""
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func_id = node.func.id
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if not (func_id in list(INTRIN_RUNTIME.keys()) +
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['max', 'min', 'len', 'ms_hybrid']):
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if not (func_id in list(VariableUsage.intrin_runtime.keys()) +
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['max', 'min', 'len', 'ms_kernel']):
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raise ValueError(
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"In the function {} written in the Hybrid DSL, function call id {} "
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"not in intrinsics' list".format(self.func_name, func_id))
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if (self.device == "Ascend" and func_id in list(INTRIN_GENERAL.keys())) or \
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(self.device == "CPU" and func_id in INTRIN_CPU_NOT_SUPPORT):
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if (self.device == "Ascend" and func_id in list(VariableUsage.intrin_general.keys())) or \
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(self.device == "CPU" and func_id in VariableUsage.intrin_cpu_not_support):
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raise ValueError(
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"In the function {} written in the Hybrid DSL, function {} is not available on the "
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"device {}".format(self.func_name, func_id, self.device))
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if func_id in list(INTRIN_UNARY_OP.keys()) + list(INTRIN_GENERAL_UNARY_OP.keys()) + list(INTRIN_LOOP.keys()) \
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if func_id in list(VariableUsage.intrin_unary_op.keys()) + list(VariableUsage.intrin_general_unary_op.keys()) \
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and len(node.args) != 1:
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raise TypeError(
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"In the function {} written in the Hybrid DSL, function {} "
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"expects one input, but get {}".format(self.func_name, func_id, len(node.args)))
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if func_id in list(INTRIN_BINARY_OP.keys()) + list(INTRIN_GENERAL_BINARY_OP.keys()) + \
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list(INTRIN_BUFFER.keys()) and len(node.args) != 2:
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if func_id in list(VariableUsage.intrin_bin_op.keys()) + list(VariableUsage.intrin_general_bin_op.keys()) + \
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list(VariableUsage.intrin_buffer.keys()) and len(node.args) != 2:
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raise TypeError(
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"In the function {} written in the Hybrid DSL, function {} "
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"expects two inputs, but get {}".format(self.func_name, func_id, len(node.args)))
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@ -470,10 +473,10 @@ def determine_variable_usage(root, func_name):
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return visitor.inplace_assign_output
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def ms_hybrid(fn=None, reg_info=None, compile_attrs=None):
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def ms_kernel(fn=None, reg_info=None, compile_attrs=None):
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"""
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The decorator of the Hybrid DSL function for the Custom Op.
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When a function written by the Hybrid DSL is decorated by ms_hybrid,
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When a function written by the Hybrid DSL is decorated by ms_kernel,
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it can be run as a usual Python function.
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Also, this function can be used in the api Custom and to create a Custom op, with func_type
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"hybrid" or "pyfunc". Creating a custom op with mode "hybrid" by the Hybrid DSL function
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@ -495,7 +498,7 @@ def ms_hybrid(fn=None, reg_info=None, compile_attrs=None):
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Examples:
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>>> import numpy as np
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>>> from mindspore import ops, Tensor
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>>> from mindspore.ops import ms_hybrid, DataType, CustomRegOp
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>>> from mindspore.ops import ms_kernel, DataType, CustomRegOp
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...
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>>> # Create a dict for the compile flags.
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>>> attrs = {
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@ -516,9 +519,9 @@ def ms_hybrid(fn=None, reg_info=None, compile_attrs=None):
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>>> input_x = np.ones([4, 4]).astype(np.float32)
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>>> input_y = np.ones([4, 4]).astype(np.float32)
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...
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>>> # Write a Hybrid DSL function through the decorator @ms_hybrid.
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>>> # Write a Hybrid DSL function through the decorator @ms_kernel.
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>>> # We can also pass the compile attrs and the reg info through the decorator.
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>>> @ms_hybrid(reg_info=op_gpu_info, compile_attrs=attrs)
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>>> @ms_kernel(reg_info=op_gpu_info, compile_attrs=attrs)
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... def outer_product(a, b):
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... c = output_tensor(a.shape, a.dtype)
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...
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@ -544,21 +547,25 @@ def ms_hybrid(fn=None, reg_info=None, compile_attrs=None):
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compile_attrs = {}
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if not isinstance(compile_attrs, dict):
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raise TypeError("The input 'compile_attrs' of @ms_hybrid must be a dict, "
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raise TypeError("The input 'compile_attrs' of @ms_kernel must be a dict, "
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"but get a {}".format(type(compile_attrs)))
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for key in compile_attrs.keys():
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if not isinstance(key, str):
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raise TypeError("The key of 'compile_attrs' of @ms_hybrid must be a str, "
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raise TypeError("The key of 'compile_attrs' of @ms_kernel must be a str, "
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"but get a {}".format(type(key)))
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if reg_info is not None and not isinstance(reg_info, (str, dict, tuple)):
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raise TypeError(
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"The input 'reg_info' of @ms_hybrid should be one of "
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"The input 'reg_info' of @ms_kernel should be one of "
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"str, dict and tuple, but get a {}".format(type(reg_info)))
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def wrap_ms_hybrid(func):
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setattr(func, "ms_hybrid_flag", True)
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def wrap_ms_kernel(func):
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setattr(func, "ms_kernel_flag", True)
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# we enable ml scheduler automatically for ms_kernel function
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compile_attrs["enable_mlsched"] = True
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setattr(func, "compile_attrs", json.dumps(compile_attrs))
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if reg_info is not None:
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setattr(func, "reg_info", reg_info)
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@ -566,12 +573,25 @@ def ms_hybrid(fn=None, reg_info=None, compile_attrs=None):
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@wraps(func)
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def _patch_intrins_to_runtime(*args):
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_globals = func.__globals__
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for elem in list(INTRIN_RUNTIME.keys()):
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_globals[elem] = INTRIN_RUNTIME[elem]
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for elem in list(VariableUsage.intrin_runtime.keys()):
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_globals[elem] = VariableUsage.intrin_runtime[elem]
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return func(*args)
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return _patch_intrins_to_runtime
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if fn is not None:
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return wrap_ms_hybrid(fn)
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return wrap_ms_hybrid
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return wrap_ms_kernel(fn)
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return wrap_ms_kernel
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def ms_hybrid(fn=None, reg_info=None, compile_attrs=None):
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"""
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Same as docarator ms_kernel. ms_hybrid will be deprecated in the future.
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Please use ms_kernel instead.
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Supported Platforms:
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Deprecated
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"""
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log.warning("'ms_hybrid' is deprecated from version 1.8 and "
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"will be removed in a future version, use 'ms_kernel' instead.")
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return ms_kernel(fn, reg_info, compile_attrs)
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@ -29,7 +29,7 @@ from mindspore.common import dtype as mstype
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from mindspore.ops import DataType
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from mindspore import log as logger
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from mindspore import ops
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from ._ms_hybrid import determine_variable_usage
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from ._ms_kernel import determine_variable_usage
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from ._custom_grad import autodiff_bprop
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from ._pyfunc_registry import add_pyfunc
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@ -107,7 +107,7 @@ class Custom(ops.PrimitiveWithInfer):
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1. A AKG operator implementation function, which can use ir builder/tvm compute/hybrid grammar.
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2. A TBE operator implementation function.
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3. A pure python function
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4. An ms_hybrid decorated function written by the Hybrid DSL.
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4. An ms_kernel decorated function written by the Hybrid DSL.
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- str: If func is of str type, then str should be a path of file along with a function name.
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This could be used when func_type is "aot" or "julia".
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@ -260,7 +260,7 @@ class Custom(ops.PrimitiveWithInfer):
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Examples:
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>>> import mindspore.ops as ops
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>>> import numpy as np
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>>> from mindspore.ops import CustomRegOp, custom_info_register, DataType, ms_hybrid
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>>> from mindspore.ops import CustomRegOp, custom_info_register, DataType, ms_kernel
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>>> from mindspore.common import dtype as mstype
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>>> from mindspore.nn import Cell
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>>> input_x = Tensor(np.ones([16, 16]).astype(np.float32))
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@ -270,8 +270,8 @@ class Custom(ops.PrimitiveWithInfer):
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>>> # This is the default func_type in Custom,
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>>> # and both out_shape and out_dtype can be None(default value).
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>>> # In this case, the input func must be a function written in the Hybrid DSL
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>>> # and decorated by @ms_hybrid.
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>>> @ms_hybrid
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>>> # and decorated by @ms_kernel.
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>>> @ms_kernel
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... def outer_product_script(a, b):
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... c = output_tensor(a.shape, a.dtype)
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... for i0 in range(a.shape[0]):
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@ -402,7 +402,7 @@ class Custom(ops.PrimitiveWithInfer):
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self.imply_path = ""
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self.func_source_str = ""
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self._func_compile_attrs = {}
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self._is_ms_hybrid = False
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self._is_ms_kernel = False
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self._check_func()
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self._update_func_info()
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@ -488,20 +488,20 @@ class Custom(ops.PrimitiveWithInfer):
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elif self.func_type == "julia":
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self._check_julia_func()
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elif self.func_type == "hybrid":
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if not hasattr(self.func, "ms_hybrid_flag"):
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raise TypeError("{}, 'func' must be a function decorated by ms_hybrid".format(self.log_prefix))
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self._is_ms_hybrid = True
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if not hasattr(self.func, "ms_kernel_flag"):
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raise TypeError("{}, 'func' must be a function decorated by ms_kernel".format(self.log_prefix))
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self._is_ms_kernel = True
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self._func_compile_attrs = getattr(self.func, "compile_attrs", {})
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elif self.func_type == "akg":
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if hasattr(self.func, "ms_hybrid_flag"):
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if hasattr(self.func, "ms_kernel_flag"):
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logger.warning("{}. To have a better user experience, the mode hybrid is suggested "
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"for the input function with decorator @ms_hybrid. "
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"for the input function with decorator @ms_kernel. "
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"To enable this mode, set the 'func_type' to be \"hybrid\"".format(self.log_prefix))
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elif self.func_type == "pyfunc":
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if hasattr(self.func, "ms_hybrid_flag"):
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logger.warning("{}. Now you are using the function with decorator @ms_hybrid in the mode pyfunc. "
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if hasattr(self.func, "ms_kernel_flag"):
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logger.warning("{}. Now you are using the function with decorator @ms_kernel in the mode pyfunc. "
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"The kernel will be executed as a native python function, which might lead to "
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"low efficiency. To accelerate the kernel, set the 'func_type' to be \"ms_hybrid\""
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"low efficiency. To accelerate the kernel, set the 'func_type' to be \"hybrid\""
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.format(self.log_prefix))
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else:
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if not callable(self.func):
|
||||
|
|
@ -524,7 +524,7 @@ class Custom(ops.PrimitiveWithInfer):
|
|||
if index != -1:
|
||||
self.func_source_str = self.func_source_str[index:]
|
||||
|
||||
if self._is_ms_hybrid:
|
||||
if self._is_ms_kernel:
|
||||
# static check for the Hybrid DSL in hybrid
|
||||
root = ast.parse(self.func_source_str)
|
||||
inplace_assign_output = determine_variable_usage(root, self.func_name)
|
||||
|
|
@ -848,7 +848,7 @@ class Custom(ops.PrimitiveWithInfer):
|
|||
|
||||
def _auto_infer(self, *args):
|
||||
"""
|
||||
the automatic infer function for functions with @ms_hybrid decorator
|
||||
the automatic infer function for functions with @ms_kernel decorator
|
||||
"""
|
||||
fake_input = []
|
||||
enable_infer_value = True
|
||||
|
|
@ -893,7 +893,7 @@ class Custom(ops.PrimitiveWithInfer):
|
|||
|
||||
# deal with the case of ms script
|
||||
# enable auto infer function if any infer information is missing
|
||||
if self._is_ms_hybrid and (infer_dtype is None or infer_shape is None):
|
||||
if self._is_ms_kernel and (infer_dtype is None or infer_shape is None):
|
||||
logger.warning("{}, 'out_shape' or 'out_dtype' is None, infer the output shape and output dtype "
|
||||
"automatically. There might be some Python RuntimeWarning but it wouldn't influence the "
|
||||
"result.".format(self.log_prefix))
|
||||
|
|
|
|||
|
|
@ -334,6 +334,7 @@ def irbuilder_case():
|
|||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
|
|
@ -347,6 +348,7 @@ def test_irbuilder_ascend_graph_mode():
|
|||
irbuilder_case()
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
|
|
|
|||
|
|
@ -19,10 +19,10 @@ import numpy as np
|
|||
from mindspore import context, Tensor
|
||||
from mindspore.nn import Cell
|
||||
import mindspore.ops as ops
|
||||
from mindspore.ops import ms_hybrid
|
||||
from mindspore.ops import ms_kernel
|
||||
|
||||
|
||||
@ms_hybrid
|
||||
@ms_kernel
|
||||
def dtype_and_cast_example(a, b):
|
||||
"""
|
||||
test function for dtype and cast in Hybrid DSL
|
||||
|
|
@ -38,7 +38,7 @@ def dtype_and_cast_example(a, b):
|
|||
return c
|
||||
|
||||
|
||||
@ms_hybrid
|
||||
@ms_kernel
|
||||
def allocate_and_math_intrin_example(a, b):
|
||||
"""
|
||||
test function for allocate and math function in Hybrid DSL
|
||||
|
|
@ -53,7 +53,7 @@ def allocate_and_math_intrin_example(a, b):
|
|||
return c
|
||||
|
||||
|
||||
@ms_hybrid
|
||||
@ms_kernel
|
||||
def grid_example(a, b):
|
||||
"""
|
||||
test function for grid in Hybrid DSL
|
||||
|
|
@ -77,7 +77,7 @@ class TestMsHybridDSL(Cell):
|
|||
return self.program(x, y)
|
||||
|
||||
|
||||
def ms_hybrid_cast_with_infer():
|
||||
def ms_kernel_cast_with_infer():
|
||||
"""
|
||||
test case Custom Op with functions written in Hybrid DSL and infer functions
|
||||
"""
|
||||
|
|
@ -93,7 +93,7 @@ def ms_hybrid_cast_with_infer():
|
|||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
||||
|
||||
|
||||
def ms_hybrid_cast_without_infer():
|
||||
def ms_kernel_cast_without_infer():
|
||||
"""
|
||||
test case Custom Op with functions written in Hybrid DSL and without infer functions
|
||||
"""
|
||||
|
|
@ -109,7 +109,7 @@ def ms_hybrid_cast_without_infer():
|
|||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
||||
|
||||
|
||||
def ms_hybrid_cast_pyfunc():
|
||||
def ms_kernel_cast_pyfunc():
|
||||
"""
|
||||
test case Custom Op with functions written in Hybrid DSL and func_type pyfunc
|
||||
"""
|
||||
|
|
@ -125,7 +125,7 @@ def ms_hybrid_cast_pyfunc():
|
|||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
||||
|
||||
|
||||
def ms_hybrid_allocate():
|
||||
def ms_kernel_allocate():
|
||||
"""
|
||||
test case Custom Op with functions written in Hybrid DSL about math functions and allocate
|
||||
"""
|
||||
|
|
@ -141,7 +141,7 @@ def ms_hybrid_allocate():
|
|||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
||||
|
||||
|
||||
def ms_hybrid_allocate_cpu():
|
||||
def ms_kernel_allocate_cpu():
|
||||
"""
|
||||
test case Custom Op with functions written in Hybrid DSL about math functions and allocate
|
||||
for cpu, we test fp32 to avoid env diff in support of data types.
|
||||
|
|
@ -158,7 +158,7 @@ def ms_hybrid_allocate_cpu():
|
|||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
||||
|
||||
|
||||
def ms_hybrid_grid():
|
||||
def ms_kernel_grid():
|
||||
"""
|
||||
test case Custom Op with functions written in Hybrid DSL about grid
|
||||
"""
|
||||
|
|
@ -174,7 +174,7 @@ def ms_hybrid_grid():
|
|||
raise ValueError("Precision error, compare result: {}".format(compare_res))
|
||||
|
||||
|
||||
def ms_hybrid_grid_cpu():
|
||||
def ms_kernel_grid_cpu():
|
||||
"""
|
||||
test case Custom Op with functions written in Hybrid DSL about grid
|
||||
"""
|
||||
|
|
@ -194,79 +194,79 @@ def ms_hybrid_grid_cpu():
|
|||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_ms_hybrid_ascend_graph_mode():
|
||||
def test_ms_kernel_ascend_graph_mode():
|
||||
"""
|
||||
Feature: test case for Custom op with func_type="ms_hybrid"
|
||||
Description: ascend test case, Python DSL with ms_hybrid decorator in GRAPH_MODE.
|
||||
Feature: test case for Custom op with func_type="ms_kernel"
|
||||
Description: ascend test case, Python DSL with ms_kernel decorator in GRAPH_MODE.
|
||||
Expectation: the result match with numpy result
|
||||
"""
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
|
||||
ms_hybrid_cast_pyfunc()
|
||||
ms_hybrid_cast_with_infer()
|
||||
ms_hybrid_cast_without_infer()
|
||||
ms_hybrid_allocate()
|
||||
ms_hybrid_grid()
|
||||
ms_kernel_cast_pyfunc()
|
||||
ms_kernel_cast_with_infer()
|
||||
ms_kernel_cast_without_infer()
|
||||
ms_kernel_allocate()
|
||||
ms_kernel_grid()
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_ms_hybrid_ascend_pynative_mode():
|
||||
def test_ms_kernel_ascend_pynative_mode():
|
||||
"""
|
||||
Feature: test case for Custom op with func_type="ms_hybrid"
|
||||
Description: ascend test case, Python DSL with ms_hybrid decorator in PYNATIVE_MODE.
|
||||
Feature: test case for Custom op with func_type="ms_kernel"
|
||||
Description: ascend test case, Python DSL with ms_kernel decorator in PYNATIVE_MODE.
|
||||
Expectation: the result match with numpy result
|
||||
"""
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="Ascend")
|
||||
ms_hybrid_cast_pyfunc()
|
||||
ms_hybrid_cast_with_infer()
|
||||
ms_hybrid_cast_without_infer()
|
||||
ms_hybrid_allocate()
|
||||
ms_hybrid_grid()
|
||||
ms_kernel_cast_pyfunc()
|
||||
ms_kernel_cast_with_infer()
|
||||
ms_kernel_cast_without_infer()
|
||||
ms_kernel_allocate()
|
||||
ms_kernel_grid()
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_ms_hybrid_gpu_graph_mode():
|
||||
def test_ms_kernel_gpu_graph_mode():
|
||||
"""
|
||||
Feature: test case for Custom op with func_type="ms_hybrid"
|
||||
Description: gpu test case, Python DSL with ms_hybrid decorator in GRAPH_MODE.
|
||||
Feature: test case for Custom op with func_type="ms_kernel"
|
||||
Description: gpu test case, Python DSL with ms_kernel decorator in GRAPH_MODE.
|
||||
Expectation: the result match with numpy result
|
||||
"""
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
|
||||
ms_hybrid_cast_pyfunc()
|
||||
ms_hybrid_cast_with_infer()
|
||||
ms_hybrid_cast_without_infer()
|
||||
ms_hybrid_allocate()
|
||||
ms_hybrid_grid()
|
||||
ms_kernel_cast_pyfunc()
|
||||
ms_kernel_cast_with_infer()
|
||||
ms_kernel_cast_without_infer()
|
||||
ms_kernel_allocate()
|
||||
ms_kernel_grid()
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
def test_ms_hybrid_gpu_pynative_mode():
|
||||
def test_ms_kernel_gpu_pynative_mode():
|
||||
"""
|
||||
Feature: test case for Custom op with func_type="ms_hybrid"
|
||||
Description: gpu test case, Python DSL with ms_hybrid decorator in PYNATIVE_MODE.
|
||||
Feature: test case for Custom op with func_type="ms_kernel"
|
||||
Description: gpu test case, Python DSL with ms_kernel decorator in PYNATIVE_MODE.
|
||||
Expectation: the result match with numpy result
|
||||
"""
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
|
||||
ms_hybrid_cast_pyfunc()
|
||||
ms_hybrid_cast_with_infer()
|
||||
ms_hybrid_cast_without_infer()
|
||||
ms_hybrid_allocate()
|
||||
ms_hybrid_grid()
|
||||
ms_kernel_cast_pyfunc()
|
||||
ms_kernel_cast_with_infer()
|
||||
ms_kernel_cast_without_infer()
|
||||
ms_kernel_allocate()
|
||||
ms_kernel_grid()
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_cpu
|
||||
@pytest.mark.env_onecard
|
||||
def test_ms_hybrid_cpu_graph_mode():
|
||||
def test_ms_kernel_cpu_graph_mode():
|
||||
"""
|
||||
Feature: test case for Custom op with func_type="ms_hybrid"
|
||||
Description: cpu test case, Python DSL with ms_hybrid decorator in GRAPH_MODE.
|
||||
Feature: test case for Custom op with func_type="ms_kernel"
|
||||
Description: cpu test case, Python DSL with ms_kernel decorator in GRAPH_MODE.
|
||||
Expectation: the result match with numpy result
|
||||
"""
|
||||
if platform.system().lower() in {"windows", "darwin"}:
|
||||
|
|
@ -274,22 +274,22 @@ def test_ms_hybrid_cpu_graph_mode():
|
|||
pass
|
||||
else:
|
||||
context.set_context(mode=context.GRAPH_MODE, device_target="CPU")
|
||||
ms_hybrid_allocate_cpu()
|
||||
ms_hybrid_grid_cpu()
|
||||
ms_kernel_allocate_cpu()
|
||||
ms_kernel_grid_cpu()
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_cpu
|
||||
@pytest.mark.env_onecard
|
||||
def test_ms_hybrid_cpu_pynative_mode():
|
||||
def test_ms_kernel_cpu_pynative_mode():
|
||||
"""
|
||||
Feature: test case for Custom op with func_type="ms_hybrid"
|
||||
Description: cpu test case, Python DSL with ms_hybrid decorator in PYNATIVE_MODE.
|
||||
Feature: test case for Custom op with func_type="ms_kernel"
|
||||
Description: cpu test case, Python DSL with ms_kernel decorator in PYNATIVE_MODE.
|
||||
Expectation: the result match with numpy result
|
||||
"""
|
||||
if platform.system().lower() in {"windows", "darwin"}:
|
||||
pass
|
||||
else:
|
||||
context.set_context(mode=context.PYNATIVE_MODE, device_target="CPU")
|
||||
ms_hybrid_allocate_cpu()
|
||||
ms_hybrid_grid_cpu()
|
||||
ms_kernel_allocate_cpu()
|
||||
ms_kernel_grid_cpu()
|
||||
Loading…
Reference in New Issue