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replace matmul/batchmatmul by mul when k is 1

This commit is contained in:
lingyunli63 2021-06-02 15:26:48 +08:00
parent 76be4aff12
commit 4f34e537a0
5 changed files with 220 additions and 12 deletions
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1
mindspore/_extends/graph_kernel/expanders/__init__.py
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@ -53,3 +53,4 @@ from .softmax_grad_ext import SoftmaxGradExt
from .square_sum_v1 import SquareSumV1
from .fused_mul_add import FusedMulAdd
from .conv2d import Conv2D
from .matmul import MatMul, BatchMatMul

74
mindspore/_extends/graph_kernel/expanders/matmul.py Normal file
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@ -0,0 +1,74 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ===========================================================================
"""generate json desc for BatchMatMul and MatMul"""
from mindspore._extends.graph_kernel.model.model import DataFormat as DF
from mindspore._extends.graph_kernel.model.model import GraphKernelUnsupportedException as GKException
from ._utils import Expander, ExpanderInfoValidator as VLD
@VLD.check_attrs('transpose_a', 'transpose_b', 'left_format', 'right_format')
class MatMul(Expander):
"""
MatMul expander
"""
def __init__(self, expand_info):
super().__init__(expand_info)
self.transpose_a = self.attrs['transpose_a']
self.transpose_b = self.attrs['transpose_b']
self.left_format = self.attrs['left_format']
self.right_format = self.attrs['right_format']
self.shape_a = self.inputs[0]['shape']
self.shape_b = self.inputs[1]['shape']
def _optimize_to_mul(self):
"""check if matmul can be replace by mul"""
if self.left_format != DF.DEFAULT or self.right_format != DF.DEFAULT:
return False
k_a = self.shape_a[-2] if self.transpose_a else self.shape_a[-1]
k_b = self.shape_b[-1] if self.transpose_b else self.shape_b[-2]
if k_a != 1 or k_b != 1:
return False
return True
def _check(self):
input_num = len(self.inputs)
if input_num < 2:
raise GKException("matul inputs number should bigger than 1, but got {}.".format(input_num))
def _trans_shape(self, shape):
trans_shape = list(shape)
trans_shape[-2] = shape[-1]
trans_shape[-1] = shape[-2]
return trans_shape
def _expand(self, graph_builder):
if not self._optimize_to_mul():
raise GKException("MatMul/BatchMatMul do not need to be replaced by Mul")
#Matmul is replaced by Mul([b m k], [b k n]) when k==1
input_a = self.inputs[0]
input_b = self.inputs[1]
if self.transpose_a:
shape_a_trans = self._trans_shape(self.shape_a)
input_a = graph_builder.emit('Reshape', [input_a], attrs={'shape': shape_a_trans})
if self.transpose_b:
shape_b_trans = self._trans_shape(self.shape_b)
input_b = graph_builder.emit('Reshape', [input_b], attrs={'shape': shape_b_trans})
result = graph_builder.emit('Mul', [input_a, input_b])
if 'dst_type' in self.attrs and self.inputs[0].dtype != self.attrs['dst_type']:
result = graph_builder.emit('Cast', [result], attrs={'dst_type': self.attrs['dst_type']})
return result
class BatchMatMul(MatMul):
"""BatchMatMul expander"""

64
mindspore/_extends/graph_kernel/model/op_infer.py
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@ -16,7 +16,6 @@
import copy
import sys
from functools import reduce
from .model import GraphKernelUnsupportedException as GKException
from .model import PrimLib, DataFormat as DF
@ -102,19 +101,60 @@ class OpInfer:
class _Elemwise(OpInfer):
"""Common infer for elementwise operators"""
def _infer_shape(self):
"""returns the input shape with largest flatten size"""
shape = (1,)
max_flatten_size = 1
for t in self.inputs:
if t.data_format != DF.DEFAULT:
return t.shape
flatten_size = reduce(lambda x, y: x * y, t.shape)
if flatten_size > max_flatten_size or (flatten_size == max_flatten_size and len(t.shape) > len(shape)):
max_flatten_size = flatten_size
shape = t.shape
def _broadcast_shape(self, shapes):
"""deduce broadcast shape using same rules as numpy"""
dim_size = max([len(shape) for shape in shapes])
align_shapes = [[1] * (dim_size - len(shape)) + shape for shape in shapes]
out_shape = [1] * dim_size
for i in range(dim_size):
for align_shape in align_shapes:
if align_shape[i] > 1:
if out_shape[i] == 1:
out_shape[i] = align_shape[i]
if out_shape[i] != align_shape[i]:
raise GKException("shape broadcast failed!")
return out_shape
def _to_nz(self, default_shape):
"""default format shape to fractal_Nz format shape"""
if len(default_shape) not in (1, 2):
raise GKException("shape is too long!")
# (32) or (1, 32) -> (2, 1, 1, 16)
if len(default_shape) == 1 or (len(default_shape) == 2 and default_shape[0] == 1):
shape = [default_shape[-1] // 16, 1, 1, 16]
if default_shape[-1] % 16 != 0:
raise GKException("should be multiplies of 16")
return shape
#(32, 1) -> (1, 2, 16, 1)
if len(default_shape) == 2 and default_shape[1] == 1:
shape = [1, default_shape[0] // 16, 16, 1]
if default_shape[0] % 16 != 0:
raise GKException("should be multiples of 16")
return shape
#(32, 48) -> (3, 2, 16, 16)
shape = [default_shape[1] // 16, default_shape[0] // 16, 16, 16]
if default_shape[0] % 16 != 0 or defautl_shape[1] % 16 != 0:
raise GKException("should be multiples of 16")
return shape
def _infer_shape(self):
"""returns the output shape with broadcast"""
# in case all inputs are default format/NHWC/NCHW
is_default = [input.data_format in (DF.DEFAULT, DF.NHWC, DF.NCHW) for input in self.inputs]
if all(is_default):
return self._broadcast_shape([input.shape for input in self.inputs])
# in case formats are fractal_nz, default_fromat/NHWC/HCHW(optional)
is_default_frac_nz = [input.data_format in (DF.DEFAULT, DF.NHWC, DF.NCHW, DF.FRAC_NZ) \
for input in self.inputs]
if all(is_default_frac_nz):
nz_shapes = [self._to_nz(input.shape) if input.data_format != DF.FRAC_NZ else input.shape \
for input in self.inputs]
return self._broadcast_shape(nz_shapes)
raise GKException("Only support default and fractal_nz")
def _infer_format(self):
for tensor in self.inputs:
if tensor.data_format != DF.DEFAULT:

2
mindspore/ccsrc/backend/optimizer/graph_kernel/graph_kernel_expander.cc
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@ -56,6 +56,8 @@ std::vector<PrimitivePtr> GetExpandOps() {
prim::kPrimLogSoftmax,
prim::kPrimLogSoftmaxGrad,
prim::kPrimTile,
prim::kPrimMatMul,
prim::kPrimBatchMatMul,
#if ENABLE_D
prim::kPrimSqrtGrad,
prim::kPrimClipByNormNoDivSum,

91
tests/st/ops/graph_kernel/test_matmul_to_mul.py Normal file
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@ -0,0 +1,91 @@
# Copyright 2021 Huawei Technologies Co., Ltd
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
import pytest
import mindspore.context as context
from mindspore import Tensor
from mindspore.nn import Cell
import mindspore.ops.operations as P
class Net(Cell):
def __init__(self):
super(Net, self).__init__()
self.matmul = P.MatMul(transpose_a=False, transpose_b=False)
def construct(self, x, y):
return self.matmul(x, y)
class Net1(Cell):
def __init__(self):
super(Net1, self).__init__()
self.bmm = P.BatchMatMul(transpose_a=False, transpose_b=False)
def construct(self, x, y):
return self.bmm(x, y)
def get_output(i0, i1, net_cls, enable_graph_kernel=False):
context.set_context(enable_graph_kernel=enable_graph_kernel)
net = net_cls()
output = net(i0, i1)
return output
def test_matmul():
i0 = Tensor(np.random.normal(1, 0.01, [96, 1]).astype(np.float32))
i1 = Tensor(np.random.normal(1, 0.01, [1, 128]).astype(np.float32))
expect = get_output(i0, i1, Net, False)
output = get_output(i0, i1, Net, True)
expect_np = expect.asnumpy().copy()
output_np = output.asnumpy().copy()
assert np.allclose(expect_np, output_np, 1.e-4, 1.e-7)
def test_batchmatmul():
i0 = Tensor(np.random.normal(1, 0.01, [16, 96, 1]).astype(np.float32))
i1 = Tensor(np.random.normal(1, 0.01, [16, 1, 128]).astype(np.float32))
expect = get_output(i0, i1, Net1, False)
output = get_output(i0, i1, Net1, True)
expect_np = expect.asnumpy().copy()
output_np = output.asnumpy().copy()
assert np.allclose(expect_np, output_np, 6.e-4, 6.e-4)
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_matmul_ascend():
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
test_matmul()
@pytest.mark.level0
@pytest.mark.platform_arm_ascend_training
@pytest.mark.platform_x86_ascend_training
@pytest.mark.env_onecard
def test_batchmatmul_ascend():
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
test_batchmatmul()
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_matmul_gpu():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
test_matmul()
@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_batchmatmul_gpu():
context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
test_batchmatmul()