Add gguf support for bloom (#33473)

* add bloom arch support for gguf

* apply format

* small refactoring, bug fix in GGUF_TENSOR_MAPPING naming

* optimize bloom GGUF_TENSOR_MAPPING

* implement reverse reshaping for bloom gguf

* add qkv weights test

* add q_8 test for bloom

docs/source/en/gguf.md

@@ -80,6 +80,7 @@ For now the supported model architectures are the architectures that have been v
 - Qwen2
 - Qwen2Moe
 - Phi3
+- Bloom
 
 ## Example usage
 

src/transformers/convert_slow_tokenizer.py

@@ -328,9 +328,11 @@ class OpenAIGPTConverter(Converter):
 
 
 class GPT2Converter(Converter):
-    def converted(self) -> Tokenizer:
-        vocab = self.original_tokenizer.encoder
-        merges = list(self.original_tokenizer.bpe_ranks.keys())
+    def converted(self, vocab: Dict[str, int] = None, merges: List[Tuple[str, str]] = None) -> Tokenizer:
+        if not vocab:
+            vocab = self.original_tokenizer.encoder
+        if not merges:
+            merges = list(self.original_tokenizer.bpe_ranks)
 
         tokenizer = Tokenizer(
             BPE(
@@ -343,9 +345,11 @@ class GPT2Converter(Converter):
             )
         )
 
-        tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=self.original_tokenizer.add_prefix_space)
+        add_prefix_space = False
+        add_prefix_space = getattr(self.original_tokenizer, "add_prefix_space", False)
+        tokenizer.pre_tokenizer = pre_tokenizers.ByteLevel(add_prefix_space=add_prefix_space)
         tokenizer.decoder = decoders.ByteLevel()
-        if self.original_tokenizer.add_bos_token:
+        if getattr(self.original_tokenizer, "add_bos_token", False):
             bos = self.original_tokenizer.bos_token
             bos_token_id = self.original_tokenizer.bos_token_id
             tokenizer.post_processor = processors.TemplateProcessing(

src/transformers/integrations/ggml.py

@@ -25,7 +25,7 @@ from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers
 from tokenizers.models import BPE
 
 from .. import AddedToken
-from ..convert_slow_tokenizer import LlamaConverter, Qwen2Converter
+from ..convert_slow_tokenizer import GPT2Converter, LlamaConverter, Qwen2Converter
 from ..utils import logging
 from ..utils.logging import tqdm
 
@@ -107,6 +107,19 @@ GGUF_TENSOR_MAPPING = {
         "output.weight": "lm_head.weight",
         "output_norm": "model.norm",
     },
+    "bloom": {
+        "token_embd.weight": "transformer.word_embeddings.weight",
+        "token_embd_norm": "transformer.word_embeddings_layernorm",
+        "blk": "transformer.h",
+        "ffn_up": "mlp.dense_h_to_4h",
+        "ffn_down": "mlp.dense_4h_to_h",
+        "ffn_norm": "post_attention_layernorm",
+        "attn_norm": "input_layernorm",
+        "attn_qkv": "self_attention.query_key_value",
+        "attn_output": "self_attention.dense",
+        "output.weight": "lm_head.weight",
+        "output_norm": "transformer.ln_f",
+    },
 }
 
 
@@ -183,6 +196,13 @@ GGUF_CONFIG_MAPPING = {
         "attention.layer_norm_rms_epsilon": "rms_norm_eps",
         "vocab_size": "vocab_size",
     },
+    "bloom": {
+        "block_count": "n_layer",
+        "embedding_length": "hidden_size",
+        "attention.head_count": "n_head",
+        "vocab_size": "vocab_size",
+        "attention.layer_norm_epsilon": "layer_norm_epsilon",
+    },
 }
 
 GGUF_TOKENIZER_MAPPING = {
@@ -492,11 +512,24 @@ class GGUFPhi3Converter(LlamaConverter):
         return tokenizer
 
 
+class GGUFBloomConverter(GPT2Converter):
+    def __init__(self, tokenizer_dict):
+        self.original_tokenizer = GGUFTokenizerSkeleton(tokenizer_dict)
+        self.additional_kwargs = {}
+
+    def converted(self) -> Tokenizer:
+        vocab = {word: i for i, word in enumerate(self.original_tokenizer.tokens)}
+        merges = self.original_tokenizer.merges
+        tokenizer = super().converted(vocab, merges)
+        return tokenizer
+
+
 GGUF_TO_FAST_CONVERTERS = {
     "llama": GGUFLlamaConverter,
     "qwen2": GGUFQwen2Converter,
     "qwen2_moe": GGUFQwen2Converter,
     "phi3": GGUFPhi3Converter,
+    "bloom": GGUFBloomConverter,
 }
 
 

src/transformers/modeling_gguf_pytorch_utils.py

@@ -169,6 +169,14 @@ def load_gguf_checkpoint(gguf_checkpoint_path, return_tensors=False):
                 elif ".attn_k." in name:
                     weights = reverse_permute_weights(weights, num_heads, num_kv_heads)
 
+            if architecture == "bloom" and "attn_qkv" in name:
+                num_heads = parsed_parameters["config"]["n_head"]
+                n_embed = parsed_parameters["config"]["hidden_size"]
+                if "weight" in name:
+                    weights = reverse_reshape_weights(weights, num_heads, n_embed)
+                else:
+                    weights = reverse_reshape_bias(weights, num_heads, n_embed)
+
             for tensor_name in tensor_key_mapping:
                 if tensor_name in name:
                     name = name.replace(tensor_name, tensor_key_mapping[tensor_name])
@@ -191,3 +199,29 @@ def reverse_permute_weights(weights: np.ndarray, n_head: int, num_kv_heads: Opti
     dim = weights.shape[0] // n_head // 2
     w = weights.reshape(n_head, dim, 2, *weights.shape[1:])
     return w.swapaxes(2, 1).reshape(weights.shape)
+
+
+def reverse_reshape_weights(weights: np.ndarray, n_head: int, n_embed: int):
+    # Original reshape implementation
+    # https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L972-L985
+    q, k, v = np.array_split(weights, 3, axis=0)
+
+    q = q.reshape(n_head, n_embed // n_head, n_embed)
+    k = k.reshape(n_head, n_embed // n_head, n_embed)
+    v = v.reshape(n_head, n_embed // n_head, n_embed)
+    qkv_weights = np.stack([q, k, v], axis=1)
+
+    return qkv_weights.reshape(n_head * 3 * (n_embed // n_head), n_embed)
+
+
+def reverse_reshape_bias(weights: np.ndarray, n_head: int, n_embed: int):
+    # Original reshape implementation
+    # https://github.com/ggerganov/llama.cpp/blob/master/convert_hf_to_gguf.py#L986-L998
+    q_bias, k_bias, v_bias = np.array_split(weights, 3)
+
+    q_bias = q_bias.reshape(n_head, n_embed // n_head)
+    k_bias = k_bias.reshape(n_head, n_embed // n_head)
+    v_bias = v_bias.reshape(n_head, n_embed // n_head)
+
+    qkv_bias = np.stack([q_bias, k_bias, v_bias], axis=1).flatten()
+    return qkv_bias

src/transformers/models/bloom/tokenization_bloom_fast.py

@@ -99,8 +99,8 @@ class BloomTokenizerFast(PreTrainedTokenizerFast):
         **kwargs,
     ):
         super().__init__(
-            vocab_file,
-            merges_file,
+            vocab_file=vocab_file,
+            merges_file=merges_file,
             tokenizer_file=tokenizer_file,
             unk_token=unk_token,
             bos_token=bos_token,

tests/quantization/ggml/test_ggml.py

@@ -42,6 +42,8 @@ class GgufIntegrationTests(unittest.TestCase):
     llama3_model_id = "NousResearch/Meta-Llama-3-8B-GGUF"
     tinyllama_model_id = "PenutChen/TinyLlama-1.1B-Chat-v1.0-GGUF"
     phi3_model_id = "microsoft/Phi-3-mini-4k-instruct-gguf"
+    bloom_model_id = "afrideva/bloom-560m-GGUF"
+    original_bloom_model_id = "bigscience/bloom-560m"
 
     # standard quants
     q4_0_gguf_model_id = "tinyllama-1.1b-chat-v1.0.Q4_0.gguf"
@@ -69,6 +71,8 @@ class GgufIntegrationTests(unittest.TestCase):
     q4_0_qwen2_model_id = "qwen1_5-0_5b-chat-q4_0.gguf"
     q4_0_qwen2_moe_model_id = "Qwen1.5-MoE-A2.7B-Chat.Q4_0.gguf"
     q4_llama3_model_id = "Meta-Llama-3-8B-Q4_K_M.gguf"
+    fp16_bloom_model_id = "bloom-560m.fp16.gguf"
+    q8_bloom_model_id = "bloom-560m.q8_0.gguf"
     f16_tinyllama_model_id = "TinyLlama-1.1B-Chat-v1.0.FP16.gguf"
 
     example_text = "Hello"
@@ -385,6 +389,62 @@ class GgufIntegrationTests(unittest.TestCase):
         EXPECTED_TEXT = "Hello, I am interested in [The Park]\nThe"
         self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
 
+    def test_bloom_fp16(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.bloom_model_id, gguf_file=self.fp16_bloom_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.bloom_model_id,
+            gguf_file=self.fp16_bloom_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello, I just want to say that I am very"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_bloom_q8_0(self):
+        tokenizer = AutoTokenizer.from_pretrained(self.bloom_model_id, gguf_file=self.q8_bloom_model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            self.bloom_model_id,
+            gguf_file=self.q8_bloom_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+
+        text = tokenizer(self.example_text, return_tensors="pt").to(torch_device)
+        out = model.generate(**text, max_new_tokens=10)
+
+        EXPECTED_TEXT = "Hello, I just want to say that I am very"
+        self.assertEqual(tokenizer.decode(out[0], skip_special_tokens=True), EXPECTED_TEXT)
+
+    def test_bloom_weights_conversion_fp16(self):
+        quantized_model = AutoModelForCausalLM.from_pretrained(
+            self.bloom_model_id,
+            gguf_file=self.fp16_bloom_model_id,
+            device_map="auto",
+            torch_dtype=torch.float16,
+        )
+        original_model = AutoModelForCausalLM.from_pretrained(
+            self.original_bloom_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()
+        ):
+            if (
+                "self_attention.query_key_value" in quantized_name
+                and "self_attention.query_key_value" in original_name
+            ):
+                self.assertTrue(quantized_param.shape == original_param.shape)
+                torch.testing.assert_close(quantized_param, original_param)
+
     def test_tokenization_xnli(self):
         import tqdm
         from datasets import load_dataset