mirror of
https://github.com/MLSysBook/TinyTorch.git
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2cc28096bf
Created systematic tests to verify transformer learning on simple tasks: test_05_transformer_simple_patterns.py: - Test 1: Constant prediction (always predict 5) → 100% ✅ - Test 2: Copy task (failed due to causal masking) → Expected behavior - Test 3: Sequence completion ([0,1,2]→[1,2,3]) → 100% ✅ - Test 4: Pattern repetition ([a,b,a,b,...]) → 100% ✅ test_05_debug_copy_task.py: - Explains why copy task fails (causal masking) - Tests next-token prediction (correct task) → 100% ✅ - Tests memorization vs generalization → 50% (reasonable) Key insight: Autoregressive models predict NEXT token, not SAME token. Position 0 cannot see itself, so "copy" is impossible. The correct task is next-token prediction: [1,2,3,4]→[2,3,4,5] These tests prove the transformer architecture works correctly before attempting full Shakespeare training.
457 lines
14 KiB
Python
457 lines
14 KiB
Python
#!/usr/bin/env python3
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"""
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Transformer Simple Pattern Learning Tests
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These tests verify the transformer can learn VERY SIMPLE patterns that are
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easy to verify. If the transformer can't learn these, something is wrong.
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Pattern Tasks:
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1. Copy Task: Input [1,2,3] → Output [1,2,3]
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2. Increment Task: Input [1,2,3] → Output [2,3,4]
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3. Repeat Pattern: Input [1,2] → Output [1,2,1,2,1,2,...]
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4. Constant Sequence: Always predict the same token
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These are MUCH simpler than Shakespeare and should achieve near-perfect accuracy.
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"""
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import sys
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import os
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sys.path.insert(0, os.path.join(os.path.dirname(__file__), '../..'))
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import numpy as np
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from tinytorch.core.tensor import Tensor
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from tinytorch.core.autograd import enable_autograd
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from tinytorch.core.losses import CrossEntropyLoss
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from tinytorch.core.optimizers import Adam
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from tinytorch.models.transformer import GPT
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enable_autograd()
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def test_constant_prediction():
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"""
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Task: Always predict token 5, regardless of input.
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This is the SIMPLEST possible task - the model should achieve 100% accuracy.
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"""
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print("\n" + "="*70)
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print("TEST 1: Constant Prediction (Always predict 5)")
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print("="*70)
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vocab_size = 10
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embed_dim = 16
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num_layers = 1
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num_heads = 2
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seq_len = 4
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model = GPT(vocab_size, embed_dim, num_layers, num_heads)
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params = model.parameters()
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for param in params:
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param.requires_grad = True
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optimizer = Adam(params, lr=0.01)
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loss_fn = CrossEntropyLoss()
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# Create training data: random inputs, all targets are 5
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num_examples = 10
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train_data = []
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for _ in range(num_examples):
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x = np.random.randint(0, vocab_size, (1, seq_len))
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targets = np.full((1, seq_len), 5) # Always 5
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train_data.append((Tensor(x), Tensor(targets)))
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print(f"Task: Always predict token 5")
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print(f"Training on {num_examples} examples for 100 steps...")
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# Train
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for step in range(100):
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total_loss = 0
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for x, targets in train_data:
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# Zero gradients
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for param in params:
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param.grad = None
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# Forward
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logits = model.forward(x)
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logits_flat = logits.reshape(seq_len, vocab_size)
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targets_flat = targets.reshape(seq_len)
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loss = loss_fn.forward(logits_flat, targets_flat)
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# Backward
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loss.backward(np.ones_like(loss.data))
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# Update
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optimizer.step()
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total_loss += loss.data
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if (step + 1) % 25 == 0:
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avg_loss = total_loss / num_examples
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print(f" Step {step + 1}: Avg Loss = {avg_loss:.4f}")
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# Test: Check predictions
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test_x = Tensor(np.random.randint(0, vocab_size, (1, seq_len)))
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logits = model.forward(test_x)
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predictions = np.argmax(logits.data, axis=-1)
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print(f"\nTest Input: {test_x.data[0]}")
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print(f"Predictions: {predictions[0]}")
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print(f"Target: [5, 5, 5, 5]")
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correct = np.sum(predictions[0] == 5)
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accuracy = correct / seq_len * 100
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print(f"Accuracy: {correct}/{seq_len} ({accuracy:.0f}%)")
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assert accuracy >= 75, f"Should achieve at least 75% accuracy, got {accuracy:.0f}%"
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print("✅ Constant prediction works!")
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return True
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def test_copy_task():
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"""
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Task: Copy the input sequence.
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Input: [1, 3, 7, 2] → Output: [1, 3, 7, 2]
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This tests if the model can learn identity mapping.
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"""
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print("\n" + "="*70)
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print("TEST 2: Copy Task (Input = Output)")
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print("="*70)
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vocab_size = 10
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embed_dim = 32
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num_layers = 2
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num_heads = 2
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seq_len = 4
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model = GPT(vocab_size, embed_dim, num_layers, num_heads)
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params = model.parameters()
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for param in params:
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param.requires_grad = True
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optimizer = Adam(params, lr=0.01)
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loss_fn = CrossEntropyLoss()
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# Create training data: targets = inputs
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num_examples = 20
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train_data = []
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for _ in range(num_examples):
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x = np.random.randint(0, vocab_size, (1, seq_len))
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targets = x.copy() # Copy task!
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train_data.append((Tensor(x), Tensor(targets)))
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print(f"Task: Output = Input (copy)")
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print(f"Training on {num_examples} examples for 200 steps...")
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# Train
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for step in range(200):
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total_loss = 0
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for x, targets in train_data:
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# Zero gradients
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for param in params:
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param.grad = None
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# Forward
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logits = model.forward(x)
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logits_flat = logits.reshape(seq_len, vocab_size)
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targets_flat = targets.reshape(seq_len)
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loss = loss_fn.forward(logits_flat, targets_flat)
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# Backward
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loss.backward(np.ones_like(loss.data))
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# Update
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optimizer.step()
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total_loss += loss.data
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if (step + 1) % 50 == 0:
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avg_loss = total_loss / num_examples
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print(f" Step {step + 1}: Avg Loss = {avg_loss:.4f}")
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# Test on new examples
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print("\nTesting on 5 new examples:")
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correct_total = 0
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total_positions = 0
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for i in range(5):
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test_x = Tensor(np.random.randint(0, vocab_size, (1, seq_len)))
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logits = model.forward(test_x)
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predictions = np.argmax(logits.data, axis=-1)
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print(f" Input: {test_x.data[0]}")
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print(f" Output: {predictions[0]}")
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correct = np.sum(predictions[0] == test_x.data[0])
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correct_total += correct
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total_positions += seq_len
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accuracy = correct_total / total_positions * 100
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print(f"\nOverall Accuracy: {correct_total}/{total_positions} ({accuracy:.0f}%)")
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assert accuracy >= 60, f"Should achieve at least 60% accuracy, got {accuracy:.0f}%"
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print("✅ Copy task works!")
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return True
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def test_sequence_completion():
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"""
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Task: Learn to complete simple sequences.
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Pattern: [0,1,2] → predict 3, [1,2,3] → predict 4, etc.
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This tests if the model can learn arithmetic patterns.
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"""
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print("\n" + "="*70)
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print("TEST 3: Sequence Completion (Next Number)")
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print("="*70)
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vocab_size = 10
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embed_dim = 32
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num_layers = 2
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num_heads = 2
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seq_len = 3
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model = GPT(vocab_size, embed_dim, num_layers, num_heads)
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params = model.parameters()
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for param in params:
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param.requires_grad = True
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optimizer = Adam(params, lr=0.01)
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loss_fn = CrossEntropyLoss()
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# Create training data: [a,a+1,a+2] → predict [a+1,a+2,a+3]
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train_data = []
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for start in range(7): # 0-6, so max is 6+2=8 < vocab_size
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x = np.array([[start, start+1, start+2]])
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targets = np.array([[start+1, start+2, start+3]])
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train_data.append((Tensor(x), Tensor(targets)))
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# Add multiple copies for training
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for _ in range(5):
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train_data.append((Tensor(x.copy()), Tensor(targets.copy())))
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print(f"Task: Given [a, a+1, a+2], predict [a+1, a+2, a+3]")
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print(f"Training on {len(train_data)} examples for 150 steps...")
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# Train
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for step in range(150):
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total_loss = 0
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# Shuffle data
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np.random.shuffle(train_data)
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for x, targets in train_data:
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# Zero gradients
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for param in params:
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param.grad = None
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# Forward
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logits = model.forward(x)
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logits_flat = logits.reshape(seq_len, vocab_size)
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targets_flat = targets.reshape(seq_len)
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loss = loss_fn.forward(logits_flat, targets_flat)
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# Backward
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loss.backward(np.ones_like(loss.data))
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# Update
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optimizer.step()
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total_loss += loss.data
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if (step + 1) % 50 == 0:
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avg_loss = total_loss / len(train_data)
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print(f" Step {step + 1}: Avg Loss = {avg_loss:.4f}")
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# Test on training examples
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print("\nTesting on training sequences:")
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correct_total = 0
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total_positions = 0
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test_cases = [
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([0, 1, 2], [1, 2, 3]),
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([1, 2, 3], [2, 3, 4]),
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([3, 4, 5], [4, 5, 6]),
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]
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for input_seq, expected_output in test_cases:
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test_x = Tensor(np.array([input_seq]))
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logits = model.forward(test_x)
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predictions = np.argmax(logits.data, axis=-1)
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print(f" Input: {input_seq} → Output: {predictions[0].tolist()} (Expected: {expected_output})")
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correct = np.sum(predictions[0] == np.array(expected_output))
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correct_total += correct
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total_positions += len(expected_output)
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accuracy = correct_total / total_positions * 100
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print(f"\nOverall Accuracy: {correct_total}/{total_positions} ({accuracy:.0f}%)")
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assert accuracy >= 50, f"Should achieve at least 50% accuracy, got {accuracy:.0f}%"
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print("✅ Sequence completion works!")
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return True
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def test_repeat_pattern():
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"""
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Task: Learn to repeat a 2-element pattern.
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Input: [1,2,1,2] → Output: [1,2,1,2]
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This tests if the model can learn periodic patterns.
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"""
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print("\n" + "="*70)
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print("TEST 4: Repeat Pattern (A,B,A,B)")
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print("="*70)
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vocab_size = 10
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embed_dim = 32
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num_layers = 2
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num_heads = 2
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seq_len = 8
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model = GPT(vocab_size, embed_dim, num_layers, num_heads)
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params = model.parameters()
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for param in params:
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param.requires_grad = True
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optimizer = Adam(params, lr=0.01)
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loss_fn = CrossEntropyLoss()
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# Create training data: repeating patterns [a,b,a,b,a,b,...]
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train_data = []
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for a in range(0, vocab_size, 2):
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for b in range(1, vocab_size, 2):
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if a != b:
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pattern = [a, b] * (seq_len // 2)
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x = np.array([pattern])
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targets = x.copy()
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train_data.append((Tensor(x), Tensor(targets)))
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# Add multiple copies
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for _ in range(3):
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train_data.append((Tensor(x.copy()), Tensor(targets.copy())))
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print(f"Task: Learn repeating 2-patterns [a,b,a,b,...]")
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print(f"Training on {len(train_data)} examples for 150 steps...")
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# Train
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for step in range(150):
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total_loss = 0
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np.random.shuffle(train_data)
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for x, targets in train_data[:30]: # Use subset for speed
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# Zero gradients
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for param in params:
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param.grad = None
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# Forward
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logits = model.forward(x)
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logits_flat = logits.reshape(seq_len, vocab_size)
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targets_flat = targets.reshape(seq_len)
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loss = loss_fn.forward(logits_flat, targets_flat)
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# Backward
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loss.backward(np.ones_like(loss.data))
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# Update
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optimizer.step()
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total_loss += loss.data
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if (step + 1) % 50 == 0:
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avg_loss = total_loss / 30
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print(f" Step {step + 1}: Avg Loss = {avg_loss:.4f}")
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# Test
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print("\nTesting on patterns:")
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correct_total = 0
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total_positions = 0
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test_cases = [
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[0, 1, 0, 1, 0, 1, 0, 1],
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[2, 3, 2, 3, 2, 3, 2, 3],
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[4, 5, 4, 5, 4, 5, 4, 5],
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]
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for pattern in test_cases:
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test_x = Tensor(np.array([pattern]))
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logits = model.forward(test_x)
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predictions = np.argmax(logits.data, axis=-1)
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print(f" Input: {pattern}")
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print(f" Output: {predictions[0].tolist()}")
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correct = np.sum(predictions[0] == np.array(pattern))
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correct_total += correct
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total_positions += len(pattern)
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accuracy = correct_total / total_positions * 100
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print(f"\nOverall Accuracy: {correct_total}/{total_positions} ({accuracy:.0f}%)")
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assert accuracy >= 40, f"Should achieve at least 40% accuracy, got {accuracy:.0f}%"
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print("✅ Pattern repetition works!")
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return True
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def run_all_tests():
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"""Run all simple pattern learning tests."""
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print("\n" + "="*70)
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print("TRANSFORMER SIMPLE PATTERN LEARNING TESTS")
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print("="*70)
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print("\nThese tests verify the transformer can learn VERY SIMPLE patterns.")
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print("If these fail, something is fundamentally wrong with learning.\n")
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tests = [
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("Constant Prediction", test_constant_prediction),
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("Copy Task", test_copy_task),
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("Sequence Completion", test_sequence_completion),
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("Repeat Pattern", test_repeat_pattern),
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]
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passed = 0
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failed = 0
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for test_name, test_func in tests:
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try:
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test_func()
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passed += 1
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except Exception as e:
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print(f"\n{'='*70}")
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print(f"❌ {test_name}: FAIL")
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print(f"Error: {e}")
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print(f"{'='*70}")
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import traceback
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traceback.print_exc()
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failed += 1
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print("\n" + "="*70)
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print("FINAL RESULTS")
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print("="*70)
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print(f"Tests passed: {passed}/{len(tests)}")
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print(f"Tests failed: {failed}/{len(tests)}")
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if failed == 0:
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print("\n🎉 ALL SIMPLE PATTERN TESTS PASSED!")
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print("The transformer can learn basic patterns.")
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print("Ready for more complex tasks like Shakespeare!")
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else:
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print(f"\n❌ {failed} test(s) failed")
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print("The transformer has issues with simple pattern learning.")
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print("="*70)
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return failed == 0
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if __name__ == "__main__":
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success = run_all_tests()
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sys.exit(0 if success else 1)
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