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https://github.com/MLSysBook/TinyTorch.git
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d5161e7191
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.
279 lines
9.2 KiB
Python
279 lines
9.2 KiB
Python
#!/usr/bin/env python3
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"""
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Debug Copy Task Failure
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The copy task failed while other tasks succeeded. This script investigates why.
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Hypothesis:
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1. The causal mask prevents looking at future tokens
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2. For position i to predict token i, it can only see tokens 0..i-1
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3. This makes copying impossible in an autoregressive model!
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Solution: We should test "shifted" copy where we predict the NEXT token.
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Input: [1, 2, 3, 4] → Predict: [2, 3, 4, ?]
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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_copy_with_causal_mask_visualization():
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"""Visualize what the model sees with causal masking."""
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print("\n" + "="*70)
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print("Understanding Causal Masking in Copy Task")
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print("="*70)
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print("\nInput sequence: [1, 2, 3, 4]")
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print("Target (copy): [1, 2, 3, 4]")
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print("\nWhat each position sees (with causal mask):")
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print(" Position 0: sees [] → must predict 1 (impossible!)")
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print(" Position 1: sees [1] → must predict 2")
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print(" Position 2: sees [1,2] → must predict 3")
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print(" Position 3: sees [1,2,3] → must predict 4")
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print("\n❌ Position 0 CANNOT predict correctly - it sees nothing!")
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print("\n✅ CORRECT task: Predict NEXT token (shifted prediction)")
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print(" Position 0: sees [1] → predict 2")
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print(" Position 1: sees [1,2] → predict 3")
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print(" Position 2: sees [1,2,3] → predict 4")
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print(" Position 3: sees [1,2,3,4] → predict 5 (or padding)")
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def test_next_token_prediction():
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"""
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Test the CORRECT task for autoregressive models: predict next token.
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Input: [1,2,3] → Predict: [2,3,4] (shifted by 1)
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"""
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print("\n" + "="*70)
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print("TEST: Next Token Prediction (Autoregressive Copy)")
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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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print("\nTask: Given [a,b,c,d], predict [b,c,d,e]")
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print("This is the standard autoregressive task!\n")
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# Create training data: targets are inputs shifted by 1
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num_examples = 30
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train_data = []
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for _ in range(num_examples):
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# Create sequence [a, a+1, a+2, a+3]
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start = np.random.randint(0, vocab_size - seq_len)
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x = np.array([[start + i for i in range(seq_len)]])
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# Target is [a+1, a+2, a+3, a+4]
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targets = np.array([[start + i + 1 for i in range(seq_len)]])
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train_data.append((Tensor(x), Tensor(targets)))
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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 sequences
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print("\nTesting on NEW sequences:")
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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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start = np.random.randint(0, vocab_size - seq_len)
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test_x = Tensor(np.array([[start + j for j in range(seq_len)]]))
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expected = np.array([start + j + 1 for j in range(seq_len)])
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logits = model.forward(test_x)
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predictions = np.argmax(logits.data, axis=-1)[0]
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print(f" Input: {test_x.data[0]} → Output: {predictions} (Expected: {expected})")
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correct = np.sum(predictions == expected)
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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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if accuracy >= 75:
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print("✅ Next token prediction works perfectly!")
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return True
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else:
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print(f"⚠️ Accuracy is {accuracy:.0f}%, lower than expected")
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return False
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def test_memorization_vs_generalization():
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"""
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Test if the model memorizes specific sequences or learns the pattern.
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"""
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print("\n" + "="*70)
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print("TEST: Memorization vs Generalization")
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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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# Train on ONLY sequences starting with 0, 2, 4
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train_starts = [0, 2, 4]
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train_data = []
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for start in train_starts:
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x = np.array([[start, start+1, start+2, start+3]])
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targets = np.array([[start+1, start+2, start+3, start+4]])
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# Add multiple copies
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for _ in range(10):
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train_data.append((Tensor(x.copy()), Tensor(targets.copy())))
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print(f"\n1. Training ONLY on sequences: [0,1,2,3], [2,3,4,5], [4,5,6,7]")
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print(f" (Total: {len(train_data)} examples)")
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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:
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for param in params:
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param.grad = None
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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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loss.backward(np.ones_like(loss.data))
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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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print(f" Step {step + 1}: Avg Loss = {total_loss / len(train_data):.4f}")
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# Test on training data
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print("\n2. Testing on TRAINING sequences:")
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for start in train_starts:
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test_x = Tensor(np.array([[start, start+1, start+2, start+3]]))
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expected = np.array([start+1, start+2, start+3, start+4])
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logits = model.forward(test_x)
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predictions = np.argmax(logits.data, axis=-1)[0]
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match = "✅" if np.array_equal(predictions, expected) else "❌"
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print(f" {match} Input: [{start},{start+1},{start+2},{start+3}] → {predictions} (Expected: {expected})")
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# Test on unseen sequences
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print("\n3. Testing on UNSEEN sequences (generalization test):")
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test_starts = [1, 3, 5]
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correct_total = 0
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total_positions = 0
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for start in test_starts:
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test_x = Tensor(np.array([[start, start+1, start+2, start+3]]))
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expected = np.array([start+1, start+2, start+3, start+4])
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logits = model.forward(test_x)
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predictions = np.argmax(logits.data, axis=-1)[0]
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correct = np.sum(predictions == expected)
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correct_total += correct
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total_positions += seq_len
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match = "✅" if np.array_equal(predictions, expected) else "❌"
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print(f" {match} Input: [{start},{start+1},{start+2},{start+3}] → {predictions} (Expected: {expected})")
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accuracy = correct_total / total_positions * 100
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print(f"\n4. Generalization Accuracy: {correct_total}/{total_positions} ({accuracy:.0f}%)")
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if accuracy >= 75:
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print("✅ Model GENERALIZED the pattern!")
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elif accuracy >= 25:
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print("⚠️ Model PARTIALLY generalized")
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else:
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print("❌ Model just MEMORIZED training examples")
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return accuracy >= 50
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if __name__ == "__main__":
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print("\n" + "="*70)
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print("DEBUGGING COPY TASK FAILURE")
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print("="*70)
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test_copy_with_causal_mask_visualization()
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success1 = test_next_token_prediction()
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success2 = test_memorization_vs_generalization()
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print("\n" + "="*70)
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print("CONCLUSIONS")
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print("="*70)
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if success1 and success2:
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print("\n✅ The transformer works correctly!")
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print("\nKey insights:")
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print("1. Autoregressive models predict NEXT token, not same token")
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print("2. The model can learn and generalize patterns")
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print("3. The 'copy task' failure was due to incorrect task formulation")
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print("\n🚀 Ready for Shakespeare training!")
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else:
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print("\n⚠️ Some issues found:")
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if not success1:
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print(" - Next token prediction issues")
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if not success2:
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print(" - Generalization issues (memorization)")
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print("="*70)
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