remove sandbox code

Author: komaksym

questions/123_mixed_precision_training/solution.py

@@ -32,36 +32,4 @@ def backward(self, gradients):
 
         # Unscale gradients (ensure result is float32)
         unscaled_gradients = gradients_fp32 / self.loss_scale
-        return unscaled_gradients.astype(np.float32)
-    
-
-tests = [
-  {
-    "input": "import numpy as np\nmp = MixedPrecision(loss_scale=1024.0)\nweights = np.array([0.5, -0.3], dtype=np.float32)\ninputs = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)\ntargets = np.array([1.0, 0.0], dtype=np.float32)\nloss = mp.forward(weights, inputs, targets)\nprint(f\"Loss: {loss:.4f}\")\nprint(f\"Loss dtype: {type(loss).__name__}\")\ngrads = np.array([512.0, -256.0], dtype=np.float32)\nresult = mp.backward(grads)\nprint(f\"Gradients: {result}\")\nprint(f\"Grad dtype: {result.dtype}\")",
-    "output": "Loss: 1638.4000\nLoss dtype: float\nGradients: [0.5 -0.25]\nGrad dtype: float32"
-  },
-  {
-    "input": "import numpy as np\nmp = MixedPrecision(loss_scale=512.0)\nweights = np.array([1.0, 0.5], dtype=np.float64)\ninputs = np.array([[2.0, 1.0]], dtype=np.float64)\ntargets = np.array([3.0], dtype=np.float64)\nloss = mp.forward(weights, inputs, targets)\nprint(f\"Loss: {loss:.1f}\")\nprint(f\"Loss dtype: {type(loss).__name__}\")\ngrads = np.array([1024.0, 512.0], dtype=np.float16)\nresult = mp.backward(grads)\nprint(f\"Gradients: [{result[0]:.0f} {result[1]:.0f}]\")\nprint(f\"Grad dtype: {result.dtype}\")",
-    "output": "Loss: 256.0\nLoss dtype: float\nGradients: [2 1]\nGrad dtype: float32"
-  },
-  {
-    "input": "import numpy as np\nmp = MixedPrecision(loss_scale=100.0)\nweights = np.array([0.1, 0.2], dtype=np.float32)\ninputs = np.array([[1.0, 1.0]], dtype=np.float32)\ntargets = np.array([0.5], dtype=np.float32)\nloss = mp.forward(weights, inputs, targets)\nprint(f\"Loss: {loss:.1f}\")\nprint(f\"Loss dtype: {type(loss).__name__}\")\ngrads = np.array([200.0, 100.0], dtype=np.float64)\nresult = mp.backward(grads)\nprint(f\"Gradients: [{result[0]:.0f} {result[1]:.0f}]\")\nprint(f\"Grad dtype: {result.dtype}\")",
-    "output": "Loss: 4.0\nLoss dtype: float\nGradients: [2 1]\nGrad dtype: float32"
-  },
-  {
-    "input": "import numpy as np\nmp = MixedPrecision(loss_scale=2048.0)\nweights = np.array([0.25], dtype=np.float64)\ninputs = np.array([[4.0]], dtype=np.float64)\ntargets = np.array([2.0], dtype=np.float64)\nloss = mp.forward(weights, inputs, targets)\nprint(f\"Loss: {loss:.1f}\")\nprint(f\"Loss dtype: {type(loss).__name__}\")\ngrads = np.array([np.nan], dtype=np.float16)\nresult = mp.backward(grads)\nprint(f\"Gradients: [{result[0]:.0f}]\")\nprint(f\"Grad dtype: {result.dtype}\")",
-    "output": "Loss: 2048.0\nLoss dtype: float\nGradients: [0]\nGrad dtype: float32"
-  },
-  {
-    "input": "import numpy as np\nmp = MixedPrecision(loss_scale=256.0)\nweights = np.array([1.0], dtype=np.float16)\ninputs = np.array([[2.0]], dtype=np.float16)\ntargets = np.array([3.0], dtype=np.float16)\nloss = mp.forward(weights, inputs, targets)\nprint(f\"Loss: {loss:.1f}\")\nprint(f\"Loss dtype: {type(loss).__name__}\")\ngrads = np.array([np.inf], dtype=np.float64)\nresult = mp.backward(grads)\nprint(f\"Gradients: [{result[0]:.0f}]\")\nprint(f\"Grad dtype: {result.dtype}\")",
-    "output": "Loss: 256.0\nLoss dtype: float\nGradients: [0]\nGrad dtype: float32"
-  }
-]
-
-for i, test in enumerate(tests):
-    print(f"Test #{i+1}.")
-    print("Got:")
-    exec(test['input'])
-    print(f"\nExpected: {test['output']}")
-    print("--------" * 3)
-
+        return unscaled_gradients.astype(np.float32)