Merge pull request #1305 from NLGithubWP/dev-postgresql

Add the example data folder for SINGA peft

Author: lzjpaul

examples/singa_peft/examples/train.py

@@ -0,0 +1,399 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements.  See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership.  The ASF licenses this file
+# to you 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 argparse
+
+from singa import device
+from singa import tensor
+from singa import opt
+import numpy as np
+import time
+from PIL import Image
+
+from singa_peft import LinearLoraConfig, get_peft_model
+
+np_dtype = {"float16": np.float16, "float32": np.float32}
+
+singa_dtype = {"float16": tensor.float16, "float32": tensor.float32}
+
+
+# Data augmentation
+def augmentation(x, batch_size):
+    xpad = np.pad(x, [[0, 0], [0, 0], [4, 4], [4, 4]], 'symmetric')
+    for data_num in range(0, batch_size):
+        offset = np.random.randint(8, size=2)
+        x[data_num, :, :, :] = xpad[data_num, :,
+                                    offset[0]:offset[0] + x.shape[2],
+                                    offset[1]:offset[1] + x.shape[2]]
+        if_flip = np.random.randint(2)
+        if (if_flip):
+            x[data_num, :, :, :] = x[data_num, :, :, ::-1]
+    return x
+
+
+# Calculate accuracy
+def accuracy(pred, target):
+    # y is network output to be compared with ground truth (int)
+    y = np.argmax(pred, axis=1)
+    a = y == target
+    correct = np.array(a, "int").sum()
+    return correct
+
+
+# Data partition according to the rank
+def partition(global_rank, world_size, train_x, train_y, val_x, val_y):
+    # Partition training data
+    data_per_rank = train_x.shape[0] // world_size
+    idx_start = global_rank * data_per_rank
+    idx_end = (global_rank + 1) * data_per_rank
+    train_x = train_x[idx_start:idx_end]
+    train_y = train_y[idx_start:idx_end]
+
+    # Partition evaluation data
+    data_per_rank = val_x.shape[0] // world_size
+    idx_start = global_rank * data_per_rank
+    idx_end = (global_rank + 1) * data_per_rank
+    val_x = val_x[idx_start:idx_end]
+    val_y = val_y[idx_start:idx_end]
+    return train_x, train_y, val_x, val_y
+
+
+# Function to all reduce NUMPY accuracy and loss from multiple devices
+def reduce_variable(variable, dist_opt, reducer):
+    reducer.copy_from_numpy(variable)
+    dist_opt.all_reduce(reducer.data)
+    dist_opt.wait()
+    output = tensor.to_numpy(reducer)
+    return output
+
+
+def resize_dataset(x, image_size):
+    num_data = x.shape[0]
+    dim = x.shape[1]
+    X = np.zeros(shape=(num_data, dim, image_size, image_size),
+                 dtype=np.float32)
+    for n in range(0, num_data):
+        for d in range(0, dim):
+            X[n, d, :, :] = np.array(Image.fromarray(x[n, d, :, :]).resize(
+                (image_size, image_size), Image.BILINEAR),
+                                     dtype=np.float32)
+    return X
+
+def run(global_rank,
+        world_size,
+        local_rank,
+        max_epoch,
+        batch_size,
+        model,
+        data,
+        dir_path,
+        sgd,
+        graph,
+        verbosity,
+        dist_option='plain',
+        spars=None,
+        peft_type='None',
+        precision='float32',
+        ):
+    dev = device.get_default_device()
+    dev.SetRandSeed(0)
+    np.random.seed(0)
+
+    if data == "mnist":
+        from examples.data import mnist
+        train_x, train_y, val_x, val_y = mnist.load(dir_path)
+    else:
+        raise ValueError(f"`r`Not support dataset {data}")
+
+
+    num_channels = train_x.shape[1]
+    image_size = train_x.shape[2]
+    data_size = np.prod(train_x.shape[1:train_x.ndim]).item()
+    num_classes = (np.max(train_y) + 1).item()
+
+    if model == "mlp":
+        from examples.model import mlp
+        model = mlp.create_model(in_features=data_size, perceptron_size=16, num_classes=num_classes)
+    elif model == "cnn":
+        from examples.model import cnn
+        model = cnn.create_model(num_channels=num_channels, num_classes=num_classes)
+    else:
+        raise ValueError(f"`r`Not support model {model}")
+
+    # For distributed training, sequential has better performance
+    if hasattr(sgd, "communicator"):
+        DIST = True
+        sequential = True
+    else:
+        DIST = False
+        sequential = False
+
+    if DIST:
+        train_x, train_y, val_x, val_y = partition(global_rank, world_size,
+                                                   train_x, train_y, val_x,
+                                                   val_y)
+
+    if model.dimension == 4:
+        tx = tensor.Tensor(
+            (batch_size, num_channels, model.input_size, model.input_size), dev, singa_dtype[precision])
+    elif model.dimension == 2:
+        tx = tensor.Tensor((batch_size, data_size), dev, singa_dtype[precision])
+        np.reshape(train_x, (train_x.shape[0], -1))
+        np.reshape(val_x, (val_x.shape[0], -1))
+
+    ty = tensor.Tensor((batch_size,), dev, tensor.int32)
+    total_train = train_x.shape[0]
+    num_train_batch = total_train // batch_size
+    total_val = val_x.shape[0]
+    num_val_batch = total_val // batch_size
+    idx = np.arange(total_train, dtype=np.int32)
+
+    # Attach model to graph
+    model.set_optimizer(sgd)
+    model.compile([tx], is_train=True, use_graph=False, sequential=sequential)
+    dev.SetVerbosity(verbosity)
+
+    # Training and evaluation loop
+    for epoch in range(max_epoch):
+        start_time = time.time()
+        np.random.shuffle(idx)
+        if global_rank == 0:
+            print('Starting Epoch %d:' % (epoch))
+
+        # Training phase
+        train_correct = np.zeros(shape=[1], dtype=np.float32)
+        test_correct = np.zeros(shape=[1], dtype=np.float32)
+        train_loss = np.zeros(shape=[1], dtype=np.float32)
+
+        model.train()
+        for b in range(num_train_batch):
+            x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
+            if model.dimension == 4:
+                x = augmentation(x, batch_size)
+                if (image_size != model.input_size):
+                    x = resize_dataset(x, model.input_size)
+            x = x.astype(np_dtype[precision])
+            y = train_y[idx[b * batch_size:(b + 1) * batch_size]]
+            # Copy the patch data into input tensors
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+            # Train the model
+            out, loss = model(tx, ty, dist_option, spars)
+            train_correct += accuracy(tensor.to_numpy(out), y)
+            train_loss += tensor.to_numpy(loss)[0]
+
+        if DIST:
+            # Reduce the evaluation accuracy and loss from multiple devices
+            reducer = tensor.Tensor((1,), dev, tensor.float32)
+            train_correct = reduce_variable(train_correct, sgd, reducer)
+            train_loss = reduce_variable(train_loss, sgd, reducer)
+
+        if global_rank == 0:
+            print('Training loss = %.2f, training accuracy = %.2f %%' %
+                  (train_loss, train_correct / (total_train * world_size) * 100.0), flush=True)
+
+        # Evaluation phase
+        model.eval()
+        for b in range(num_val_batch):
+            x = val_x[b * batch_size:(b + 1) * batch_size]
+            if model.dimension == 4:
+                if (image_size != model.input_size):
+                    x = resize_dataset(x, model.input_size)
+            x = x.astype(np_dtype[precision])
+            y = val_y[b * batch_size:(b + 1) * batch_size]
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+            out_test = model(tx)
+            test_correct += accuracy(tensor.to_numpy(out_test), y)
+
+        if DIST:
+            # Reduce the evaulation accuracy from multiple devices
+            test_correct = reduce_variable(test_correct, sgd, reducer)
+
+        # Output the evaluation accuracy
+        if global_rank == 0:
+            print('Evaluation accuracy = %.2f %%, Elapsed Time = %fs' %
+                  (test_correct / (total_val * world_size) * 100.0, time.time() - start_time), flush=True)
+    # peft
+    peft(global_rank, world_size, local_rank, max_epoch, batch_size, model, data, dir_path, sgd, graph, verbosity, dist_option, spars, peft_type, precision)
+
+
+def peft(global_rank,
+         world_size,
+         local_rank,
+         max_epoch,
+         batch_size,
+         model,
+         data,
+         dir_path,
+         sgd,
+         graph,
+         verbosity,
+         dist_option='plain',
+         spars=None,
+         peft_type='None',
+         precision='float32'):
+    dev = device.get_default_device()
+    dev.SetRandSeed(0)
+    np.random.seed(0)
+
+    if data == "mnist":
+        from examples.data import mnist
+        train_x, train_y, val_x, val_y = mnist.load(dir_path)
+    else:
+        raise ValueError(f"`r`Not support dataset {data}")
+
+    num_channels = train_x.shape[1]
+    image_size = train_x.shape[2]
+    data_size = np.prod(train_x.shape[1:train_x.ndim]).item()
+    num_classes = (np.max(train_y) + 1).item()
+
+    # For distributed training, sequential has better performance
+    if hasattr(sgd, "communicator"):
+        DIST = True
+        sequential = True
+    else:
+        DIST = False
+        sequential = False
+
+    if DIST:
+        train_x, train_y, val_x, val_y = partition(global_rank, world_size,
+                                                   train_x, train_y, val_x,
+                                                   val_y)
+
+    if model.dimension == 4:
+        tx = tensor.Tensor(
+            (batch_size, num_channels, model.input_size, model.input_size), dev, singa_dtype[precision])
+    elif model.dimension == 2:
+        tx = tensor.Tensor((batch_size, data_size), dev, singa_dtype[precision])
+        np.reshape(train_x, (train_x.shape[0], -1))
+        np.reshape(val_x, (val_x.shape[0], -1))
+
+    ty = tensor.Tensor((batch_size,), dev, tensor.int32)
+    total_train = train_x.shape[0]
+    num_train_batch = total_train // batch_size
+    total_val = val_x.shape[0]
+    num_val_batch = total_val // batch_size
+    idx = np.arange(total_train, dtype=np.int32)
+    # Attach model to graph
+    peft_model = model
+    # peft
+    if peft_type == 'linear_lora':
+        config = LinearLoraConfig(8, 1, 0., ["linear1", "linear2"])
+        peft_model = get_peft_model(model, config)
+    peft_model.set_optimizer(sgd)
+    peft_model.compile([tx], is_train=True, use_graph=graph, sequential=sequential)
+    dev.SetVerbosity(verbosity)
+
+    # Training and evaluation loop
+    for epoch in range(max_epoch):
+        start_time = time.time()
+        np.random.shuffle(idx)
+        if global_rank == 0:
+            print('Starting Epoch %d:' % (epoch))
+
+        # Training phase
+        train_correct = np.zeros(shape=[1], dtype=np.float32)
+        test_correct = np.zeros(shape=[1], dtype=np.float32)
+        train_loss = np.zeros(shape=[1], dtype=np.float32)
+
+        peft_model.train()
+        for b in range(num_train_batch):
+            x = train_x[idx[b * batch_size:(b + 1) * batch_size]]
+            if model.dimension == 4:
+                x = augmentation(x, batch_size)
+                if (image_size != model.input_size):
+                    x = resize_dataset(x, model.input_size)
+            x = x.astype(np_dtype[precision])
+            y = train_y[idx[b * batch_size:(b + 1) * batch_size]]
+            # Copy the patch data into input tensors
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+            # Train the model
+            out, loss = peft_model(tx, ty, dist_option, spars)
+            train_correct += accuracy(tensor.to_numpy(out), y)
+            train_loss += tensor.to_numpy(loss)[0]
+
+        if DIST:
+            # Reduce the evaluation accuracy and loss from multiple devices
+            reducer = tensor.Tensor((1,), dev, tensor.float32)
+            train_correct = reduce_variable(train_correct, sgd, reducer)
+            train_loss = reduce_variable(train_loss, sgd, reducer)
+
+        if global_rank == 0:
+            print('Training loss = %.2f, training accuracy = %.2f %%' %
+                  (train_loss, train_correct / (total_train * world_size) * 100.0), flush=True)
+        # Evaluation phase
+        peft_model.eval()
+        for b in range(num_val_batch):
+            x = val_x[b * batch_size:(b + 1) * batch_size]
+            if model.dimension == 4:
+                if (image_size != model.input_size):
+                    x = resize_dataset(x, model.input_size)
+            x = x.astype(np_dtype[precision])
+            y = val_y[b * batch_size:(b + 1) * batch_size]
+            tx.copy_from_numpy(x)
+            ty.copy_from_numpy(y)
+            out_test = peft_model(tx)
+            test_correct += accuracy(tensor.to_numpy(out_test), y)
+        if DIST:
+            # Reduce the evaulation accuracy from multiple devices
+            test_correct = reduce_variable(test_correct, sgd, reducer)
+
+        # Output the evaluation accuracy
+        if global_rank == 0:
+            print('Evaluation accuracy = %.2f %%, Elapsed Time = %fs' %
+                  (test_correct / (total_val * world_size) * 100.0, time.time() - start_time), flush=True)
+
+    # for infer, merge_weights can speed up
+    peft_model.merge_weights(mode=True)
+    print("after merge weights.")
+    print(peft_model.get_params())
+    # for train or val, unmerge weights to train
+    peft_model.merge_weights(mode=False)
+    print("after unmerge weights.")
+    print(peft_model.get_params())
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(
+        description='Training using the autograd and graph.')
+    parser.add_argument('model', choices=['mlp', 'cnn'], default='mlp')
+    parser.add_argument('data', choices=['mnist'], default='mnist')
+    parser.add_argument('peft', choices=['None', "linear_lora"], default='None')
+    parser.add_argument('-m', '--max-epoch', default=100, type=int, help='maximum epochs', dest='max_epoch')
+    parser.add_argument('-dir', '--dir-path', default="/tmp/mnist", type=str, help='the directory to store the malaria dataset', dest='dir_path')
+    parser.add_argument('-b', '--batch-size', default=32, type=int, help='batch size', dest='batch_size')
+    parser.add_argument('-l', '--learning-rate', default=0.01, type=float, help='initial learning rate', dest='lr')
+    parser.add_argument('-i', '--device-id', default=0, type=int, help='which GPU to use', dest='device_id')
+    parser.add_argument('-g', '--disable-graph', default='True', action='store_false', help='disable graph', dest='graph')
+    parser.add_argument('-v', '--log-verbosity', default=0, type=int, help='logging verbosity', dest='verbosity')
+
+    args = parser.parse_args()
+    sgd = opt.SGD(lr=args.lr, momentum=0.9, weight_decay=1e-5, dtype=singa_dtype[args.precision])
+    run(0, 1, args.device_id,
+        args.max_epoch,
+        args.batch_size,
+        args.model,
+        args.data,
+        args.dir_path,
+        sgd,
+        args.graph,
+        args.verbosity,
+        peft_type=args.peft,
+        precision=args.precision)