Generated GPT_OSS model files through porter script.

keras_hub/api/models/__init__.py

@@ -322,6 +322,18 @@
 from keras_hub.src.models.gpt_neo_x.gpt_neo_x_tokenizer import (
     GPTNeoXTokenizer as GPTNeoXTokenizer,
 )
+from keras_hub.src.models.gpt_oss.gpt_oss_backbone import (
+    GptOssBackbone as GptOssBackbone,
+)
+from keras_hub.src.models.gpt_oss.gpt_oss_causal_lm import (
+    GptOssCausalLM as GptOssCausalLM,
+)
+from keras_hub.src.models.gpt_oss.gpt_oss_causal_lm_preprocessor import (
+    GptOssCausalLMPreprocessor as GptOssCausalLMPreprocessor,
+)
+from keras_hub.src.models.gpt_oss.gpt_oss_tokenizer import (
+    GptOssTokenizer as GptOssTokenizer,
+)
 from keras_hub.src.models.hgnetv2.hgnetv2_backbone import (
     HGNetV2Backbone as HGNetV2Backbone,
 )

keras_hub/api/tokenizers/__init__.py

@@ -47,6 +47,9 @@
 from keras_hub.src.models.gpt_neo_x.gpt_neo_x_tokenizer import (
     GPTNeoXTokenizer as GPTNeoXTokenizer,
 )
+from keras_hub.src.models.gpt_oss.gpt_oss_tokenizer import (
+    GptOssTokenizer as GptOssTokenizer,
+)
 from keras_hub.src.models.llama.llama_tokenizer import (
     LlamaTokenizer as LlamaTokenizer,
 )

keras_hub/src/models/gpt_oss/__init__.py

@@ -0,0 +1,19 @@
+# Copyright 2024 The KerasHub Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from keras_hub.src.models.gpt_oss.gpt_oss_backbone import GptOssBackbone
+from keras_hub.src.models.gpt_oss.gpt_oss_presets import backbone_presets
+from keras_hub.src.utils.preset_utils import register_presets
+
+register_presets(backbone_presets, GptOssBackbone)

keras_hub/src/models/gpt_oss/gpt_oss_attention.py

@@ -0,0 +1,334 @@
+# Copyright 2024 The KerasHub Authors
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     https://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import math
+
+import keras
+from keras import ops
+
+from keras_hub.src.layers.modeling.rotary_embedding import RotaryEmbedding
+from keras_hub.src.utils.keras_utils import clone_initializer
+
+
+class GptOssAttention(keras.layers.Layer):
+    """A cached attention layer with sliding window and sink tokens.
+
+    This layer implements the attention mechanism described in the GPT-OSS
+    paper. It includes grouped-query attention, rotary position embeddings,
+    sliding window attention, and sink tokens for improved performance on
+    long sequences.
+
+    Args:
+        num_query_heads (int): The number of query attention heads.
+        num_key_value_heads (int): The number of key and value attention
+            heads.
+        rope_max_wavelength (int, optional): The maximum wavelength for the
+            rotary position embedding. Defaults to 10000.
+        rope_scaling_factor (float, optional): The scaling factor for the
+            rotary position embedding. Defaults to 1.0.
+        kernel_initializer (str, optional): The initializer for the kernel
+            weights. Defaults to "glorot_uniform".
+        sliding_window (int, optional): The size of the sliding window.
+            Defaults to 4096.
+        dropout (float, optional): The dropout rate. Defaults to 0.
+        head_dim (int, optional): Head dimension for attention. If None,
+            calculated as hidden_dim // num_query_heads. Defaults to None.
+    """
+
+    def __init__(
+        self,
+        num_query_heads,
+        num_key_value_heads,
+        rope_max_wavelength=10000,
+        rope_scaling_factor=1.0,
+        kernel_initializer="glorot_uniform",
+        sliding_window=4096,
+        dropout=0,
+        head_dim=None,  # Accept but handle head_dim parameter for HF compatibility
+        **kwargs,
+    ):
+        super().__init__(**kwargs)
+        self.num_query_heads = num_query_heads
+        self.num_key_value_heads = num_key_value_heads
+        self.sliding_window = sliding_window
+        self.dropout = dropout
+        self.head_dim = head_dim  # Store for use in build()
+        self.rope_max_wavelength = rope_max_wavelength  # Needed for RotaryEmbedding
+        self.rope_scaling_factor = rope_scaling_factor
+
+        self.num_key_value_groups = num_query_heads // num_key_value_heads
+
+        self._kernel_initializer = keras.initializers.get(
+            clone_initializer(kernel_initializer)
+        )
+
+    def build(self, inputs_shape):
+        # Einsum variables:
+        # b = batch size
+        # q = query length
+        # k = key/value length
+        # m = model dim
+        # u = num query heads
+        # v = num key/value heads
+        # h = head dim
+        self._hidden_dim = inputs_shape[-1]
+        # Use HF head_dim if provided, otherwise calculate dynamically
+        if self.head_dim is not None:
+            self._head_dim = self.head_dim
+            print(f"Using HF head_dim: {self._head_dim}")
+        else:
+            # Calculate head_dim dynamically based on the model configuration
+            self._head_dim = self._hidden_dim // self.num_query_heads
+            print(f"Calculated head_dim: {self._head_dim}")
+        self._inv_norm_factor = 1.0 / math.sqrt(self._head_dim)
+
+        # Calculate rotary dimension -
+        # use the largest even number <= head_dim
+        self._rotary_dim = (self._head_dim // 2) * 2
+
+        self.query_dense = keras.layers.EinsumDense(
+            equation="bqm,muh->bquh",
+            output_shape=(None, self.num_query_heads, self._head_dim),
+            bias_axes="uh",
+            kernel_initializer=self._kernel_initializer,
+            bias_initializer="zeros",
+            dtype=self.dtype_policy,
+            name="query",
+        )
+        self.query_dense.build(inputs_shape)
+
+        self.key_dense = keras.layers.EinsumDense(
+            equation="bkm,mvh->bkvh",
+            output_shape=(
+                None,
+                self.num_key_value_heads,
+                self._head_dim,
+            ),
+            bias_axes="vh",
+            kernel_initializer=self._kernel_initializer,
+            bias_initializer="zeros",
+            dtype=self.dtype_policy,
+            name="key",
+        )
+        self.key_dense.build(inputs_shape)
+
+        self.value_dense = keras.layers.EinsumDense(
+            equation="bkm,mvh->bkvh",
+            output_shape=(
+                None,
+                self.num_key_value_heads,
+                self._head_dim,
+            ),
+            bias_axes="vh",
+            kernel_initializer=self._kernel_initializer,
+            bias_initializer="zeros",
+            dtype=self.dtype_policy,
+            name="value",
+        )
+        self.value_dense.build(inputs_shape)
+
+        self.dropout_layer = keras.layers.Dropout(
+            rate=self.dropout,
+            dtype=self.dtype_policy,
+        )
+
+        self.output_dense = keras.layers.EinsumDense(
+            equation="bquh,uhm->bqm",
+            output_shape=(None, self._hidden_dim),
+            bias_axes="m",
+            kernel_initializer=self._kernel_initializer,
+            bias_initializer="zeros",
+            dtype=self.dtype_policy,
+            name="attention_output",
+        )
+        self.output_dense.build(
+            (None, None, self.num_query_heads, self._head_dim)
+        )
+
+        self.rotary_embedding_layer = RotaryEmbedding(
+            max_wavelength=self.rope_max_wavelength,
+            scaling_factor=self.rope_scaling_factor,
+            dtype=self.dtype_policy,
+        )
+
+        self.sinks = self.add_weight(
+            shape=(self.num_query_heads,),
+            initializer="random_normal",
+            dtype=self.dtype,
+            name="sinks",
+        )
+
+        self._dot_product_equation = "bquh,bkuh->buqk"
+        self._combine_equation = "buqk,bkuh->bquh"
+
+        self.built = True
+
+    def call(
+        self,
+        hidden_states,
+        attention_mask=None,
+        cache=None,
+        cache_update_index=None,
+        training=None,
+    ):
+        start_index = (
+            cache_update_index if cache_update_index is not None else 0
+        )
+
+        query = self.query_dense(hidden_states)
+
+        # Compute RoPE for queries (only apply to first _rotary_dim dimensions)
+        # Ensure RoPE is applied consistently with HuggingFace implementation
+        if self._rotary_dim < self._head_dim:
+            query_rot = query[..., : self._rotary_dim]
+            query_rot = self.rotary_embedding_layer(
+                query_rot, start_index=start_index
+            )
+            query = ops.concatenate(
+                [query_rot, query[..., self._rotary_dim :]], axis=-1
+            )
+        else:
+            query = self.rotary_embedding_layer(query, start_index=start_index)
+
+        def _compute_key_value(x):
+            key, value = self.key_dense(x), self.value_dense(x)
+            # Compute RoPE for keys (only apply to first _rotary_dim dimensions)
+            if self._rotary_dim < self._head_dim:
+                key_rot = key[..., : self._rotary_dim]
+                key_rot = self.rotary_embedding_layer(
+                    key_rot, start_index=start_index
+                )
+                key = ops.concatenate(
+                    [key_rot, key[..., self._rotary_dim :]], axis=-1
+                )
+            else:
+                key = self.rotary_embedding_layer(key, start_index=start_index)
+            return key, value
+
+        if cache is not None:
+            key_cache = cache[:, 0, ...]
+            value_cache = cache[:, 1, ...]
+            if cache_update_index is None:
+                key = key_cache
+                value = value_cache
+            else:
+                key_update, value_update = _compute_key_value(hidden_states)
+                start = [0, cache_update_index, 0, 0]
+                key = ops.slice_update(key_cache, start, key_update)
+                value = ops.slice_update(value_cache, start, value_update)
+                cache = ops.stack((key, value), axis=1)
+        else:
+            if cache_update_index is not None:
+                raise ValueError(
+                    "`cache_update_index` should not be set if `cache` is "
+                    f"`None`. Received: cache={cache}, "
+                    f"cache_update_index={cache_update_index}"
+                )
+            key, value = _compute_key_value(hidden_states)
+
+        # [batch_shape, seq_len, num_key_value_heads, head_dim]
+        # -> [batch_shape, seq_len, num_heads, head_dim]
+        key = ops.repeat(key, repeats=self.num_key_value_groups, axis=2)
+        value = ops.repeat(value, repeats=self.num_key_value_groups, axis=2)
+
+        attention_output = self._compute_attention(
+            query, key, value, attention_mask
+        )
+
+        attention_output = self.dropout_layer(
+            attention_output, training=training
+        )
+
+        attention_output = self.output_dense(attention_output)
+
+        if cache is not None:
+            return attention_output, cache
+        return attention_output
+
+    def _compute_attention(self, query, key, value, attention_mask=None):
+        attention_scores = ops.einsum(self._dot_product_equation, query, key)
+        attention_scores = ops.multiply(
+            attention_scores,
+            ops.cast(self._inv_norm_factor, self.compute_dtype),
+        )
+
+        # Apply sliding window mask if specified
+        if self.sliding_window is not None and self.sliding_window > 0:
+            seq_len = ops.shape(attention_scores)[-1]
+            # Create sliding window mask
+            positions = ops.arange(seq_len)
+            sliding_mask = ops.abs(positions[:, None] - positions[None, :]) > self.sliding_window
+            # Convert to large negative value for masking
+            if self.compute_dtype == "float32":
+                sliding_adder = ops.cast(-1e9, self.compute_dtype)
+            else:
+                sliding_adder = ops.cast(-1e4, self.compute_dtype)
+            attention_scores = ops.where(sliding_mask[None, None, :, :], sliding_adder, attention_scores)
+
+        if attention_mask is not None:
+            # The mask is a boolean tensor, True for positions to be masked.
+            # We add a large negative number to the masked positions.
+            # Use a large negative value for masking
+            if self.compute_dtype == "float32":
+                adder = ops.cast(-1e9, self.compute_dtype)
+            else:
+                adder = ops.cast(-1e4, self.compute_dtype)
+            attention_scores = ops.where(
+                attention_mask[:, None, :, :], adder, attention_scores
+            )
+
+        # Handle sink tokens by concatenating them to the logits.
+        b = ops.shape(query)[0]
+        q = ops.shape(query)[1]
+        sinks = ops.reshape(self.sinks, (1, self.num_query_heads, 1, 1))
+        sinks = ops.broadcast_to(sinks, (b, self.num_query_heads, q, 1))
+        # attention_scores shape: [b, num_heads, q, k]
+        # sinks shape: [b, num_heads, q, 1]
+        # We need to concatenate along the last dimension
+        combined_logits = ops.concatenate([attention_scores, sinks], axis=-1)
+
+        # Stabilize logits before softmax for numerical stability.
+        max_logits = ops.max(combined_logits, axis=-1, keepdims=True)
+        max_logits = ops.stop_gradient(max_logits)
+        combined_logits = combined_logits - max_logits
+
+        probs = ops.softmax(combined_logits, axis=-1)
+
+        # Remove the sink probabilities before computing the output.
+        attention_scores = probs[..., :-1]
+        attention_scores = ops.cast(attention_scores, self.compute_dtype)
+
+        attention_output = ops.einsum(
+            self._combine_equation, attention_scores, value
+        )
+
+        return attention_output
+
+    def get_config(self):
+        config = super().get_config()
+        config.update(
+            {
+                "num_query_heads": self.num_query_heads,
+                "num_key_value_heads": self.num_key_value_heads,
+                "rope_max_wavelength": self.rope_max_wavelength,
+                "rope_scaling_factor": self.rope_scaling_factor,
+                "kernel_initializer": keras.initializers.serialize(
+                    self._kernel_initializer
+                ),
+                "sliding_window": self.sliding_window,
+                "dropout": self.dropout,
+                "head_dim": self.head_dim,
+            }
+        )
+        return config