From 7821759d48f7b09b33303478d3829b124768afe3 Mon Sep 17 00:00:00 2001 From: "joanna.wozna.intel" Date: Fri, 6 Nov 2020 03:11:26 +0100 Subject: [PATCH] Add bfloat16 softmax and gelu (#28394) * Add bfloat16 softmax and gelu * Add pass attr bfloat16_enabled_op_types * Changes from review --- .../framework/ir/graph_pattern_detector.cc | 5 +- .../cpu_bfloat16_placement_pass_tester.cc | 8 +- .../inference/analysis/ir_pass_manager.cc | 4 + paddle/fluid/operators/gelu_op.cc | 5 + .../operators/mkldnn/activation_mkldnn_op.cc | 25 +++-- .../operators/mkldnn/softmax_mkldnn_op.cc | 3 +- paddle/fluid/operators/softmax_op.cc | 5 + .../mkldnn/test_activation_mkldnn_op.py | 78 +++++++++++++++- .../mkldnn/test_softmax_bf16_mkldnn_op.py | 92 +++++++++++++++++++ tools/static_mode_white_list.py | 1 + 10 files changed, 211 insertions(+), 15 deletions(-) create mode 100644 python/paddle/fluid/tests/unittests/mkldnn/test_softmax_bf16_mkldnn_op.py diff --git a/paddle/fluid/framework/ir/graph_pattern_detector.cc b/paddle/fluid/framework/ir/graph_pattern_detector.cc index 4f1080952a1..5704dd09cf2 100644 --- a/paddle/fluid/framework/ir/graph_pattern_detector.cc +++ b/paddle/fluid/framework/ir/graph_pattern_detector.cc @@ -2101,8 +2101,9 @@ PDNode *patterns::QuantizePlacement::operator()( PDNode *patterns::Bfloat16Placement::operator()( const std::unordered_set &bfloat16_enabled_op_types) { std::unordered_set supported_op_types = - std::unordered_set( - {"concat", "conv2d", "fusion_gru", "reshape2", "transpose2", "sum"}); + std::unordered_set({"concat", "conv2d", "fusion_gru", "gelu", + "reshape2", "softmax", "sum", + "transpose2"}); if (!bfloat16_enabled_op_types.empty()) { supported_op_types = bfloat16_enabled_op_types; } diff --git a/paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass_tester.cc b/paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass_tester.cc index 4ca9724026a..4e3704e510c 100644 --- a/paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass_tester.cc +++ b/paddle/fluid/framework/ir/mkldnn/cpu_bfloat16_placement_pass_tester.cc @@ -33,7 +33,7 @@ void SetOp(ProgramDesc* prog, const std::string& type, const std::string& name, if (type == "conv2d") { op->SetAttr("name", name); op->SetInput("Input", {inputs[0]}); - } else if (type == "relu") { + } else if (type == "gelu") { op->SetInput("X", inputs); } else if (type == "concat") { op->SetAttr("axis", 1); @@ -71,7 +71,7 @@ ProgramDesc BuildProgramDesc() { SetOp(&prog, "concat", "concat1", {"a", "b"}, {"c"}); SetOp(&prog, "conv2d", "conv1", {"c"}, {"f"}); - SetOp(&prog, "relu", "relu1", {"f"}, {"g"}); + SetOp(&prog, "gelu", "gelu1", {"f"}, {"g"}); SetOp(&prog, "pool2d", "pool1", {"g"}, {"h"}); SetOp(&prog, "conv2d", "conv2", {"h"}, {"k"}); SetOp(&prog, "pool2d", "pool2", {"k"}, {"l"}); @@ -126,7 +126,7 @@ void DefaultAttrTest(unsigned expected_bfloat16_data_type_count) { } TEST(Bfloat16PlacementPass, enable_all) { - MainTest({"conv2d", "pool2d", "relu", "concat", "sum"}, 8); + MainTest({"conv2d", "pool2d", "gelu", "concat", "sum"}, 8); } TEST(Bfloat16PlacementPass, enabled_conv_and_pool) { @@ -134,7 +134,7 @@ TEST(Bfloat16PlacementPass, enabled_conv_and_pool) { MainTest({"conv2d", "pool2d"}, 3); } -TEST(Bfloat16PlacementPass, default_attr_value) { DefaultAttrTest(6); } +TEST(Bfloat16PlacementPass, default_attr_value) { DefaultAttrTest(7); } } // namespace ir } // namespace framework diff --git a/paddle/fluid/inference/analysis/ir_pass_manager.cc b/paddle/fluid/inference/analysis/ir_pass_manager.cc index e94590e847c..3566b856912 100644 --- a/paddle/fluid/inference/analysis/ir_pass_manager.cc +++ b/paddle/fluid/inference/analysis/ir_pass_manager.cc @@ -79,6 +79,10 @@ void IRPassManager::CreatePasses(Argument *argument, } else if (pass_name == "cpu_quantize_pass") { pass->Set("quant_var_scales", new VarQuantScale(argument->quant_var_scales())); + } else if (pass_name == "cpu_bfloat16_placement_pass") { + pass->Set("bfloat16_enabled_op_types", + new std::unordered_set( + argument->bfloat16_enabled_op_types())); #endif } else if (pass_name == "tensorrt_subgraph_pass") { pass->Set("workspace_size", new int(argument->tensorrt_workspace_size())); diff --git a/paddle/fluid/operators/gelu_op.cc b/paddle/fluid/operators/gelu_op.cc index c72cabad891..9ca0d30362c 100644 --- a/paddle/fluid/operators/gelu_op.cc +++ b/paddle/fluid/operators/gelu_op.cc @@ -111,6 +111,11 @@ class GeluOpMaker : public framework::OpProtoAndCheckerMaker { AddAttr("use_mkldnn", "(bool, default false) Only used in mkldnn kernel") .SetDefault(false); + AddAttr( + "mkldnn_data_type", + "(string, default \"float32\"). Data type of mkldnn kernel") + .SetDefault("float32") + .InEnum({"float32", "int8", "bfloat16"}); AddAttr("use_cudnn", "(bool, default false) Only used in cudnn kernel, need " "install cudnn") diff --git a/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc index aecf67fc3bb..22954203d6b 100644 --- a/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc @@ -83,14 +83,14 @@ void eltwise_forward(const framework::ExecutionContext &ctx, const auto *x = ctx.Input("X"); auto *y = ctx.Output("Out"); - T alpha = ctx.HasAttr("alpha") ? ctx.Attr("alpha") : 0; - T beta = ctx.HasAttr("beta") ? ctx.Attr("beta") : 0; + float alpha = ctx.HasAttr("alpha") ? ctx.Attr("alpha") : 0; + float beta = ctx.HasAttr("beta") ? ctx.Attr("beta") : 0; // paddle uses beta but mkldnn uses alpha for swish if (algorithm == mkldnn::algorithm::eltwise_swish) { std::swap(alpha, beta); } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) { - alpha = ctx.Attr("threshold"); + alpha = ctx.Attr("threshold"); } PADDLE_ENFORCE( @@ -128,14 +128,14 @@ void eltwise_grad(const framework::ExecutionContext &ctx, const auto *diff_y = ctx.Input(framework::GradVarName("Out")); auto *diff_x = ctx.Output(framework::GradVarName("X")); - T alpha = ctx.HasAttr("alpha") ? ctx.Attr("alpha") : 0; - T beta = ctx.HasAttr("beta") ? ctx.Attr("beta") : 0; + float alpha = ctx.HasAttr("alpha") ? ctx.Attr("alpha") : 0; + float beta = ctx.HasAttr("beta") ? ctx.Attr("beta") : 0; // paddle uses beta but mkldnn uses alpha for swish if (algorithm == mkldnn::algorithm::eltwise_swish) { std::swap(alpha, beta); } else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) { - alpha = ctx.Attr("threshold"); + alpha = ctx.Attr("threshold"); } auto diff_dst_tz = framework::vectorize(diff_y->dims()); @@ -272,11 +272,20 @@ namespace ops = paddle::operators; act_type##_grad, MKLDNN, ::paddle::platform::CPUPlace, \ ops::MKLDNNActivationGradKernel>); +#define REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(act_type, functor, \ + grad_functor) \ + REGISTER_OP_KERNEL( \ + act_type, MKLDNN, ::paddle::platform::CPUPlace, \ + ops::MKLDNNActivationKernel>, \ + ops::MKLDNNActivationKernel>); \ + REGISTER_OP_KERNEL( \ + act_type##_grad, MKLDNN, ::paddle::platform::CPUPlace, \ + ops::MKLDNNActivationGradKernel>); + #define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro) \ __macro(relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \ __macro(relu6, Relu6MKLDNNFunctor, Relu6MKLDNNGradFunctor); \ __macro(leaky_relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \ - __macro(gelu, GeluMKLDNNFunctor, GeluMKLDNNGradFunctor); \ __macro(swish, SwishMKLDNNFunctor, SwishMKLDNNGradFunctor); \ __macro(sigmoid, SigmoidMKLDNNFunctor, SigmoidMKLDNNGradFunctor); \ __macro(tanh, TanhMKLDNNFunctor, TanhMKLDNNGradFunctor); \ @@ -284,3 +293,5 @@ namespace ops = paddle::operators; __macro(abs, AbsMKLDNNFunctor, AbsMKLDNNGradFunctor); FOR_EACH_MKLDNN_KERNEL_FUNCTOR(REGISTER_ACTIVATION_MKLDNN_KERNEL); +REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(gelu, GeluMKLDNNFunctor, + GeluMKLDNNGradFunctor); diff --git a/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc b/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc index 0b159f9dcfa..9d9e1e2d8de 100644 --- a/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc +++ b/paddle/fluid/operators/mkldnn/softmax_mkldnn_op.cc @@ -181,6 +181,7 @@ class SoftmaxMKLDNNGradKernel : public paddle::framework::OpKernel { namespace ops = paddle::operators; REGISTER_OP_KERNEL(softmax, MKLDNN, ::paddle::platform::CPUPlace, - ops::SoftmaxMKLDNNKernel); + ops::SoftmaxMKLDNNKernel, + ops::SoftmaxMKLDNNKernel); REGISTER_OP_KERNEL(softmax_grad, MKLDNN, ::paddle::platform::CPUPlace, ops::SoftmaxMKLDNNGradKernel); diff --git a/paddle/fluid/operators/softmax_op.cc b/paddle/fluid/operators/softmax_op.cc index cf46b4fc3bd..63a27a8ccbf 100644 --- a/paddle/fluid/operators/softmax_op.cc +++ b/paddle/fluid/operators/softmax_op.cc @@ -115,6 +115,11 @@ class SoftmaxOpMaker : public framework::OpProtoAndCheckerMaker { AddAttr("use_mkldnn", "(bool, default false) Only used in mkldnn kernel") .SetDefault(false); + AddAttr( + "mkldnn_data_type", + "(string, default \"float32\"). Data type of mkldnn kernel") + .SetDefault("float32") + .InEnum({"float32", "bfloat16"}); AddAttr("is_test", "(bool, default false) Set to true for inference only, false " "for training. Some layers may run faster when this is true.") diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py index d904bdbfa96..63db1b1475d 100644 --- a/python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_activation_mkldnn_op.py @@ -18,7 +18,7 @@ import unittest import numpy as np from scipy.special import expit import paddle.fluid.core as core -from paddle.fluid.tests.unittests.op_test import OpTest +from paddle.fluid.tests.unittests.op_test import OpTest, convert_float_to_uint16 from paddle.fluid.tests.unittests.test_activation_op import TestActivation, TestRelu, TestTanh, TestSqrt, TestAbs, TestLeakyRelu, TestSwish, TestRelu6, TestSigmoid from paddle.fluid.tests.unittests.test_gelu_op import gelu from mkldnn_op_test import check_if_mkldnn_primitives_exist_in_bwd @@ -79,6 +79,44 @@ class TestMKLDNNGeluDim2Approx(TestActivation): self.attrs = {"use_mkldnn": True, "approximate": True} +class TestMKLDNNGeluBf16Dim2(TestActivation): + def setUp(self): + self.op_type = "gelu" + self.dtype = np.uint16 + + x = np.random.uniform(-1, 1, [11, 17]).astype(np.float32) + out = convert_float_to_uint16(gelu(x, False)) + + self.inputs = {'X': convert_float_to_uint16(x)} + self.outputs = {'Out': out} + self.attrs = {"use_mkldnn": True} + + def test_check_output(self): + self.check_output_with_place(core.CPUPlace()) + + def test_check_grad(self): + pass + + +class TestMKLDNNGeluBf16Dim2Approx(TestActivation): + def setUp(self): + self.op_type = "gelu" + self.dtype = np.uint16 + + x = np.random.uniform(-1, 1, [11, 17]).astype(np.float32) + out = convert_float_to_uint16(gelu(x, True)) + + self.inputs = {'X': convert_float_to_uint16(x)} + self.outputs = {'Out': out} + self.attrs = {"use_mkldnn": True, "approximate": True} + + def test_check_output(self): + self.check_output_with_place(core.CPUPlace()) + + def test_check_grad(self): + pass + + class TestMKLDNNTanhDim2(TestTanh): def setUp(self): super(TestMKLDNNTanhDim2, self).setUp() @@ -187,6 +225,44 @@ class TestMKLDNNGeluDim4Approx(TestActivation): self.attrs = {"use_mkldnn": True, "approximate": True} +class TestMKLDNNGeluBf16Dim4(TestActivation): + def setUp(self): + self.op_type = "gelu" + self.dtype = np.uint16 + + x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype(np.float32) + out = convert_float_to_uint16(gelu(x, False)) + + self.inputs = {'X': convert_float_to_uint16(x)} + self.outputs = {'Out': out} + self.attrs = {"use_mkldnn": True} + + def test_check_output(self): + self.check_output_with_place(core.CPUPlace()) + + def test_check_grad(self): + pass + + +class TestMKLDNNGeluBf16Dim4Approx(TestActivation): + def setUp(self): + self.op_type = "gelu" + self.dtype = np.uint16 + + x = np.random.uniform(-1, 1, [2, 4, 3, 5]).astype(np.float32) + out = convert_float_to_uint16(gelu(x, True)) + + self.inputs = {'X': convert_float_to_uint16(x)} + self.outputs = {'Out': out} + self.attrs = {"use_mkldnn": True, "approximate": True} + + def test_check_output(self): + self.check_output_with_place(core.CPUPlace()) + + def test_check_grad(self): + pass + + class TestMKLDNNTanhDim4(TestTanh): def setUp(self): super(TestMKLDNNTanhDim4, self).setUp() diff --git a/python/paddle/fluid/tests/unittests/mkldnn/test_softmax_bf16_mkldnn_op.py b/python/paddle/fluid/tests/unittests/mkldnn/test_softmax_bf16_mkldnn_op.py new file mode 100644 index 00000000000..5ba944c3b98 --- /dev/null +++ b/python/paddle/fluid/tests/unittests/mkldnn/test_softmax_bf16_mkldnn_op.py @@ -0,0 +1,92 @@ +# 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 +import numpy as np +from paddle.fluid.tests.unittests.op_test import convert_float_to_uint16 +import paddle.fluid.core as core +from paddle.fluid.tests.unittests.test_softmax_op import TestSoftmaxOp, TestSoftmaxOp2, TestSoftmaxOp3, TestSoftmaxOp4, TestSoftmaxOp5, TestSoftmaxOp6 +from paddle import enable_static + + +def stable_softmax(x): + """Compute the softmax of vector x in a numerically stable way.""" + shiftx = x - np.max(x).clip(-64.) + exps = np.exp(shiftx) + return exps / np.sum(exps) + + +class TestSoftmaxMKLDNNOp(TestSoftmaxOp): + def get_x_shape(self): + return [10, 10] + + def get_axis(self): + return -1 + + def setUp(self): + self.op_type = "softmax" + self.use_mkldnn = True + self.dtype = np.uint16 + self.init_kernel_type() + self.shape = self.get_x_shape() + self.axis = self.get_axis() + + x = np.random.uniform(0.1, 1, self.shape).astype(np.float) + out = convert_float_to_uint16( + np.apply_along_axis(stable_softmax, self.axis, x)) + + self.inputs = {'X': convert_float_to_uint16(x)} + self.outputs = {'Out': out} + self.attrs = {'axis': self.axis, 'use_mkldnn': self.use_mkldnn} + + def test_check_output(self): + self.check_output_with_place(core.CPUPlace()) + + def test_check_grad(self): + pass + + def init_kernel_type(self): + self.use_mkldnn = True + + +class TestSoftmaxMKLDNNOp2(TestSoftmaxOp2): + def init_kernel_type(self): + self.use_mkldnn = True + + +class TestSoftmaxMKLDNNOp3(TestSoftmaxOp3): + def init_kernel_type(self): + self.use_mkldnn = True + + +class TestSoftmaxMKLDNNOp4(TestSoftmaxOp4): + def init_kernel_type(self): + self.use_mkldnn = True + + +class TestSoftmaxMKLDNNOp5(TestSoftmaxOp5): + def init_kernel_type(self): + self.use_mkldnn = True + + +class TestSoftmaxMKLDNNOp6(TestSoftmaxOp6): + def init_kernel_type(self): + self.use_mkldnn = True + + +if __name__ == '__main__': + enable_static() + unittest.main() diff --git a/tools/static_mode_white_list.py b/tools/static_mode_white_list.py index 77e7372290d..6a2a121cd61 100644 --- a/tools/static_mode_white_list.py +++ b/tools/static_mode_white_list.py @@ -601,6 +601,7 @@ STATIC_MODE_TESTING_LIST = [ 'test_quantize_mkldnn_op', 'test_requantize_mkldnn_op', 'test_softmax_mkldnn_op', + 'test_softmax_bf16_mkldnn_op', 'test_sum_mkldnn_op', 'test_sum_bf16_mkldnn_op', 'test_transpose_int8_mkldnn_op', -- GitLab