Add T5 GGUF loading support

docs/source/en/gguf.md

@@ -85,6 +85,7 @@ For now the supported model architectures are the architectures that have been v
 - StableLM
 - GPT2
 - Starcoder2
+- T5
 
 ## Example usage
 

src/transformers/integrations/ggml.py

@@ -21,11 +21,11 @@
 from array import array
 
 import numpy as np
-from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers
-from tokenizers.models import BPE
+from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
+from tokenizers.models import BPE, Unigram
 
 from .. import AddedToken
-from ..convert_slow_tokenizer import GPT2Converter, LlamaConverter, Qwen2Converter
+from ..convert_slow_tokenizer import GPT2Converter, LlamaConverter, Qwen2Converter, T5Converter
 from ..utils import logging
 from ..utils.logging import tqdm
 
@@ -148,6 +148,51 @@
         ".output.": ".lm_head.",
         "output_norm": "ln_f",
     },
+    "t5": {
+        "token_embd": "shared",
+        "dec.blk.{bid}.attn_q": "decoder.block.{bid}.layer.0.SelfAttention.q",
+        "dec.blk.{bid}.attn_k": "decoder.block.{bid}.layer.0.SelfAttention.k",
+        "dec.blk.{bid}.attn_v": "decoder.block.{bid}.layer.0.SelfAttention.v",
+        "dec.blk.{bid}.attn_o": "decoder.block.{bid}.layer.0.SelfAttention.o",
+        "dec.blk.{bid}.attn_rel_b": "decoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias",
+        "dec.blk.{bid}.attn_norm": "decoder.block.{bid}.layer.0.layer_norm",
+        "dec.blk.{bid}.cross_attn_q": "decoder.block.{bid}.layer.1.EncDecAttention.q",
+        "dec.blk.{bid}.cross_attn_k": "decoder.block.{bid}.layer.1.EncDecAttention.k",
+        "dec.blk.{bid}.cross_attn_v": "decoder.block.{bid}.layer.1.EncDecAttention.v",
+        "dec.blk.{bid}.cross_attn_o": "decoder.block.{bid}.layer.1.EncDecAttention.o",
+        "dec.blk.{bid}.cross_attn_norm": "decoder.block.{bid}.layer.1.layer_norm",
+        "dec.blk.{bid}.ffn_gate": "decoder.block.{bid}.layer.2.DenseReluDense.wi_0",
+        "dec.blk.{bid}.ffn_up": "decoder.block.{bid}.layer.2.DenseReluDense.wi_1",
+        "dec.blk.{bid}.ffn_down": "decoder.block.{bid}.layer.2.DenseReluDense.wo",
+        "dec.blk.{bid}.ffn_norm": "decoder.block.{bid}.layer.2.layer_norm",
+        "dec.output_norm": "decoder.final_layer_norm",
+        "enc.blk.{bid}.attn_q": "encoder.block.{bid}.layer.0.SelfAttention.q",
+        "enc.blk.{bid}.attn_k": "encoder.block.{bid}.layer.0.SelfAttention.k",
+        "enc.blk.{bid}.attn_v": "encoder.block.{bid}.layer.0.SelfAttention.v",
+        "enc.blk.{bid}.attn_o": "encoder.block.{bid}.layer.0.SelfAttention.o",
+        "enc.blk.{bid}.attn_rel_b": "encoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias",
+        "enc.blk.{bid}.attn_norm": "encoder.block.{bid}.layer.0.layer_norm",
+        "enc.blk.{bid}.ffn_gate": "encoder.block.{bid}.layer.1.DenseReluDense.wi_0",
+        "enc.blk.{bid}.ffn_up": "encoder.block.{bid}.layer.1.DenseReluDense.wi_1",
+        "enc.blk.{bid}.ffn_down": "encoder.block.{bid}.layer.1.DenseReluDense.wo",
+        "enc.blk.{bid}.ffn_norm": "encoder.block.{bid}.layer.1.layer_norm",
+        "enc.output_norm": "encoder.final_layer_norm",
+        "output.weight": "lm_head.weight",
+    },
+    "t5encoder": {
+        "token_embd": "shared",
+        "enc.blk.{bid}.attn_q": "encoder.block.{bid}.layer.0.SelfAttention.q",
+        "enc.blk.{bid}.attn_k": "encoder.block.{bid}.layer.0.SelfAttention.k",
+        "enc.blk.{bid}.attn_v": "encoder.block.{bid}.layer.0.SelfAttention.v",
+        "enc.blk.{bid}.attn_o": "encoder.block.{bid}.layer.0.SelfAttention.o",
+        "enc.blk.{bid}.attn_rel_b": "encoder.block.{bid}.layer.0.SelfAttention.relative_attention_bias",
+        "enc.blk.{bid}.attn_norm": "encoder.block.{bid}.layer.0.layer_norm",
+        "enc.blk.{bid}.ffn_gate": "encoder.block.{bid}.layer.1.DenseReluDense.wi_0",
+        "enc.blk.{bid}.ffn_up": "encoder.block.{bid}.layer.1.DenseReluDense.wi_1",
+        "enc.blk.{bid}.ffn_down": "encoder.block.{bid}.layer.1.DenseReluDense.wo",
+        "enc.blk.{bid}.ffn_norm": "encoder.block.{bid}.layer.1.layer_norm",
+        "enc.output_norm": "encoder.final_layer_norm",
+    },
     "stablelm": {
         "token_embd": "model.embed_tokens",
         "blk": "model.layers",
@@ -287,6 +332,19 @@
         "vocab_size": "vocab_size",
         "attention.layer_norm_epsilon": "layer_norm_epsilon",
     },
+    "t5": {
+        "context_length": "n_positions",
+        "block_count": "num_layers",
+        "feed_forward_length": "d_ff",
+        "embedding_length": "d_model",
+        "attention.key_length": "d_kv",
+        "attention.head_count": "num_heads",
+        "attention.head_count_kv": "num_key_value_heads",
+        "attention.layer_norm_epsilon": "layer_norm_epsilon",
+        "attention.relative_buckets_count": "relative_attention_num_buckets",
+        "decoder_start_token_id": "decoder_start_token_id",
+        "vocab_size": "vocab_size",
+    },
     "stablelm": {
         "context_length": "max_position_embeddings",
         "block_count": "num_hidden_layers",
@@ -636,6 +694,69 @@ def converted(self) -> Tokenizer:
         return tokenizer
 
 
+class GGUFT5Converter(T5Converter):
+    def __init__(self, tokenizer_dict):
+        # set dummy data to avoid unnecessary merges calculation
+        tokenizer_dict["merges"] = ["dummy text"]
+
+        self.proto = GGUFTokenizerSkeleton(tokenizer_dict)
+        self.token2id = {k: v for v, k in enumerate(self.proto.tokens)}
+        self.original_tokenizer = self.proto
+        self.additional_kwargs = {}
+
+    def vocab(self, proto):
+        return list(zip(proto.tokens, proto.scores))
+
+    def normalizer(self, proto):
+        if getattr(self.original_tokenizer, "legacy", True):
+            sequence = []
+            if getattr(self.original_tokenizer, "add_prefix_space", True):
+                sequence += [normalizers.Prepend(prepend="▁")]
+            sequence += [normalizers.Replace(pattern=" ", content="▁")]
+            return normalizers.Sequence(sequence)
+        return None  # non-legacy, no normalizer
+
+    def post_processor(self):
+        return processors.TemplateProcessing(
+            single=["$A", "</s>"],
+            pair=["$A", "</s>", "$B", "</s>"],
+            special_tokens=[
+                ("</s>", self.token2id["</s>"]),
+            ],
+        )
+
+    def converted(self) -> Tokenizer:
+        vocab_scores = self.vocab(self.proto)
+        tokenizer = Tokenizer(
+            Unigram(
+                vocab_scores,
+                unk_id=self.proto.unk_token_id,
+                byte_fallback=False,
+            )
+        )
+
+        # Tokenizer assemble
+        normalizer = self.normalizer(self.proto)
+        if normalizer is not None:
+            tokenizer.normalizer = normalizer
+
+        replacement = "▁"
+        add_prefix_space = True
+        if hasattr(self.original_tokenizer, "add_prefix_space"):
+            add_prefix_space = self.original_tokenizer.add_prefix_space
+
+        pre_tokenizer = self.pre_tokenizer(replacement, add_prefix_space)
+        if pre_tokenizer is not None:
+            tokenizer.pre_tokenizer = pre_tokenizer
+
+        tokenizer.decoder = self.decoder(replacement, add_prefix_space)
+        post_processor = self.post_processor()
+        if post_processor:
+            tokenizer.post_processor = post_processor
+
+        return tokenizer
+
+
 GGUF_TO_FAST_CONVERTERS = {
     "llama": GGUFLlamaConverter,
     "qwen2": GGUFQwen2Converter,
@@ -646,6 +767,7 @@ def converted(self) -> Tokenizer:
     "stablelm": GGUFGPTConverter,
     "gpt2": GGUFGPTConverter,
     "starcoder2": GGUFGPTConverter,
+    "t5": GGUFT5Converter,
 }
 
 

src/transformers/modeling_gguf_pytorch_utils.py

@@ -94,6 +94,12 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
     # to add this patch to ensure things work correctly on our side.
     if "llama" in architecture and "mistral" in model_name:
         updated_architecture = "mistral"
+    # FIXME: Currnetly this implementation is only for flan-t5 architecture.
+    # It needs to be developed for supporting legacy t5.
+    elif "t5" in architecture or "t5encoder" in architecture:
+        parsed_parameters["config"]["tie_word_embeddings"] = False
+        parsed_parameters["config"]["is_gated_act"] = True
+        updated_architecture = "t5"
     else:
         updated_architecture = architecture
 
@@ -191,6 +197,13 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
                 else:
                     weights = reverse_reshape_bias(weights, num_heads, n_embed)
 
+            bid = None
+            if architecture in ("t5", "t5encoder"):
+                for chunk in name.split("."):
+                    if chunk.isdigit():
+                        bid = int(chunk)
+                        break
+
             if architecture == "gpt2":
                 if (
                     "attn_qkv.weight" in name
@@ -209,8 +222,8 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
                     continue
 
             for tensor_name in tensor_key_mapping:
-                if tensor_name in name:
-                    name = name.replace(tensor_name, tensor_key_mapping[tensor_name])
+                if tensor_name.format(bid=bid) in name:
+                    name = name.replace(tensor_name.format(bid=bid), tensor_key_mapping[tensor_name].format(bid=bid))
 
             # Use copy to avoid errors with numpy and pytorch
             parsed_parameters["tensors"][name] = torch.from_numpy(np.copy(weights))

src/transformers/models/t5/tokenization_t5_fast.py

@@ -117,7 +117,7 @@ def __init__(
             kwargs["from_slow"] = True
 
         super().__init__(
-            vocab_file,
+            vocab_file=vocab_file,
             tokenizer_file=tokenizer_file,
             eos_token=eos_token,
             unk_token=unk_token,

tests/quantization/ggml/test_ggml.py

@@ -15,7 +15,7 @@
 import tempfile
 import unittest
 
-from transformers import AddedToken, AutoModelForCausalLM, AutoTokenizer
+from transformers import AddedToken, AutoModelForCausalLM, AutoModelForSeq2SeqLM, AutoTokenizer
 from transformers.testing_utils import (
     require_gguf,
     require_torch_gpu,
@@ -48,6 +48,8 @@ class GgufIntegrationTests(unittest.TestCase):
     falcon7b_model_id = "xaviviro/falcon-7b-quantized-gguf"
     falcon40b_model_id = "maddes8cht/tiiuae-falcon-40b-gguf"
     original_flacon7b_model_id = "tiiuae/falcon-7b"
+    t5_model_id = "repetitio/flan-t5-small"
+    original_t5_model_id = "google/flan-t5-small"
     stablelm_model_id = "afrideva/stablelm-3b-4e1t-GGUF"
     stablelm2_model_id = "afrideva/stablelm-2-1_6b-GGUF"
     original_stablelm2_model_id = "stabilityai/stablelm-2-1_6b"
@@ -92,6 +94,8 @@ class GgufIntegrationTests(unittest.TestCase):
     q2_k_falcon7b_model_id = "falcon-7b-q2_k.gguf"
     fp16_falcon7b_model_id = "falcon-7b-fp16.gguf"
     q2_k_falcon40b_model_id = "tiiuae-falcon-40b-Q2_K.gguf"
+    fp16_t5_model_id = "flan-t5-small-f16.gguf"
+    q8_0_t5_model_id = "flan-t5-small-q8_0.gguf"
     fp16_qwen2moe_model_id = "Qwen1.5-MoE-A2.7B.gguf"
     fp16_gpt2_model_id = "gpt2.f16.gguf"
     q8_gpt2_model_id = "gpt2.Q8_0.gguf"
@@ -487,6 +491,56 @@ def test_bloom_weights_conversion_fp16(self):
                 self.assertTrue(quantized_param.shape == original_param.shape)
                 torch.testing.assert_close(quantized_param, original_param)
 
+    def test_t5_f16(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.t5_model_id, gguf_file=self.fp16_t5_model_id)
+        model = AutoModelForSeq2SeqLM.from_pretrained(
+            self.t5_model_id, gguf_file=self.fp16_t5_model_id, device_map="auto", torch_dtype=torch.float16
+        )
+
+        T5_EXAMPLE_TEXT = "translate English to German: How old are you?"
+
+        text = tokenizer(T5_EXAMPLE_TEXT, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Wie ich er?"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_t5_q8_0(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.t5_model_id, gguf_file=self.q8_0_t5_model_id)
+        model = AutoModelForSeq2SeqLM.from_pretrained(
+            self.t5_model_id, gguf_file=self.q8_0_t5_model_id, device_map="auto", torch_dtype=torch.float16
+        )
+
+        T5_EXAMPLE_TEXT = "translate English to German: How old are you?"
+
+        text = tokenizer(T5_EXAMPLE_TEXT, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Wie ich er?"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_t5_weights_conversion_fp16(self):
+        quantized_model = AutoModelForSeq2SeqLM.from_pretrained(
+            self.t5_model_id,
+            gguf_file=self.fp16_t5_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+        original_model = AutoModelForSeq2SeqLM.from_pretrained(
+            self.original_t5_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        quantized_state_dict = quantized_model.state_dict()
+        original_state_dict = original_model.state_dict()
+
+        for (quantized_name, quantized_param), (original_name, original_param) in zip(
+            quantized_state_dict.items(), original_state_dict.items()
+        ):
+            self.assertTrue(quantized_param.shape == original_param.shape)
+            torch.testing.assert_close(quantized_param, original_param, rtol=5e-04, atol=5e-04)
+
     def test_gpt2_q8(self):
         tokenizer = AutoTokenizer.from_pretrained(self.gpt2_model_id, gguf_file=self.q8_gpt2_model_id)
         model = AutoModelForCausalLM.from_pretrained(