Gpt2safetensors

keras_hub/src/models/gpt2/gpt2_tokenizer.py

@@ -71,3 +71,29 @@ def __init__(
             merges=merges,
             **kwargs,
         )
+
+    def save_assets(self, dir_path):
+        import json
+        import os
+        import shutil
+
+        # Save vocabulary.
+        if isinstance(self.vocabulary, str):
+            # If `vocabulary` is a file path, copy it.
+            shutil.copy(
+                self.vocabulary, os.path.join(dir_path, "vocabulary.json")
+            )
+        else:
+            # Otherwise, `vocabulary` is a dict. Save it to a JSON file.
+            with open(os.path.join(dir_path, "vocabulary.json"), "w") as f:
+                json.dump(self.vocabulary, f)
+
+        # Save merges.
+        if isinstance(self.merges, str):
+            # If `merges` is a file path, copy it.
+            shutil.copy(self.merges, os.path.join(dir_path, "merges.txt"))
+        else:
+            # Otherwise,  `merges` is a list. Save it to a text file.
+            with open(os.path.join(dir_path, "merges.txt"), "w") as f:
+                for merge in self.merges:
+                    f.write(f"{merge}\n")

keras_hub/src/utils/transformers/export/gemma.py

@@ -1,9 +1,10 @@
 import keras.ops as ops
+import transformers
 
 
 def get_gemma_config(backbone):
     token_embedding_layer = backbone.get_layer("token_embedding")
-    hf_config = {
+    hf_config_dict = {
         "vocab_size": backbone.vocabulary_size,
         "num_hidden_layers": backbone.num_layers,
         "num_attention_heads": backbone.num_query_heads,
@@ -18,13 +19,14 @@ def get_gemma_config(backbone):
         "eos_token_id": 1,
         "model_type": "gemma",
     }
+    hf_config = transformers.GemmaConfig(**hf_config_dict)
     return hf_config
 
 
 def get_gemma_weights_map(backbone, include_lm_head=False):
     weights_dict = {}
 
-    # Map token embedding
+    # Map token embeddings
     token_embedding_layer = backbone.get_layer("token_embedding")
     weights_dict["model.embed_tokens.weight"] = token_embedding_layer.weights[0]
 

keras_hub/src/utils/transformers/export/gpt2.py

@@ -0,0 +1,146 @@
+import tensorflow as tf
+import transformers
+
+
+def get_gpt2_config(keras_model):
+    """Convert Keras GPT-2 config to Hugging Face GPT2Config."""
+    return transformers.GPT2Config(
+        vocab_size=keras_model.vocabulary_size,
+        n_positions=keras_model.max_sequence_length,
+        n_embd=keras_model.hidden_dim,
+        n_layer=keras_model.num_layers,
+        n_head=keras_model.num_heads,
+        n_inner=keras_model.intermediate_dim,
+        activation_function="gelu_new",
+        resid_pdrop=0.1,
+        embd_pdrop=0.1,
+        attn_pdrop=0.1,
+        layer_norm_epsilon=1e-5,
+        initializer_range=0.02,
+        summary_type="cls_index",
+        summary_use_proj=True,
+        summary_activation=None,
+        summary_proj_to_labels=True,
+        summary_first_dropout=0.1,
+        scale_attn_weights=True,
+        use_cache=True,
+        bos_token_id=50256,
+        eos_token_id=50256,
+    )
+
+
+def get_gpt2_weights_map(keras_model, include_lm_head=False):
+    """Create a weights map for a given GPT-2 model."""
+    weights_map = {}
+
+    # Token and position embeddings
+    weights_map["transformer.wte.weight"] = keras_model.get_layer(
+        "token_embedding"
+    ).embeddings
+    weights_map["transformer.wpe.weight"] = keras_model.get_layer(
+        "position_embedding"
+    ).position_embeddings
+
+    for i in range(keras_model.num_layers):
+        # Attention weights
+        q_w = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer._query_dense.kernel
+        k_w = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer._key_dense.kernel
+        v_w = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer._value_dense.kernel
+        q_b = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer._query_dense.bias
+        k_b = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer._key_dense.bias
+        v_b = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer._value_dense.bias
+
+        q_w = tf.reshape(q_w, (keras_model.hidden_dim, keras_model.hidden_dim))
+        k_w = tf.reshape(k_w, (keras_model.hidden_dim, keras_model.hidden_dim))
+        v_w = tf.reshape(v_w, (keras_model.hidden_dim, keras_model.hidden_dim))
+
+        c_attn_w = tf.concat([q_w, k_w, v_w], axis=-1)
+        weights_map[f"transformer.h.{i}.attn.c_attn.weight"] = c_attn_w
+
+        q_b = tf.reshape(q_b, [-1])
+        k_b = tf.reshape(k_b, [-1])
+        v_b = tf.reshape(v_b, [-1])
+
+        c_attn_b = tf.concat([q_b, k_b, v_b], axis=-1)
+        weights_map[f"transformer.h.{i}.attn.c_attn.bias"] = c_attn_b
+
+        # Attention projection
+        c_proj_w = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer._output_dense.kernel
+        c_proj_w = tf.reshape(
+            c_proj_w, (keras_model.hidden_dim, keras_model.hidden_dim)
+        )
+        weights_map[f"transformer.h.{i}.attn.c_proj.weight"] = c_proj_w
+        weights_map[f"transformer.h.{i}.attn.c_proj.bias"] = (
+            keras_model.get_layer(
+                f"transformer_layer_{i}"
+            )._self_attention_layer._output_dense.bias
+        )
+
+        # Layer norms
+        weights_map[f"transformer.h.{i}.ln_1.weight"] = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer_norm.gamma
+        weights_map[f"transformer.h.{i}.ln_1.bias"] = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._self_attention_layer_norm.beta
+        weights_map[f"transformer.h.{i}.ln_2.weight"] = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._feedforward_layer_norm.gamma
+        weights_map[f"transformer.h.{i}.ln_2.bias"] = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._feedforward_layer_norm.beta
+
+        # MLP
+        c_fc_w = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._feedforward_intermediate_dense.kernel
+        weights_map[f"transformer.h.{i}.mlp.c_fc.weight"] = c_fc_w
+        weights_map[f"transformer.h.{i}.mlp.c_fc.bias"] = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._feedforward_intermediate_dense.bias
+        c_proj_w_mlp = keras_model.get_layer(
+            f"transformer_layer_{i}"
+        )._feedforward_output_dense.kernel
+        weights_map[f"transformer.h.{i}.mlp.c_proj.weight"] = c_proj_w_mlp
+        weights_map[f"transformer.h.{i}.mlp.c_proj.bias"] = (
+            keras_model.get_layer(
+                f"transformer_layer_{i}"
+            )._feedforward_output_dense.bias
+        )
+
+    # Final layer norm
+    weights_map["transformer.ln_f.weight"] = keras_model.get_layer(
+        "layer_norm"
+    ).gamma
+    weights_map["transformer.ln_f.bias"] = keras_model.get_layer(
+        "layer_norm"
+    ).beta
+
+    if include_lm_head:
+        # lm_head is tied to token embeddings
+        weights_map["lm_head.weight"] = weights_map["transformer.wte.weight"]
+
+    return weights_map
+
+
+def get_gpt2_tokenizer_config(tokenizer):
+    return {
+        "model_type": "gpt2",
+        "bos_token": "<|endoftext|>",
+        "eos_token": "<|endoftext|>",
+        "unk_token": "<|endoftext|>",
+    }

keras_hub/src/utils/transformers/export/gpt2_test.py

@@ -0,0 +1,122 @@
+import os
+import shutil
+import sys
+import tempfile
+from os.path import abspath
+from os.path import dirname
+
+# import keras
+import numpy as np
+import tensorflow as tf
+
+# import torch
+from absl.testing import parameterized
+from transformers import AutoModelForCausalLM
+from transformers import AutoTokenizer
+
+# Add the project root to the Python path.
+sys.path.insert(
+    0, dirname(dirname(dirname(dirname(dirname(abspath(__file__))))))
+)
+
+from keras_hub.src.models.gpt2.gpt2_causal_lm import GPT2CausalLM
+from keras_hub.src.utils.transformers.export.hf_exporter import (
+    export_to_safetensors,
+)
+
+
+def to_numpy(x):
+    # Torch tensor
+    if hasattr(x, "detach") and hasattr(x, "cpu"):
+        return x.detach().cpu().numpy()
+
+    # TF tensor
+    if hasattr(x, "numpy"):
+        return x.numpy()
+
+    # Numpy
+    if isinstance(x, np.ndarray):
+        return x
+
+    # KerasTensor or ragged wrapper → convert to TF → numpy
+    try:
+        import tensorflow as tf
+
+        return tf.convert_to_tensor(x).numpy()
+    except Exception:
+        pass
+
+    raise TypeError(f"Cannot convert value of type {type(x)} to numpy")
+
+
+class GPT2ExportTest(tf.test.TestCase, parameterized.TestCase):
+    @parameterized.named_parameters(
+        ("gpt2_base_en", "gpt2_base_en"),
+    )
+    def test_gpt2_export(self, preset):
+        # Create a temporary directory to save the converted model.
+        temp_dir = tempfile.mkdtemp()
+        output_path = os.path.join(temp_dir, preset)
+
+        # Load Keras model.
+        keras_model = GPT2CausalLM.from_preset(preset)
+
+        # Export to Hugging Face format.
+        export_to_safetensors(keras_model, output_path)
+
+        # Load the converted model with Hugging Face Transformers.
+        hf_model = AutoModelForCausalLM.from_pretrained(output_path)
+        hf_tokenizer = AutoTokenizer.from_pretrained(output_path)
+
+        # Assertions for config parameters.
+        self.assertEqual(
+            keras_model.backbone.hidden_dim, hf_model.config.hidden_size
+        )
+        self.assertEqual(
+            keras_model.backbone.num_layers, hf_model.config.n_layer
+        )
+        self.assertEqual(keras_model.backbone.num_heads, hf_model.config.n_head)
+        self.assertEqual(
+            keras_model.backbone.intermediate_dim, hf_model.config.n_inner
+        )
+        self.assertEqual(
+            keras_model.backbone.vocabulary_size, hf_model.config.vocab_size
+        )
+        self.assertEqual(
+            keras_model.backbone.max_sequence_length,
+            hf_model.config.n_positions,
+        )
+
+        # Test logits.
+        prompt = "Hello, my name is"
+        token_ids = tf.constant(
+            keras_model.preprocessor.tokenizer(tf.constant([prompt]))
+        )
+        padding_mask = tf.ones_like(token_ids, dtype=tf.int32)
+        keras_inputs = {"token_ids": token_ids, "padding_mask": padding_mask}
+        keras_logits = keras_model(keras_inputs)
+
+        hf_inputs = hf_tokenizer(prompt, return_tensors="pt")
+        hf_logits = hf_model(**hf_inputs).logits
+        print(hf_logits)
+
+        # Compare logits.
+        # Keras logits are (batch_size, sequence_length, vocab_size)
+        # HF logits are (batch_size, sequence_length, vocab_size)
+        # We need to convert Keras logits to numpy and then to torch tensor
+        # for comparison.
+
+        # Convert Keras logits (TF) -> numpy
+        keras_logits_np = to_numpy(keras_logits)
+
+        # Convert HF logits (Torch, possibly MPS) -> numpy
+        hf_logits_np = to_numpy(hf_logits)
+
+        self.assertAllClose(keras_logits_np, hf_logits_np, atol=1e-3, rtol=1e-3)
+
+        # Clean up the temporary directory.
+        shutil.rmtree(temp_dir)
+
+
+if __name__ == "__main__":
+    tf.test.main()