[DNNL][INT8][FP32] MatMul (#23395)
* Initial FP32 DNNL MatMul Implementation * Implement int8 DNNL MatMul * Unify in-kernel-naming, clean UTs * MatmuL: Introduce op caching * Final adjustments test=develop * Remove dy_graph disablement test=develop * Change dnnl header name to new one test=develop * Contrain multi head check to prevent fails test=develop * Resolve dnnl header problems on MAC CI * Variable namings to kernel and skip_grad_ci added test=develop * Prevent MAC CI from failing * Prevent windows build from failing test=develop * Modify UTs to conform to the rules * Modify MatMul aux functions namings test=developrevert-23830-2.0-beta
Michał Gallus
6 years ago
committed by
GitHub
parent
a2315d37cf
commit
a63bcf9ae7
File diff suppressed because it is too large
Load Diff
@ -0,0 +1,165 @@
|
||||
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import unittest, os
|
||||
import numpy as np
|
||||
from paddle.fluid.tests.unittests.op_test import OpTest, skip_check_grad_ci
|
||||
|
||||
|
||||
@skip_check_grad_ci(reason="DNNL's MatMul doesn't implemend grad kernel.")
|
||||
class TestDnnlMatMulOp(OpTest):
|
||||
def generate_data(self):
|
||||
self.x = np.random.random((25, 2, 2)).astype("float32")
|
||||
self.y = np.random.random((25, 2, 2)).astype("float32")
|
||||
self.alpha = 1.0
|
||||
self.out = self.alpha * np.matmul(self.x, self.y)
|
||||
|
||||
def set_attributes(self):
|
||||
self.alpha = self.alpha if hasattr(self, 'alpha') else 1.0
|
||||
self.attrs = {'alpha': self.alpha}
|
||||
|
||||
def setUp(self):
|
||||
# Set max isa, otherwise fails on SKX and earlier
|
||||
os.environ["DNNL_MAX_CPU_ISA"] = "AVX"
|
||||
self.op_type = "matmul"
|
||||
self._cpu_only = True
|
||||
self.use_mkldnn = True
|
||||
self.generate_data()
|
||||
self.set_attributes()
|
||||
self.attrs['use_mkldnn'] = True
|
||||
|
||||
self.inputs = {'X': self.x, 'Y': self.y}
|
||||
self.outputs = {'Out': self.out}
|
||||
|
||||
def test_check_output(self):
|
||||
self.check_output()
|
||||
|
||||
|
||||
class TestDnnlMatMulOpAlpha(TestDnnlMatMulOp):
|
||||
def generate_data(self):
|
||||
self.x = np.random.random((17, 2, 3)).astype("float32")
|
||||
self.y = np.random.random((17, 3, 2)).astype("float32")
|
||||
self.alpha = 2.0
|
||||
self.out = self.alpha * np.matmul(self.x, self.y)
|
||||
|
||||
|
||||
class TestDnnlMatMulOp2D(TestDnnlMatMulOp):
|
||||
def print_tensor(self, name, tensor):
|
||||
print(name)
|
||||
print(tensor)
|
||||
|
||||
def generate_data(self):
|
||||
self.x = np.random.random((12, 9)).astype("float32")
|
||||
self.y = np.random.random((9, 12)).astype("float32")
|
||||
self.out = np.matmul(self.x, self.y)
|
||||
|
||||
|
||||
class TestDnnlMatMulOpTransposeX(TestDnnlMatMulOp):
|
||||
def generate_data(self):
|
||||
self.x = np.random.random((12, 9)).astype("float32")
|
||||
self.y = np.random.random((12, 9)).astype("float32")
|
||||
self.out = np.matmul(np.transpose(self.x), self.y)
|
||||
|
||||
def set_attributes(self):
|
||||
self.attrs = {'transpose_X': True}
|
||||
|
||||
|
||||
class TestDnnlMatMulOpTransposeY(TestDnnlMatMulOp):
|
||||
def generate_data(self):
|
||||
self.x = np.random.random((12, 9)).astype("float32")
|
||||
self.y = np.random.random((12, 9)).astype("float32")
|
||||
self.out = np.matmul(self.x, np.transpose(self.y))
|
||||
|
||||
def set_attributes(self):
|
||||
self.attrs = {'transpose_Y': True}
|
||||
|
||||
|
||||
class TestDnnlMatMulOpTransposeY3D(TestDnnlMatMulOp):
|
||||
def generate_data(self):
|
||||
self.x = np.random.random((17, 3, 2)).astype("float32")
|
||||
self.y = np.random.random((17, 3, 2)).astype("float32")
|
||||
self.out = np.matmul(self.x, np.transpose(self.y, (0, 2, 1)))
|
||||
|
||||
def set_attributes(self):
|
||||
self.attrs = {'transpose_Y': True}
|
||||
|
||||
|
||||
class TestDnnlMatMulOpInt8NoScales(TestDnnlMatMulOp):
|
||||
def generate_data(self):
|
||||
self.x = np.random.random((12, 9)).astype("int8")
|
||||
self.y = np.random.random((9, 12)).astype("int8")
|
||||
self.out = np.matmul(self.x, self.y)
|
||||
|
||||
|
||||
class TestDnnlMatMulOpInt8(TestDnnlMatMulOp):
|
||||
def quantize(self, tensor):
|
||||
scale = 127. / np.abs(np.amax(tensor))
|
||||
quantized = np.round(scale * tensor).astype("int8")
|
||||
return scale, quantized
|
||||
|
||||
def generate_data(self):
|
||||
x_float = np.random.random((12, 9)).astype("float32")
|
||||
self.x_scale, self.x = self.quantize(x_float)
|
||||
|
||||
y_float = np.random.random((9, 12)).astype("float32")
|
||||
self.y_scale, self.y = self.quantize(y_float)
|
||||
|
||||
out_float = np.matmul(x_float, y_float)
|
||||
self.out_scale, self.out = self.quantize(out_float)
|
||||
|
||||
def set_attributes(self):
|
||||
self.attrs = {
|
||||
'Scale_x': self.x_scale,
|
||||
'Scale_y': self.y_scale,
|
||||
'Scale_out': self.out_scale,
|
||||
}
|
||||
|
||||
def test_check_output(self):
|
||||
int_atol = 1
|
||||
self.check_output(atol=int_atol)
|
||||
|
||||
|
||||
class TestDnnlMatMulOpInt8ForceFP32(TestDnnlMatMulOpInt8):
|
||||
def generate_data(self):
|
||||
x_float = np.random.random((12, 9)).astype("float32")
|
||||
self.x_scale, self.x = self.quantize(x_float)
|
||||
|
||||
y_float = np.random.random((9, 12)).astype("float32")
|
||||
self.y_scale, self.y = self.quantize(y_float)
|
||||
|
||||
out_float = np.matmul(x_float, y_float)
|
||||
self.out = out_float
|
||||
|
||||
def set_attributes(self):
|
||||
self.attrs = {
|
||||
'Scale_x': self.x_scale,
|
||||
'Scale_y': self.y_scale,
|
||||
'force_fp32_output': True
|
||||
}
|
||||
|
||||
|
||||
class TestDnnlMatMulOpInt8ForceFP32BasicScales(TestDnnlMatMulOp):
|
||||
def generate_data(self):
|
||||
self.x = np.random.randint(0, 3, (12, 9)).astype("int8")
|
||||
self.y = np.random.randint(0, 3, (9, 12)).astype("int8")
|
||||
self.out = np.matmul(self.x, self.y).astype("float32")
|
||||
|
||||
def set_attributes(self):
|
||||
self.attrs = {'force_fp32_output': True}
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue