fix(mge/sdk): fix README.md and add missed example

GitOrigin-RevId: 0836029312279ef7bcd6fa040d57348dd5af0630

sdk/load-and-run/README.md

@@ -3,60 +3,65 @@
 Load a model and run, for testing/debugging/profiling.
 
 ## Build
-*megvii3 build*
-```sh
-bazel build //brain/megbrain:load_and_run
-```
 
-See [mnist-example](../mnist-example) for detailed explanations on build.
+<!--
+-->
+
+### Build with cmake
+
+Build MegEngine from source following [README.md](../../README.md). It will also produce the executable, `load_and_run`, which loads a model and runs the test cases attached to the model.
+
 
-## Dump Model
+<!--
+-->
 
-There are two methods to dump model:
+## Dump Model with Test Cases Using [dump_with_testcase_mge.py](dump_with_testcase_mge.py)
 
-1. Dump by `MegHair/utils/debug/load_network_and_run.py --dump-cpp-model
-   /path/to/output`, to test on random inputs. Useful for profiling.
-2. Pack model as specified by
-   [`dump_with_testcase.py`](dump_with_testcase.py), and use
-   that script to dump model. This is useful for checking correctness on
-   different platforms.
+### Step 1
 
-### Input File for `dump_with_testcase.py`
+Dump the model by calling the python API `megengine.jit.trace.dump()`.
 
-The input file must be a python pickle. It can be in one of the following two
-formats:
+### Step 2
 
-1. Contain a network that can be loaded by `meghair.utils.io.load_network`; in
-   such case, `--data` must be given and network output evaulated on current
-   computing device is used as groundtruth. All output vars would be checked.
-   The input data can be one of the following:
-   1. In the format `var0:file0;var1:file1...` meaning that `var0` should use
-      image file `file0`, `var1` should use image `file1` and so on. If there
-      is only one input var, the var name can be omitted. This can be combined
-      with `--resize-input` option.
-   2. In the format `var0:#rand(min, max, shape...);var1:#rand(min, max)...` 
-      meaning to fill the corresponding input vars with uniform random numbers 
-      in the range `[min, max)`, optionally overriding its shape.
-2. Contain a dict in the format `{"outputs": [], "testcases": []}`, where
-   `outputs` is a list of output `VarNode`s and `testcases` is a list of test
-   cases. Each test case should be a dict that maps input var names to
-   corresponding values as `numpy.ndarray`. The expected outputs should also be
-   provided as inputs, and correctness should be checked by `AssertEqual`. You
-   can find more details in `dump_with_testcase.py`.
+Append the test cases to the dumped model using [dump_with_testcase_mge.py](dump_with_testcase_mge.py).
 
-### Input File for `dump_with_testcase_mge.py`
+The basic usage of [dump_with_testcase_mge.py](dump_with_testcase_mge.py) is
+
+```
+python3 dump_with_testcase_mge.py model -d input_description -o model_with_testcases
+
+```
 
-The input file is obtained by calling `megengine.jit.trace.dump()`.
-`--data` must be given.
+where `model` is the file dumped at step 1, `input_description` describes the input data of the test cases, and `model_with_testcases` is the saved model with test cases.
 
-## Example
+`input_description` can be provided in the following ways:
 
-1. Obtain the model file by running [xornet.py](../../python_module/examples/xor/xornet.py)
+1. In the format `var0:file0;var1:file1...` meaning that `var0` should use
+   image file `file0`, `var1` should use image `file1` and so on. If there
+   is only one input var, the var name can be omitted. This can be combined
+   with `--resize-input` option.
+2. In the format `var0:#rand(min, max, shape...);var1:#rand(min, max)...`
+   meaning to fill the corresponding input vars with uniform random numbers
+   in the range `[min, max)`, optionally overriding its shape.
+
+For more usages, run
+
+```
+python3 dump_with_testcase_mge.py --help
+```
+
+### Example
+
+1. Obtain the model file by running [xornet.py](../xor-deploy/xornet.py).
 
 2. Dump the file with test cases attached to the model.
 
-    ```
-    python3 dump_with_testcase_mge.py xornet_deploy.mge -o xornet.mge -d "#rand(0.1, 0.8, 4, 2)"
-    ```
+   ```
+   python3 dump_with_testcase_mge.py xornet_deploy.mge -o xornet.mge -d "#rand(0.1, 0.8, 4, 2)"
+   ```
+
+3. Verify the correctness by running `load_and_run` at the target platform.
 
-    The dumped file `xornet.mge` can be loaded by `load_and_run`.
+   ```
+   load_and_run xornet.mge
+   ```

sdk/xor-deploy/README.md

@@ -12,11 +12,11 @@
 
 * Step 3: run with dumped model
 
-     The dumped model can be obtained by running [xornet.py](../../python_module/examples/xor/xornet.py)
+     The dumped model can be obtained by running [xornet.py](xornet.py)
 
 
     ```
-    LD_LIBRARY_PATH=$MGE_INSTALL_PATH:$LD_LIBRARY_PATH ./xor_deploy xornet_deploy.mge 0.6 0.9
+    LD_LIBRARY_PATH=$MGE_INSTALL_PATH/lib64:$LD_LIBRARY_PATH ./xor_deploy xornet_deploy.mge 0.6 0.9
     ```
 
     Sample output:

sdk/xor-deploy/xornet.py

+import numpy as np
+
+import megengine as mge
+import megengine.functional as F
+import megengine.module as M
+import megengine.optimizer as optim
+from megengine.jit import trace
+
+
+def minibatch_generator(batch_size):
+    while True:
+        inp_data = np.zeros((batch_size, 2))
+        label = np.zeros(batch_size, dtype=np.int32)
+        for i in range(batch_size):
+            inp_data[i, :] = np.random.rand(2) * 2 - 1
+            label[i] = 1 if np.prod(inp_data[i]) < 0 else 0
+        yield {"data": inp_data.astype(np.float32), "label": label.astype(np.int32)}
+
+
+class XORNet(M.Module):
+    def __init__(self):
+        self.mid_dim = 14
+        self.num_class = 2
+        super().__init__()
+        self.fc0 = M.Linear(self.num_class, self.mid_dim, bias=True)
+        self.fc1 = M.Linear(self.mid_dim, self.mid_dim, bias=True)
+        self.fc2 = M.Linear(self.mid_dim, self.num_class, bias=True)
+
+    def forward(self, x):
+        x = self.fc0(x)
+        x = F.tanh(x)
+        x = self.fc1(x)
+        x = F.tanh(x)
+        x = self.fc2(x)
+        return x
+
+
+@trace(symbolic=True)
+def train_fun(data, label, net=None, opt=None):
+    net.train()
+    pred = net(data)
+    loss = F.cross_entropy_with_softmax(pred, label)
+    opt.backward(loss)
+    return pred, loss
+
+
+@trace(symbolic=True)
+def val_fun(data, label, net=None):
+    net.eval()
+    pred = net(data)
+    loss = F.cross_entropy_with_softmax(pred, label)
+    return pred, loss
+
+
+@trace(symbolic=True)
+def pred_fun(data, net=None):
+    net.eval()
+    pred = net(data)
+    pred_normalized = F.softmax(pred)
+    return pred_normalized
+
+
+def main():
+
+    if not mge.is_cuda_available():
+        mge.set_default_device("cpux")
+
+    net = XORNet()
+    opt = optim.SGD(net.parameters(requires_grad=True), lr=0.01, momentum=0.9)
+    batch_size = 64
+    train_dataset = minibatch_generator(batch_size)
+    val_dataset = minibatch_generator(batch_size)
+
+    data = mge.tensor()
+    label = mge.tensor(np.zeros((batch_size,)), dtype=np.int32)
+    train_loss = []
+    val_loss = []
+    for step, minibatch in enumerate(train_dataset):
+        if step > 1000:
+            break
+        data.set_value(minibatch["data"])
+        label.set_value(minibatch["label"])
+        opt.zero_grad()
+        _, loss = train_fun(data, label, net=net, opt=opt)
+        train_loss.append((step, loss.numpy()))
+        if step % 50 == 0:
+            minibatch = next(val_dataset)
+            _, loss = val_fun(data, label, net=net)
+            loss = loss.numpy()[0]
+            val_loss.append((step, loss))
+            print("Step: {} loss={}".format(step, loss))
+        opt.step()
+
+    test_data = np.array(
+        [
+            (0.5, 0.5),
+            (0.3, 0.7),
+            (0.1, 0.9),
+            (-0.5, -0.5),
+            (-0.3, -0.7),
+            (-0.9, -0.1),
+            (0.5, -0.5),
+            (0.3, -0.7),
+            (0.9, -0.1),
+            (-0.5, 0.5),
+            (-0.3, 0.7),
+            (-0.1, 0.9),
+        ]
+    )
+
+    data.set_value(test_data)
+    out = pred_fun(data, net=net)
+    pred_output = out.numpy()
+    pred_label = np.argmax(pred_output, 1)
+
+    print("Test data")
+    print(test_data)
+
+    with np.printoptions(precision=4, suppress=True):
+        print("Predicated probability:")
+        print(pred_output)
+
+    print("Predicated label")
+    print(pred_label)
+
+    model_name = "xornet_deploy.mge"
+
+    if pred_fun.enabled:
+        print("Dump model as {}".format(model_name))
+        pred_fun.dump(model_name, arg_names=["data"])
+    else:
+        print("pred_fun must be run with trace enabled in order to dump model")
+
+
+if __name__ == "__main__":
+    main()

src/jit/README.md

@@ -33,7 +33,6 @@ elemwise expressions by JIT.
     |speed   |100%|122% | 124%          |
 
 
-
 ## What does JIT do
 Detection the subgraph can be fused and compiling the subgraph into a fusion
 kernel are the most two important parts in JIT.