diff --git a/paddle/fluid/platform/device/ipu/popart_canonicalization/loss_ops.cc b/paddle/fluid/platform/device/ipu/popart_canonicalization/loss_ops.cc index 035b15b2770a7..48962456d4ca7 100644 --- a/paddle/fluid/platform/device/ipu/popart_canonicalization/loss_ops.cc +++ b/paddle/fluid/platform/device/ipu/popart_canonicalization/loss_ops.cc @@ -278,6 +278,123 @@ Node *kldiv_loss_handler(Graph *graph, Node *node) { return loss; } +Node *sigmoid_cross_entropy_with_logits_handler(Graph *graph, Node *node) { + // Out = max(logits, 0) - logits * label + log(1 + exp(-abs(logits))) + auto *op = node->Op(); + int reduction = 2; + if (is_dynamic_graph()) { + reduction = RemoveTailReduction(graph, node, "Out"); + } + bool append_identity_loss = + is_dynamic_graph() && IsLastVarNode(GetOutputVarNode("Out", node)); + + auto logits = GetInputVarNode("X", node); + auto label = GetInputVarNode("Label", node); + // sigmoid_cross_entropy_with_logits uses float label as input. + auto ignore_index_value = + static_cast(PADDLE_GET_CONST(int, op->GetAttr("ignore_index"))); + auto normalize = PADDLE_GET_CONST(bool, op->GetAttr("normalize")); + + // const + auto one = CreateConst( + graph, node, std::vector{1.0}, {1}, GetVarDType(logits)) + ->outputs.front(); + auto zero = + CreateConst( + graph, node, std::vector{0.0}, {1}, GetVarDType(logits)) + ->outputs.front(); + auto ignore_index = CreateConst(graph, + node, + std::vector{ignore_index_value}, + {1}, + GetVarDType(label)) + ->outputs.front(); + // max(logits, 0) + auto max_zero = + CreateBaseOp(graph, node, "popart_max", {logits, zero}, {}, {}) + ->outputs.front(); + + // logits * label + auto mul = CreateBaseOp(graph, node, "popart_mul", {logits, label}, {}, {}) + ->outputs.front(); + + // abs(logits) + auto abs = CreateBaseOp(graph, node, "popart_abs", {logits}, {}, {}) + ->outputs.front(); + // -abs(logits) + auto neg_abs = + CreateBaseOp(graph, node, "popart_neg", {abs}, {}, {})->outputs.front(); + // exp(-abs(logits)) + auto exp_neg_abs = CreateBaseOp(graph, node, "popart_exp", {neg_abs}, {}, {}) + ->outputs.front(); + // 1+exp(-abs(logits)) + auto log_term = + CreateBaseOp(graph, node, "popart_add", {exp_neg_abs, one}, {}, {}) + ->outputs.front(); + // log(1+exp(-abs(logits))) + auto log = CreateBaseOp(graph, node, "popart_log", {log_term}, {}, {}) + ->outputs.front(); + + // max(logits, 0) - logits * label + auto sub = CreateBaseOp(graph, node, "popart_sub", {max_zero, mul}, {}, {}) + ->outputs.front(); + // max(logits, 0) - logits * label + log(1 + exp(-abs(logits))) + auto loss = CreateBaseOp(graph, node, "popart_add", {sub, log}, {}, {}) + ->outputs.front(); + + // label == ignore_index ? 0 : loss + auto equal_cond = + CreateBaseOp(graph, node, "popart_equal", {label, ignore_index}, {}, {}) + ->outputs.front(); + loss = CreateBaseOp(graph, + node, + "popart_where", + {equal_cond, zero, loss}, + append_identity_loss || normalize + ? std::vector{} + : std::vector{GetOutputVarNode("Out", node)}, + {}); + + if (normalize) { + // normalize the output as: loss = loss / sum(label != ignore_index) + auto not_equal = + CreateBaseOp(graph, node, "popart_logical_not", {equal_cond}, {}, {}) + ->outputs.front(); + auto mask = + CreateCast(graph, node, {not_equal}, {}, logits->Var()->GetDataType()) + ->outputs.front(); + auto sum = CreateBaseOp(graph, + node, + "popart_reducesum", + {mask}, + {}, + {{"keepdims", int64_t{0}}}) + ->outputs.front(); + auto eps = + CreateConst( + graph, node, std::vector{1e-5}, {1}, GetVarDType(logits)) + ->outputs.front(); + // avoid division by zero + auto add_eps = CreateBaseOp(graph, node, "popart_add", {sum, eps}, {}, {}) + ->outputs.front(); + loss = + CreateBaseOp(graph, + node, + "popart_div", + {loss->outputs[0], add_eps}, + append_identity_loss + ? std::vector{} + : std::vector{GetOutputVarNode("Out", node)}, + {}); + } + + if (append_identity_loss) { + loss = CreateIdentityLossOp( + graph, node, loss->outputs, {GetOutputVarNode("Out", node)}, reduction); + } + return loss; +} + Node *binary_cross_entropy_handler(Graph *graph, Node *node) { // Out = -1 * weight * (label * log(x) + (1 - label) * log(1 - x)) int reduction = 2; @@ -493,6 +610,97 @@ Node *warpctc_handler(Graph *graph, Node *node) { return loss; } +Node *rank_loss_handler(Graph *graph, Node *node) { + // (1.0f + (left - right).exp()).log() - label * (left - right) + auto label = GetInputVarNode("Label", node); + auto left = GetInputVarNode("Left", node); + auto right = GetInputVarNode("Right", node); + auto output = GetOutputVarNode("Out", node); + int reduction = 2; + if (is_dynamic_graph()) { + reduction = RemoveTailReduction(graph, node, "Out"); + } + bool append_identity_loss = is_dynamic_graph() && IsLastVarNode(output); + + auto sub = CreateBaseOp(graph, node, "popart_sub", {left, right}, {}, {}) + ->outputs.front(); + auto mul = CreateBaseOp(graph, node, "popart_mul", {label, sub}, {}, {}) + ->outputs.front(); + // const + auto one = + CreateConst(graph, node, std::vector{1.0}, {1}, GetVarDType(label)) + ->outputs.front(); + auto exp = + CreateBaseOp(graph, node, "popart_exp", {sub}, {}, {})->outputs.front(); + auto add = CreateBaseOp(graph, node, "popart_add", {one, exp}, {}, {}) + ->outputs.front(); + auto log = + CreateBaseOp(graph, node, "popart_log", {add}, {}, {})->outputs.front(); + auto loss = CreateBaseOp(graph, + node, + "popart_sub", + {log, mul}, + append_identity_loss ? std::vector{} + : std::vector{output}, + {}) + ->outputs.front(); + if (append_identity_loss) { + loss = + CreateIdentityLossOp(graph, node, loss->outputs, {output}, reduction); + } + return loss; +} + +Node *margin_rank_loss_handler(Graph *graph, Node *node) { + // rank_loss = max(0, -label * (left - right) + margin) + auto *op = node->Op(); + auto label = GetInputVarNode("Label", node); + auto left = GetInputVarNode("X1", node); + auto right = GetInputVarNode("X2", node); + auto output = GetOutputVarNode("Out", node); + auto margin_value = PADDLE_GET_CONST(float, op->GetAttr("margin")); + int reduction = 2; + if (is_dynamic_graph()) { + reduction = RemoveTailReduction(graph, node, "Out"); + } + bool append_identity_loss = is_dynamic_graph() && IsLastVarNode(output); + + // -(left - right) + auto sub = CreateBaseOp(graph, node, "popart_sub", {right, left}, {}, {}) + ->outputs.front(); + // -label * (left - right) + auto mul = CreateBaseOp(graph, node, "popart_mul", {label, sub}, {}, {}) + ->outputs.front(); + // const + auto zero = + CreateConst(graph, node, std::vector{0.0}, {1}, GetVarDType(label)) + ->outputs.front(); + auto margin = CreateConst(graph, + node, + std::vector{margin_value}, + {1}, + GetVarDType(label)) + ->outputs.front(); + auto margin_add = + CreateBaseOp(graph, node, "popart_add", {mul, margin}, {}, {}) + ->outputs.front(); + + // max(0, term) + auto loss = CreateBaseOp(graph, + node, + "popart_max", + {zero, margin_add}, + append_identity_loss ? std::vector{} + : std::vector{output}, + {}) + ->outputs.front(); + if (append_identity_loss) { + loss = + CreateIdentityLossOp(graph, node, loss->outputs, {output}, reduction); + } + return loss; +} + } // namespace } // namespace ipu } // namespace platform @@ -502,7 +710,11 @@ REGISTER_HANDLER(identity_loss, identity_loss_handler); REGISTER_HANDLER(softmax_with_cross_entropy, softmax_with_cross_entropy_handler); REGISTER_HANDLER(cross_entropy2, cross_entropy2_handler); +REGISTER_HANDLER(sigmoid_cross_entropy_with_logits, + sigmoid_cross_entropy_with_logits_handler); REGISTER_HANDLER(kldiv_loss, kldiv_loss_handler); REGISTER_HANDLER(bce_loss, binary_cross_entropy_handler); REGISTER_HANDLER(huber_loss, huber_loss_handler); REGISTER_HANDLER(warpctc, warpctc_handler); +REGISTER_HANDLER(rank_loss, rank_loss_handler); +REGISTER_HANDLER(margin_rank_loss, margin_rank_loss_handler); diff --git a/python/paddle/fluid/tests/unittests/ipu/test_dy2static_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_dy2static_ipu.py index 7b581de222819..8b3e0104c29cc 100644 --- a/python/paddle/fluid/tests/unittests/ipu/test_dy2static_ipu.py +++ b/python/paddle/fluid/tests/unittests/ipu/test_dy2static_ipu.py @@ -70,8 +70,8 @@ def set_op_attrs(self): self.loss_op = paddle.fluid.layers.cross_entropy def set_data_feed(self): - self.data = paddle.uniform((32, 3, 10, 10), dtype='float32') - self.label = paddle.randint(0, 10, shape=[32], dtype='int64') + self.data = paddle.uniform((8, 3, 10, 10), dtype='float32') + self.label = paddle.randint(0, 10, shape=[8], dtype='int64') def create_model(self, use_ipu=False): return SimpleLayer(loss_op=self.loss_op, @@ -215,8 +215,8 @@ def set_op_attrs(self): self.loss_op = paddle.fluid.layers.softmax_with_cross_entropy def set_data_feed(self): - self.data = paddle.uniform((32, 3, 10, 10), dtype='float32') - self.label = paddle.randint(0, 10, shape=[32, 1], dtype='int64') + self.data = paddle.uniform((8, 3, 10, 10), dtype='float32') + self.label = paddle.randint(0, 10, shape=[8, 1], dtype='int64') def create_model(self, use_ipu=False): return SimpleLayer(loss_op=self.loss_op, @@ -231,8 +231,41 @@ def set_op_attrs(self): self.loss_op = partial(paddle.fluid.layers.kldiv_loss, reduction="none") def set_data_feed(self): - self.data = paddle.uniform((32, 3, 10, 10), dtype='float32') - self.label = paddle.rand(shape=[32, 81], dtype='float32') + self.data = paddle.uniform((8, 3, 10, 10), dtype='float32') + self.label = paddle.rand(shape=[8, 81], dtype='float32') + + def create_model(self, use_ipu=False): + return SimpleLayer(loss_op=self.loss_op, + use_softmax=True, + use_reduction=True, + use_identity_loss=False) + + +class TestWithoutIdentityLoss4(TestBase): + + def set_op_attrs(self): + self.loss_op = paddle.nn.functional.binary_cross_entropy + + def set_data_feed(self): + self.data = paddle.uniform((8, 3, 10, 10), dtype='float32') + self.label = paddle.rand(shape=[8, 81], dtype='float32') + + def create_model(self, use_ipu=False): + return SimpleLayer(loss_op=self.loss_op, + use_softmax=True, + use_reduction=False, + use_identity_loss=False) + + +class TestWithoutIdentityLoss5(TestBase): + + def set_op_attrs(self): + self.loss_op = paddle.fluid.layers.sigmoid_cross_entropy_with_logits + + def set_data_feed(self): + self.data = paddle.uniform((8, 3, 10, 10), dtype='float32') + self.label = paddle.randint(0, 10, shape=[8, 81], + dtype='int64').astype('float32') def create_model(self, use_ipu=False): return SimpleLayer(loss_op=self.loss_op, diff --git a/python/paddle/fluid/tests/unittests/ipu/test_margin_rank_loss_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_margin_rank_loss_op_ipu.py new file mode 100644 index 0000000000000..e9964156a128b --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ipu/test_margin_rank_loss_op_ipu.py @@ -0,0 +1,90 @@ +# Copyright (c) 2022 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. + +import unittest + +import numpy as np +import paddle +import paddle.static +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest + + +class TestBase(IPUOpTest): + + def setUp(self): + self.set_atol() + self.set_training() + self.set_data_feed() + self.set_feed_attr() + self.set_op_attrs() + + def set_data_feed(self): + label = np.random.uniform(size=[3, 1]) + left = np.random.uniform(size=[3, 1]) + right = np.random.uniform(size=[3, 1]) + self.feed_fp32 = { + "label": label.astype(np.float32), + "left": left.astype(np.float32), + "right": right.astype(np.float32), + } + self.feed_fp16 = { + "label": label.astype(np.float16), + "left": left.astype(np.float16), + "right": right.astype(np.float16), + } + + def set_feed_attr(self): + self.feed_shape = [x.shape for x in self.feed_fp32.values()] + self.feed_list = list(self.feed_fp32.keys()) + + def set_op_attrs(self): + self.attrs = { + 'margin': 0.1, + } + + @IPUOpTest.static_graph + def build_model(self, on_ipu): + label = paddle.static.data(name=self.feed_list[0], + shape=self.feed_shape[0], + dtype="float32") + left = paddle.static.data(name=self.feed_list[1], + shape=self.feed_shape[1], + dtype='float32') + right = paddle.static.data(name=self.feed_list[2], + shape=self.feed_shape[2], + dtype='float32') + out = paddle.fluid.layers.margin_rank_loss(label, left, right) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model(self.is_ipu_mode(m)) + self.run_model(m) + self.check() + + +class TestCase1(TestBase): + + def set_op_attrs(self): + self.attrs = { + 'margin': 0.5, + } + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ipu/test_rank_loss_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_rank_loss_op_ipu.py new file mode 100644 index 0000000000000..ad3bbde11923a --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ipu/test_rank_loss_op_ipu.py @@ -0,0 +1,76 @@ +# Copyright (c) 2022 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. + +import unittest + +import numpy as np +import paddle +import paddle.static +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest + + +class TestBase(IPUOpTest): + + def setUp(self): + self.set_atol() + self.set_training() + self.set_data_feed() + self.set_feed_attr() + + def set_data_feed(self): + label = np.random.uniform(size=[3, 1]) + left = np.random.uniform(size=[3, 1]) + right = np.random.uniform(size=[3, 1]) + self.feed_fp32 = { + "label": label.astype(np.float32), + "left": left.astype(np.float32), + "right": right.astype(np.float32), + } + self.feed_fp16 = { + "label": label.astype(np.float16), + "left": left.astype(np.float16), + "right": right.astype(np.float16), + } + + def set_feed_attr(self): + self.feed_shape = [x.shape for x in self.feed_fp32.values()] + self.feed_list = list(self.feed_fp32.keys()) + + @IPUOpTest.static_graph + def build_model(self, on_ipu): + label = paddle.static.data(name=self.feed_list[0], + shape=self.feed_shape[0], + dtype="float32") + left = paddle.static.data(name=self.feed_list[1], + shape=self.feed_shape[1], + dtype='float32') + right = paddle.static.data(name=self.feed_list[2], + shape=self.feed_shape[2], + dtype='float32') + out = paddle.fluid.layers.rank_loss(label, left, right) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model(self.is_ipu_mode(m)) + self.run_model(m) + self.check() + + +if __name__ == "__main__": + unittest.main() diff --git a/python/paddle/fluid/tests/unittests/ipu/test_sigmoid_cross_entropy_with_logits_op_ipu.py b/python/paddle/fluid/tests/unittests/ipu/test_sigmoid_cross_entropy_with_logits_op_ipu.py new file mode 100644 index 0000000000000..997ae46ec82cb --- /dev/null +++ b/python/paddle/fluid/tests/unittests/ipu/test_sigmoid_cross_entropy_with_logits_op_ipu.py @@ -0,0 +1,102 @@ +# Copyright (c) 2022 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. + +import unittest + +import numpy as np +import paddle +import paddle.static +from paddle.fluid.tests.unittests.ipu.op_test_ipu import IPUOpTest +import paddle.nn.functional as F + + +class TestBase(IPUOpTest): + + def setUp(self): + self.set_atol() + self.set_training() + self.set_data_feed() + self.set_feed_attr() + self.set_op_attrs() + + def set_data_feed(self): + x = np.random.uniform(size=[10]) + label = np.arange(10).reshape([10]) + self.feed_fp32 = { + "x": x.astype(np.float32), + "label": label.astype(np.float32) + } + self.feed_fp16 = { + "x": x.astype(np.float16), + "label": label.astype(np.float16) + } + + def set_feed_attr(self): + self.feed_shape = [x.shape for x in self.feed_fp32.values()] + self.feed_list = list(self.feed_fp32.keys()) + + def set_op_attrs(self): + self.attrs = { + 'ignore_index': -100, + } + + @IPUOpTest.static_graph + def build_model(self, on_ipu): + x = paddle.static.data(name=self.feed_list[0], + shape=self.feed_shape[0], + dtype="float32") + label = paddle.static.data(name=self.feed_list[1], + shape=self.feed_shape[1], + dtype='float32') + out = paddle.fluid.layers.sigmoid_cross_entropy_with_logits( + x, label, **self.attrs) + self.fetch_list = [out.name] + + def run_model(self, exec_mode): + self.run_op_test(exec_mode) + + def test(self): + for m in IPUOpTest.ExecutionMode: + if not self.skip_mode(m): + self.build_model(self.is_ipu_mode(m)) + self.run_model(m) + self.check() + + +class TestCase1(TestBase): + + def set_op_attrs(self): + self.attrs = { + 'ignore_index': 1, + } + + +class TestCase2(TestBase): + + def set_atol(self): + # epsilon is added when normalize is True, use larger atol. + self.atol = 1e-6 + self.rtol = 1e-5 + self.atol_fp16 = 1e-3 + self.rtol_fp16 = 1e-3 + + def set_op_attrs(self): + self.attrs = { + 'ignore_index': 1, + 'normalize': True, + } + + +if __name__ == "__main__": + unittest.main()