Enable flash attention

keras/api/_tf_keras/keras/config/__init__.py

@@ -13,6 +13,9 @@
 from keras.src.backend.config import set_image_data_format
 from keras.src.dtype_policies.dtype_policy import dtype_policy
 from keras.src.dtype_policies.dtype_policy import set_dtype_policy
+from keras.src.layers.attention.attention import disable_flash_attention
+from keras.src.layers.attention.attention import enable_flash_attention
+from keras.src.layers.attention.attention import is_flash_attention_enabled
 from keras.src.saving.serialization_lib import enable_unsafe_deserialization
 from keras.src.utils.backend_utils import set_backend
 from keras.src.utils.io_utils import disable_interactive_logging

keras/api/config/__init__.py

@@ -13,6 +13,9 @@
 from keras.src.backend.config import set_image_data_format
 from keras.src.dtype_policies.dtype_policy import dtype_policy
 from keras.src.dtype_policies.dtype_policy import set_dtype_policy
+from keras.src.layers.attention.attention import disable_flash_attention
+from keras.src.layers.attention.attention import enable_flash_attention
+from keras.src.layers.attention.attention import is_flash_attention_enabled
 from keras.src.saving.serialization_lib import enable_unsafe_deserialization
 from keras.src.utils.backend_utils import set_backend
 from keras.src.utils.io_utils import disable_interactive_logging

keras/src/backend/jax/nn.py

@@ -6,6 +6,9 @@
 import jax.numpy as jnp
 from jax import lax
 from jax import nn as jnn
+from jax.experimental.pallas.ops.tpu import (
+    flash_attention as flash_attention_tpu,
+)
 
 from keras.src import backend
 from keras.src.backend.common.backend_utils import (
@@ -1019,7 +1022,18 @@ def dot_product_attention(
             f"Received: query.shape={query.shape}, key.shape={key.shape}, "
             f"value.shape={value.shape}."
         )
-
+    is_tpu = jax.devices()[0].platform == "tpu"
+    if is_tpu and flash_attention:
+        # Use TPU-optimized flash attention from Pallas
+        return flash_attention_tpu(
+            query,
+            key,
+            value,
+            ab=bias,
+            segment_ids=mask,
+            causal=is_causal,
+            sm_scale=scale,
+        )
     # `dot_product_attention` is only available in jax>=0.4.31
     if hasattr(jax.nn, "dot_product_attention"):
         implementation = "cudnn" if flash_attention else "xla"
@@ -1040,7 +1054,6 @@ def dot_product_attention(
             "current JAX version. Please update it "
             "using `pip install -U jax jaxlib`."
         )
-
     # Ref: jax.nn.dot_product_attention
     # https://github.com/jax-ml/jax/blob/jax-v0.4.33/jax/_src/nn/functions.py#L886
     # Not support `query_seq_lengths` and `key_value_seq_lengths` args

keras/src/backend/torch/nn.py

@@ -954,7 +954,14 @@ def dot_product_attention(
                 scale=scale,
             )
     else:
+        if mask is not None:
+            mask = mask.contiguous()
         attention_output = torch.nn.functional.scaled_dot_product_attention(
-            query, key, value, attn_mask=mask, is_causal=is_causal, scale=scale
+            query.contiguous(),
+            key.contiguous(),
+            value.contiguous(),
+            attn_mask=mask,
+            is_causal=is_causal,
+            scale=scale,
         )
     return torch.transpose(attention_output, axis1, axis0)

keras/src/layers/attention/attention.py

@@ -1,6 +1,7 @@
 from keras.src import backend
 from keras.src import ops
 from keras.src.api_export import keras_export
+from keras.src.backend.common import global_state
 from keras.src.layers.layer import Layer
 
 
@@ -282,3 +283,49 @@ def get_config(self):
             "dropout": self.dropout,
         }
         return {**base_config, **config}
+
+
+@keras_export("keras.config.enable_flash_attention")
+def enable_flash_attention():
+    """Enable flash attention.
+
+    Flash attention offers performance optimization for attention layers,
+    making it especially useful for large language models (LLMs) that
+    benefit from faster and more memory-efficient attention computations.
+
+    Once enabled, supported layers like `MultiHeadAttention` will
+    use flash attention for faster computations.
+    """
+    global_state.set_global_attribute("flash_attention", True)
+
+
+@keras_export("keras.config.disable_flash_attention")
+def disable_flash_attention():
+    """Disable flash attention.
+
+    Flash attention offers performance optimization for attention layers,
+    making it especially useful for large language models (LLMs) that
+    benefit from faster and more memory-efficient attention computations.
+
+    Once disabled, supported layers like `MultiHeadAttention` will not
+    use flash attention for faster computations.
+    """
+    global_state.set_global_attribute("flash_attention", False)
+
+
+@keras_export("keras.config.is_flash_attention_enabled")
+def is_flash_attention_enabled():
+    """Checks whether flash attention is globally enabled in Keras.
+
+    Flash attention is a performance-optimized method for computing attention
+    in large models, such as transformers, allowing for faster and more
+    memory-efficient operations. This function checks the global Keras
+    configuration to determine if flash attention is enabled for compatible
+    layers (e.g., `MultiHeadAttention`).
+
+    Returns:
+        bool or None: Returns `True` if flash attention is enabled,
+                      `False` if it is disabled, and `None` if the global
+                      setting has not been defined.
+    """
+    return global_state.get_global_attribute("flash_attention", default=None)

keras/src/layers/attention/multi_head_attention.py

@@ -11,6 +11,7 @@
 from keras.src import regularizers
 from keras.src.api_export import keras_export
 from keras.src.layers.activations.softmax import Softmax
+from keras.src.layers.attention.attention import is_flash_attention_enabled
 from keras.src.layers.core.einsum_dense import EinsumDense
 from keras.src.layers.layer import Layer
 from keras.src.layers.regularization.dropout import Dropout
@@ -52,6 +53,9 @@ class MultiHeadAttention(Layer):
             feature dim (the query input's last dimension).
         attention_axes: axes over which the attention is applied. `None` means
             attention over all axes, but batch, heads, and features.
+        flash_attention: If unspecified, defaults to the global flash attention
+            configuration setting (which can be set via
+            `keras.config.enable_flash_attention().
         kernel_initializer: Initializer for dense layer kernels.
         bias_initializer: Initializer for dense layer biases.
         kernel_regularizer: Regularizer for dense layer kernels.
@@ -104,6 +108,7 @@ def __init__(
         use_bias=True,
         output_shape=None,
         attention_axes=None,
+        flash_attention=None,
         kernel_initializer="glorot_uniform",
         bias_initializer="zeros",
         kernel_regularizer=None,
@@ -131,6 +136,8 @@ def __init__(
         self._activity_regularizer = regularizers.get(activity_regularizer)
         self._kernel_constraint = constraints.get(kernel_constraint)
         self._bias_constraint = constraints.get(bias_constraint)
+        self._flash_attention = flash_attention or is_flash_attention_enabled()
+
         if isinstance(attention_axes, int):
             attention_axes = (attention_axes,)
         elif attention_axes and not isinstance(attention_axes, (list, tuple)):
@@ -392,7 +399,13 @@ def _masked_softmax(self, attention_scores, attention_mask=None):
         return self._softmax(attention_scores, mask=attention_mask)
 
     def _compute_attention(
-        self, query, key, value, attention_mask=None, training=None
+        self,
+        query,
+        key,
+        value,
+        return_attention_scores,
+        attention_mask=None,
+        training=None,
     ):
         """Applies Dot-product attention with query, key, value tensors.
 
@@ -415,9 +428,56 @@ def _compute_attention(
           attention_output: Multi-headed outputs of attention computation.
           attention_scores: Multi-headed attention weights.
         """
-        # Note: Applying scalar multiply at the smaller end of einsum improves
-        # XLA performance, but may introduce slight numeric differences in
-        # the Transformer attention head.
+
+        # Check for flash attention constraints
+        if self._flash_attention and return_attention_scores:
+            raise ValueError(
+                "Returning attention scores is not supported when flash "
+                "attention is enabled. Please disable flash attention to access"
+                " attention scores."
+            )
+        if self._flash_attention and self._dropout > 0.0:
+            raise ValueError(
+                "Dropout is not supported when flash "
+                "attention is enabled. Please set dropout to 0.0 to use "
+                "flash attention."
+            )
+
+        # Determine whether to use dot-product attention
+        use_dot_product_attention = not (
+            self._dropout > 0.0
+            or return_attention_scores
+            or (len(query.shape) != 4)
+        )
+
+        if use_dot_product_attention:
+            if attention_mask is not None:
+                # Ensure attention_mask has the correct shape for broadcasting
+                # Expected shape: [batch_size, num_heads, query_seq_len,
+                # key_seq_len]. This is because masked_softmax is not supported
+                # in JAX.
+                while len(attention_mask.shape) < 4:
+                    attention_mask = ops.expand_dims(
+                        attention_mask, axis=1
+                    )  # Add dimension for num_heads
+                if attention_mask.shape[1] != self._num_heads:
+                    attention_mask = ops.tile(
+                        attention_mask, [1, self._num_heads, 1, 1]
+                    )
+            # Directly compute the attention output using dot-product attention
+            attention_output = ops.dot_product_attention(
+                query=query,
+                key=key,
+                value=value,
+                mask=attention_mask,
+                scale=self._inverse_sqrt_key_dim,
+                is_causal=False,
+                flash_attention=self._flash_attention,
+            )
+            return attention_output, None
+
+        # Default behavior without flash attention, with explicit attention
+        # scores
         query = ops.multiply(
             query, ops.cast(self._inverse_sqrt_key_dim, query.dtype)
         )
@@ -426,13 +486,13 @@ def _compute_attention(
         # attention scores.
         attention_scores = ops.einsum(self._dot_product_equation, key, query)
 
+        # Apply the mask using the custom masked softmax
         attention_scores = self._masked_softmax(
             attention_scores, attention_mask
         )
 
-        # This is actually dropping out entire tokens to attend to, which might
-        # seem a bit unusual, but is taken from the original Transformer paper.
-        if self.dropout:
+        # Apply dropout to the attention scores if needed
+        if self._dropout > 0.0:
             final_attn_scores = self._dropout_layer(
                 attention_scores, training=training
             )
@@ -460,7 +520,6 @@ def call(
     ):
         if key is None:
             key = value
-
         attention_mask = self._compute_attention_mask(
             query,
             value,
@@ -470,9 +529,9 @@ def call(
             attention_mask=attention_mask,
             use_causal_mask=use_causal_mask,
         )
-
         #   N = `num_attention_heads`
         #   H = `size_per_head`
+
         # `query` = [B, T, N ,H]
         query = self._query_dense.call(query)
 
@@ -481,9 +540,13 @@ def call(
 
         # `value` = [B, S, N, H]
         value = self._value_dense.call(value)
-
         attention_output, attention_scores = self._compute_attention(
-            query, key, value, attention_mask, training
+            query,
+            key,
+            value,
+            return_attention_scores,
+            attention_mask,
+            training,
         )
         attention_output = self._output_dense.call(attention_output)