forked from mindspore-Ecosystem/mindspore
[GraphKernel] fix bert and add graph kernel ops.
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e6e544dbc4
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0271535429
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@ -38,6 +38,7 @@
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"mindspore/model_zoo/official/cv" "c-extension-no-member"
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"mindspore/model_zoo/official/nlp/bert_thor/src/bert_model.py" "redefined-outer-name"
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"mindspore/mindspore/_extends/parallel_compile/akg_compiler/akg_process.py" "Catching too general exception BaseException"
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"mindspore/mindspore/_extends/graph_kernel/model/model.py" "super-on-old-class"
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# MindData
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"mindspore/mindspore/dataset/__init__.py" "redefined-builtin"
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2
akg
2
akg
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@ -1 +1 @@
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Subproject commit 15b59fb739944c1903558659a39b34bb632de448
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Subproject commit 8902440c825f90846a5b0fe5c1644d450dbab631
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@ -51,6 +51,7 @@ from .sigmoid import Sigmoid
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from .sigmoid_cross_entropy_with_logits import SigmoidCrossEntropyWithLogits
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from .sigmoid_cross_entropy_with_logits_grad import SigmoidCrossEntropyWithLogitsGrad
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from .sigmoid_grad import SigmoidGrad
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from .slice import Slice
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from .softmax import Softmax
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from .softmax_cross_entropy_with_logits import SoftmaxCrossEntropyWithLogits
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from .softmax_grad_ext import SoftmaxGradExt
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@ -0,0 +1,35 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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"""generate json desc for slice"""
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from ._utils import Expander, ExpanderInfoValidator as VLD
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@VLD.check_attrs('begin', 'size')
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class Slice(Expander):
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"""Slice expander"""
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def _expand(self, graph_builder):
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input_x = self.inputs[0]
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begin = self.attrs['begin']
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size = self.attrs['size']
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end = []
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strides = []
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for i in range(len(begin)):
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strides.append(1)
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end.append(begin[i] + size[i])
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output = graph_builder.tensor(size, input_x.dtype, input_x.data_format)
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graph_builder.op('StridedSlice', output, [input_x], attrs={'begin': begin, 'end': end, 'strides': strides})
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return output
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@ -804,6 +804,16 @@ class GraphSplitGpu(GraphSplitByPattern):
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fused.append(a)
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return fused, True
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def _strided_slice(dom):
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if dom.dom_op().prim != "StridedSlice":
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return None
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fused = []
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for a, _ in dom.in_relations.items():
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if a.pattern <= PrimLib.BROADCAST and a.check_acyclic(dom) and \
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len(a.out_relations) == 1 and not a.is_output:
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fused.append(a)
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return fused, True
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def _fuse_loop():
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changed = True
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while changed:
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@ -814,6 +824,7 @@ class GraphSplitGpu(GraphSplitByPattern):
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changed = self.fuse(_reduce_width) or changed
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changed = self.fuse(_broadcast_depth) or changed
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changed = self.fuse(_broadcast_width) or changed
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changed = self.fuse(_strided_slice) or changed
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if use_poly_reduce:
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changed = self.fuse(_reduce_output) or changed
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if enable_stitch_fusion:
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@ -216,6 +216,7 @@ class PrimLib:
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'Transpose': Prim(OPAQUE),
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'Tile': Prim(BROADCAST),
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'BroadcastTo': Prim(BROADCAST),
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'StridedSlice': Prim(OPAQUE),
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'MatMul': Prim(OPAQUE),
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'TransData': Prim(OPAQUE),
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'BatchMatMul': Prim(OPAQUE),
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@ -99,6 +99,7 @@ std::vector<PrimitivePtr> GetClusterableOpList() {
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prim::kPrimSelect,
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prim::kPrimSign,
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prim::kPrimSin,
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prim::kPrimStridedSlice,
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#endif
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};
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const auto &flags = context::GraphKernelFlags::GetInstance();
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@ -82,6 +82,7 @@ std::vector<PrimitivePtr> GetExpandOps() {
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prim::kPrimSigmoidGrad,
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prim::kPrimSigmoidCrossEntropyWithLogits,
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prim::kPrimSigmoidCrossEntropyWithLogitsGrad,
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prim::kPrimSlice,
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prim::kPrimSoftmax,
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prim::kPrimSoftmaxCrossEntropyWithLogits,
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prim::kPrimSquaredDifference,
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@ -0,0 +1,55 @@
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# Copyright 2021 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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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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import numpy as np
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import pytest
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import mindspore.context as context
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore.ops import operations as P
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class Net(nn.Cell):
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def __init__(self):
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super(Net, self).__init__()
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self.slice = P.Slice()
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def construct(self, x, begin, size):
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return self.slice(x, begin, size)
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def get_output(x, begin, size, enable_graph_kernel=False):
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context.set_context(enable_graph_kernel=enable_graph_kernel)
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net = Net()
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output = net(x, begin, size)
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return output
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def test_slice():
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in1 = np.array([[[1, -1, 1], [2, -2, 2]], [[3, -3, 3], [4, -4, 4]], [[5, -5, 5], [6, -6, 6]]]).astype(np.float32)
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x1 = Tensor(in1)
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begin1 = (0, 1, 0)
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size1 = (2, 1, 3)
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expect = get_output(x1, begin1, size1, False)
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output = get_output(x1, begin1, size1, True)
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assert np.allclose(expect.asnumpy(), output.asnumpy(), 0.0001, 0.0001)
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@pytest.mark.level0
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@pytest.mark.platform_x86_gpu_training
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@pytest.mark.env_onecard
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def test_slice_gpu():
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context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
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test_slice()
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