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- import uctc.nn as nn
- import std_model as stdnn
- import numpy as np
-
- class LinearTestModel:
- def __init__(self, input_features, output_features):
- self.w1 = nn.Parameter([input_features, output_features])
- self.b1 = nn.Parameter([1, output_features])
-
- def forward(self, x):
- l1 = nn.Linear(x, self.w1)
- l2 = nn.AddBias(l1, self.b1)
- return l2
-
- def get_loss(self, x, y):
- return nn.SquareLoss(self.forward(x), y)
-
- def backward(self, x, y):
- loss = self.get_loss(x, y)
- g_w1, g_b1 = nn.gradients(loss, [self.w1, self.b1])
- return g_w1.data(), g_b1.data()
-
- class StdLinerTestModel:
- def __init__(self, input_features, output_features, tmodel: LinearTestModel):
- self.w1 = stdnn.Parameter(input_features, output_features)
- self.b1 = stdnn.Parameter(1, output_features)
- self.w1.data = np.array(tmodel.w1.data()).reshape(input_features, output_features)
- self.b1.data = np.array(tmodel.b1.data()).reshape(1, output_features)
-
- def forward(self, x):
- l1 = stdnn.Linear(x, self.w1)
- l2 = stdnn.AddBias(l1, self.b1)
- return l2
-
- def get_loss(self, x, y):
- return stdnn.SquareLoss(self.forward(x), y)
-
- def backward(self, x, y):
- loss = self.get_loss(x, y)
- g_w1, g_b1 = stdnn.gradients(loss, [self.w1, self.b1])
- return g_w1.data.flatten().tolist(), g_b1.data.flatten().tolist()
-
- input_features = 16
- output_features = 32
- batch_size = 4
- x = np.random.randn(batch_size, input_features).astype(np.float32)
- y = np.random.randn(batch_size, output_features).astype(np.float32)
-
- model = LinearTestModel(input_features, output_features)
- test_x = nn.Constant(x)
- predict_y = model.forward(test_x).data()
- test_y = nn.Constant(y)
- loss = model.get_loss(test_x, test_y).data()
- g_w1, g_b1 = model.backward(test_x, test_y)
-
- stdmodel = StdLinerTestModel(input_features, output_features, model)
- std_test_x = stdnn.Constant(x)
- std_predict_y = stdmodel.forward(std_test_x)
- std_test_y = stdnn.Constant(y)
- std_loss = stdmodel.get_loss(std_test_x, std_test_y)
- std_g_w1, std_g_b1 = stdmodel.backward(std_test_x, std_test_y)
-
- # check forward
- for x, y in zip(predict_y, std_predict_y.data.tolist()[0]):
- if (abs(x-y) > 1e-4):
- assert 0, "Forward data mismatch!"
-
- # check loss
- if abs(loss[0] - std_loss.data) > 1e-4:
- assert 0, "Loss mismatch!"
-
- # check backward
- for i, (x, y) in enumerate(zip(g_w1, std_g_w1)):
- if (abs(x-y) > 1e-4):
- assert 0, f"Gradient w1 mismatch at position {i}, g_w1 is {x} while std g_w1 is {y}"
- for i, (x, y) in enumerate(zip(g_b1, std_g_b1)):
- if (abs(x-y) > 1e-4):
- assert 0, f"Gradient b1 mismatch at position {i}, g_b1 is {x} while std g_b1 is {y}"
-
-
- print("Test passed")
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