Examples of training bias

stack-info: PR: #84, branch: drisspg/stack/2

examples/learnable_bias.py

@@ -0,0 +1,247 @@
+import functools
+import logging
+import torch
+import torch.nn.functional as F
+import json
+import argparse
+from torch.nn.attention.flex_attention import flex_attention
+from typing import Callable, Dict, List, Tuple, Optional
+from enum import Enum, auto
+from torch.optim import Adam
+from torch.utils.data import DataLoader, TensorDataset
+from tqdm import tqdm
+
+logging.basicConfig(
+    level=logging.INFO, format="%(asctime)s - %(name)s - %(levelname)s - %(message)s"
+)
+logger = logging.getLogger(__name__)
+
+
+class BiasType(Enum):
+    RELATIVE_1D = "relative_1d"
+    ABSOLUTE_2D = "absolute_2d"
+    HEAD_SPECIFIC = "head_specific"
+    BATCH_HEAD = "batch_head"
+    MULTIPLICATIVE = "multiplicative"
+    LOCAL_WINDOW = "local_window"
+    GLOBAL_TOKENS = "global_tokens"
+    WEIRD = "weird"
+    OFFSET = "offset"
+
+
+class AttentionTrainer:
+
+    def __init__(
+        self,
+        batch_size: int = 8,
+        num_heads: int = 4,
+        seq_length: int = 256,
+        head_dim: int = 64,
+        device: str = "cuda",
+        dtype: torch.dtype = torch.float32,
+        window_size: int = 16,
+        learning_rate: float = 1e-1,
+    ):
+        self.B = batch_size
+        self.H = num_heads
+        self.S = seq_length
+        self.D = head_dim
+        self.W = window_size
+        self.device = device
+        self.dtype = dtype
+        self.lr = learning_rate
+        self.which_bias = torch.tensor(0, device=device)
+        self.offset = None
+
+        # Initialize bias generators and functions like in the original
+        self.bias_generators = {
+            BiasType.RELATIVE_1D: self._generate_relative_1d_bias,
+            BiasType.ABSOLUTE_2D: self._generate_absolute_2d_bias,
+            BiasType.HEAD_SPECIFIC: self._generate_head_specific_bias,
+            BiasType.BATCH_HEAD: self._generate_batch_head_bias,
+            BiasType.MULTIPLICATIVE: self._generate_multiplicative_bias,
+            BiasType.LOCAL_WINDOW: self._generate_local_window_bias,
+            BiasType.GLOBAL_TOKENS: self._generate_global_tokens_bias,
+            BiasType.WEIRD: self._generate_weird_bias,
+            BiasType.OFFSET: self._generate_offset_bias,
+        }
+
+        # Copy the bias application functions from the original
+        self.bias_functions = {
+            BiasType.RELATIVE_1D: self._apply_relative_1d_bias,
+            BiasType.ABSOLUTE_2D: self._apply_absolute_2d_bias,
+            BiasType.HEAD_SPECIFIC: self._apply_head_specific_bias,
+            BiasType.BATCH_HEAD: self._apply_batch_head_bias,
+            BiasType.MULTIPLICATIVE: self._apply_multiplicative_bias,
+            BiasType.LOCAL_WINDOW: self._apply_local_window_bias,
+            BiasType.GLOBAL_TOKENS: self._apply_global_tokens_bias,
+            BiasType.WEIRD: self._apply_weird_bias,
+            BiasType.OFFSET: self._apply_offset_bias,
+        }
+
+    def _generate_tensor(self, *size):
+        return torch.randn(
+            *size, device=self.device, dtype=self.dtype, requires_grad=True
+        )
+
+        # Bias Generators
+
+    def _generate_relative_1d_bias(self):
+        return self._generate_tensor(2 * self.S)
+
+    def _generate_absolute_2d_bias(self):
+        return self._generate_tensor(self.S, self.S)
+
+    def _generate_head_specific_bias(self):
+        return self._generate_tensor(self.H, self.S, self.S)
+
+    def _generate_batch_head_bias(self):
+        return self._generate_tensor(self.B, self.H, self.S, self.S)
+
+    def _generate_multiplicative_bias(self):
+        return self._generate_tensor(self.S)
+
+    def _generate_local_window_bias(self):
+        return self._generate_tensor(2 * self.W + 1)
+
+    def _generate_learned_pattern_bias(self):
+        return self._generate_tensor(self.H, self.D)
+
+    def _generate_global_tokens_bias(self):
+        return self._generate_tensor(self.S)
+
+    def _generate_weird_bias(self):
+        return self._generate_tensor(self.B, self.H, 4, self.S)
+
+    def _generate_offset_bias(self):
+        # Generate both the bias and offset tensors
+        bias = self._generate_tensor(self.S)
+        self.offset = torch.randint(0, self.S, (self.S,), device=self.device)
+        return bias
+
+    # Bias Application Functions
+    def _apply_relative_1d_bias(self, score, b, h, q_idx, kv_idx, bias):
+        return score + bias[torch.abs(q_idx - kv_idx)]
+
+    def _apply_absolute_2d_bias(self, score, b, h, q_idx, kv_idx, bias):
+        return score + bias[q_idx, kv_idx]
+
+    def _apply_head_specific_bias(self, score, b, h, q_idx, kv_idx, bias):
+        return score + bias[h, q_idx, kv_idx]
+
+    def _apply_batch_head_bias(self, score, b, h, q_idx, kv_idx, bias):
+        return score + bias[b, h, q_idx, kv_idx]
+
+    def _apply_multiplicative_bias(self, score, b, h, q_idx, kv_idx, bias):
+        return score * bias[q_idx]
+
+    def _apply_local_window_bias(self, score, b, h, q_idx, kv_idx, bias):
+        window_idx = torch.clamp(q_idx - kv_idx + self.W, 0, 2 * self.W)
+        return score + bias[window_idx]
+
+    def _apply_global_tokens_bias(self, score, b, h, q_idx, kv_idx, bias):
+        return score + bias[kv_idx]
+
+    def _apply_weird_bias(self, score, b, h, q_idx, kv_idx, bias):
+        return score + bias[b, h, self.which_bias, q_idx]
+
+    def _apply_offset_bias(self, score, b, h, q_idx, kv_idx, bias):
+        return score + bias[self.offset[q_idx]]
+
+    # Copy all the bias generator and application methods from the original class
+    # [Previous methods remain the same as in the original code]
+
+    def generate_dummy_data(self, num_samples: int) -> TensorDataset:
+        """Generate dummy training data."""
+        queries = torch.randn(
+            num_samples, self.B, self.H, self.S, self.D, device=self.device
+        )
+        keys = torch.randn(
+            num_samples, self.B, self.H, self.S, self.D, device=self.device
+        )
+        values = torch.randn(
+            num_samples, self.B, self.H, self.S, self.D, device=self.device
+        )
+
+        # Generate dummy targets (for this example, we'll try to predict specific patterns)
+        targets = torch.randn(
+            num_samples, self.B, self.H, self.S, self.D, device=self.device
+        )
+
+        return TensorDataset(queries, keys, values, targets)
+
+    def train(
+        self,
+        bias_type: BiasType = BiasType.RELATIVE_1D,
+        num_epochs: int = 10,
+        num_samples: int = 2,
+        batch_size: int = 4,
+    ):
+        """Train the attention model with the specified bias type."""
+        # Generate bias parameters
+        bias = self.bias_generators[bias_type]()
+        optimizer = Adam([bias], lr=self.lr)
+
+        # Generate dummy dataset
+        dataset = self.generate_dummy_data(num_samples)
+        dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)
+
+        # Create bias function closure
+        def bias_func(score, b, h, q_idx, kv_idx):
+            return self.bias_functions[bias_type](score, b, h, q_idx, kv_idx, bias)
+
+        # Compile the attention function
+        flex_compiled = torch.compile(
+            flex_attention, backend="eager", fullgraph=True, dynamic=False
+        )
+
+        # Training loop
+        for epoch in range(num_epochs):
+            total_loss = 0.0
+            with tqdm(dataloader, desc=f"Epoch {epoch+1}/{num_epochs}") as pbar:
+                for batch_idx, (q_batch, k_batch, v_batch, targets) in enumerate(pbar):
+                    q_batch.requires_grad_()
+                    optimizer.zero_grad()
+
+                    # Forward pass
+                    outputs = flex_compiled(
+                        q_batch[0], k_batch[0], v_batch[0], score_mod=bias_func
+                    )
+
+                    # Compute loss (MSE for this example)
+                    loss = F.mse_loss(outputs, targets[0])
+
+                    # Backward pass
+                    loss.backward()
+                    optimizer.step()
+
+                    total_loss += loss.item()
+                    pbar.set_postfix({"loss": f"{loss.item():.6f}"})
+
+            avg_loss = total_loss / len(dataloader)
+            logger.info(f"Epoch {epoch+1}/{num_epochs}, Average Loss: {avg_loss:.6f}")
+
+        return bias, avg_loss
+
+
+def main(
+    bias_type: BiasType = BiasType.RELATIVE_1D,
+    num_epochs: int = 10,
+    num_samples: int = 2,
+    batch_size: int = 4,
+):
+    trainer = AttentionTrainer()
+    trained_bias, final_loss = trainer.train(
+        bias_type=bias_type,
+        num_epochs=num_epochs,
+        num_samples=num_samples,
+        batch_size=batch_size,
+    )
+
+    logger.info(f"Final loss: {final_loss:.6f}")
+
+
+if __name__ == "__main__":
+    from jsonargparse import CLI
+
+    CLI(main)