tts : add OuteTTS support (#10784)
* server : add "tokens" output ggml-ci * server : output embeddings for all tokens when pooling = none ggml-ci * server : be explicit about the pooling type in the tests ggml-ci * server : do not normalize embeddings when there is no pooling ggml-ci * llama : add OuteTTS support (wip) * wip * extract features * first conv * group norm * resnet conv * resnet * attn * pos net * layer norm * convnext * head * hann window * fix n_embd + remove llama.cpp hacks * compute hann window * fft * spectrum processing * clean-up * tts : receive input text and generate codes * clip : fix new conv name * tts : minor fix * tts : add header + minor fixes ggml-ci * tts : add matchematical constant ggml-ci * tts : fix sampling + cut initial noise * tts : fixes * tts : update default samplers ggml-ci * tts : text pre-processing * tts : outetts-voc -> wavtokenizer-dec * tts : remove hardcoded constants ggml-ci * tts : fix tensor shapes * llama : refactor wavtokenizer tensors ggml-ci * cont ggml-ci * cont [no ci] * llama : update WavTokenizer to non-causal attn * llama : handle no-vocab detokenization * tts : add Python example for OuteTTS (wip) * tts : extend python example to generate spectrogram ggml-ci * server : fix rebase artifacts * tts : enable "return_tokens" in Python example ggml-ci * tts : minor fixes * common : support HF download for vocoder
This commit is contained in:
Georgi Gerganov
GitHub
parent
7bbb5acf12
commit
0bf2d10c55
19 changed files with 2509 additions and 532 deletions
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@ -90,6 +90,7 @@ class Keys:
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VOCAB_SIZE = "{arch}.vocab_size"
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CONTEXT_LENGTH = "{arch}.context_length"
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EMBEDDING_LENGTH = "{arch}.embedding_length"
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FEATURES_LENGTH = "{arch}.features_length"
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BLOCK_COUNT = "{arch}.block_count"
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LEADING_DENSE_BLOCK_COUNT = "{arch}.leading_dense_block_count"
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FEED_FORWARD_LENGTH = "{arch}.feed_forward_length"
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@ -122,6 +123,8 @@ class Keys:
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VALUE_LENGTH = "{arch}.attention.value_length"
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LAYERNORM_EPS = "{arch}.attention.layer_norm_epsilon"
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LAYERNORM_RMS_EPS = "{arch}.attention.layer_norm_rms_epsilon"
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GROUPNORM_EPS = "{arch}.attention.group_norm_epsilon"
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GROUPNORM_GROUPS = "{arch}.attention.group_norm_groups"
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CAUSAL = "{arch}.attention.causal"
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Q_LORA_RANK = "{arch}.attention.q_lora_rank"
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KV_LORA_RANK = "{arch}.attention.kv_lora_rank"
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@ -155,6 +158,14 @@ class Keys:
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class WKV:
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HEAD_SIZE = "{arch}.wkv.head_size"
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class PosNet:
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EMBEDDING_LENGTH = "{arch}.posnet.embedding_length"
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BLOCK_COUNT = "{arch}.posnet.block_count"
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class ConvNext:
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EMBEDDING_LENGTH = "{arch}.convnext.embedding_length"
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BLOCK_COUNT = "{arch}.convnext.block_count"
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class Tokenizer:
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MODEL = "tokenizer.ggml.model"
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PRE = "tokenizer.ggml.pre"
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@ -209,58 +220,59 @@ class GGUFType:
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class MODEL_ARCH(IntEnum):
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LLAMA = auto()
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FALCON = auto()
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BAICHUAN = auto()
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GROK = auto()
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GPT2 = auto()
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GPTJ = auto()
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GPTNEOX = auto()
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MPT = auto()
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STARCODER = auto()
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REFACT = auto()
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BERT = auto()
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NOMIC_BERT = auto()
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JINA_BERT_V2 = auto()
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BLOOM = auto()
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STABLELM = auto()
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QWEN = auto()
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QWEN2 = auto()
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QWEN2MOE = auto()
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QWEN2VL = auto()
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PHI2 = auto()
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PHI3 = auto()
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PLAMO = auto()
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CODESHELL = auto()
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ORION = auto()
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INTERNLM2 = auto()
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MINICPM = auto()
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MINICPM3 = auto()
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GEMMA = auto()
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GEMMA2 = auto()
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STARCODER2 = auto()
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RWKV6 = auto()
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MAMBA = auto()
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XVERSE = auto()
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COMMAND_R = auto()
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DBRX = auto()
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OLMO = auto()
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OLMO2 = auto()
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OLMOE = auto()
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OPENELM = auto()
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ARCTIC = auto()
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DEEPSEEK = auto()
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DEEPSEEK2 = auto()
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CHATGLM = auto()
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BITNET = auto()
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T5 = auto()
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T5ENCODER = auto()
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JAIS = auto()
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NEMOTRON = auto()
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EXAONE = auto()
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GRANITE = auto()
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GRANITE_MOE = auto()
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CHAMELEON = auto()
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LLAMA = auto()
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FALCON = auto()
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BAICHUAN = auto()
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GROK = auto()
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GPT2 = auto()
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GPTJ = auto()
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GPTNEOX = auto()
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MPT = auto()
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STARCODER = auto()
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REFACT = auto()
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BERT = auto()
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NOMIC_BERT = auto()
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JINA_BERT_V2 = auto()
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BLOOM = auto()
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STABLELM = auto()
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QWEN = auto()
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QWEN2 = auto()
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QWEN2MOE = auto()
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QWEN2VL = auto()
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PHI2 = auto()
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PHI3 = auto()
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PLAMO = auto()
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CODESHELL = auto()
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ORION = auto()
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INTERNLM2 = auto()
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MINICPM = auto()
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MINICPM3 = auto()
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GEMMA = auto()
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GEMMA2 = auto()
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STARCODER2 = auto()
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RWKV6 = auto()
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MAMBA = auto()
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XVERSE = auto()
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COMMAND_R = auto()
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DBRX = auto()
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OLMO = auto()
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OLMO2 = auto()
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OLMOE = auto()
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OPENELM = auto()
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ARCTIC = auto()
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DEEPSEEK = auto()
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DEEPSEEK2 = auto()
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CHATGLM = auto()
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BITNET = auto()
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T5 = auto()
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T5ENCODER = auto()
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JAIS = auto()
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NEMOTRON = auto()
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EXAONE = auto()
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GRANITE = auto()
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GRANITE_MOE = auto()
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CHAMELEON = auto()
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WAVTOKENIZER_DEC = auto()
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class MODEL_TENSOR(IntEnum):
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@ -370,61 +382,78 @@ class MODEL_TENSOR(IntEnum):
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ENC_OUTPUT_NORM = auto()
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CLS = auto() # classifier
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CLS_OUT = auto() # classifier output projection
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CONV1D = auto()
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CONVNEXT_DW = auto()
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CONVNEXT_NORM = auto()
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CONVNEXT_PW1 = auto()
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CONVNEXT_PW2 = auto()
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CONVNEXT_GAMMA = auto()
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POSNET_CONV1 = auto()
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POSNET_CONV2 = auto()
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POSNET_NORM = auto()
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POSNET_NORM1 = auto()
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POSNET_NORM2 = auto()
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POSNET_ATTN_NORM = auto()
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POSNET_ATTN_Q = auto()
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POSNET_ATTN_K = auto()
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POSNET_ATTN_V = auto()
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POSNET_ATTN_OUT = auto()
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MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.LLAMA: "llama",
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MODEL_ARCH.FALCON: "falcon",
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MODEL_ARCH.BAICHUAN: "baichuan",
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MODEL_ARCH.GROK: "grok",
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MODEL_ARCH.GPT2: "gpt2",
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MODEL_ARCH.GPTJ: "gptj",
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MODEL_ARCH.GPTNEOX: "gptneox",
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MODEL_ARCH.MPT: "mpt",
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MODEL_ARCH.STARCODER: "starcoder",
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MODEL_ARCH.REFACT: "refact",
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MODEL_ARCH.BERT: "bert",
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MODEL_ARCH.NOMIC_BERT: "nomic-bert",
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MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2",
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MODEL_ARCH.BLOOM: "bloom",
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MODEL_ARCH.STABLELM: "stablelm",
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MODEL_ARCH.QWEN: "qwen",
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MODEL_ARCH.QWEN2: "qwen2",
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MODEL_ARCH.QWEN2MOE: "qwen2moe",
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MODEL_ARCH.QWEN2VL: "qwen2vl",
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MODEL_ARCH.PHI2: "phi2",
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MODEL_ARCH.PHI3: "phi3",
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MODEL_ARCH.PLAMO: "plamo",
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MODEL_ARCH.CODESHELL: "codeshell",
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MODEL_ARCH.ORION: "orion",
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MODEL_ARCH.INTERNLM2: "internlm2",
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MODEL_ARCH.MINICPM: "minicpm",
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MODEL_ARCH.MINICPM3: "minicpm3",
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MODEL_ARCH.GEMMA: "gemma",
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MODEL_ARCH.GEMMA2: "gemma2",
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MODEL_ARCH.STARCODER2: "starcoder2",
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MODEL_ARCH.RWKV6: "rwkv6",
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.XVERSE: "xverse",
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MODEL_ARCH.COMMAND_R: "command-r",
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MODEL_ARCH.DBRX: "dbrx",
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MODEL_ARCH.OLMO: "olmo",
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MODEL_ARCH.OLMO2: "olmo2",
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MODEL_ARCH.OLMOE: "olmoe",
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MODEL_ARCH.OPENELM: "openelm",
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MODEL_ARCH.ARCTIC: "arctic",
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MODEL_ARCH.DEEPSEEK: "deepseek",
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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MODEL_ARCH.CHATGLM: "chatglm",
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MODEL_ARCH.BITNET: "bitnet",
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MODEL_ARCH.T5: "t5",
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MODEL_ARCH.T5ENCODER: "t5encoder",
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MODEL_ARCH.JAIS: "jais",
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MODEL_ARCH.NEMOTRON: "nemotron",
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MODEL_ARCH.EXAONE: "exaone",
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MODEL_ARCH.GRANITE: "granite",
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MODEL_ARCH.GRANITE_MOE: "granitemoe",
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MODEL_ARCH.CHAMELEON: "chameleon",
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MODEL_ARCH.LLAMA: "llama",
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MODEL_ARCH.FALCON: "falcon",
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MODEL_ARCH.BAICHUAN: "baichuan",
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MODEL_ARCH.GROK: "grok",
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MODEL_ARCH.GPT2: "gpt2",
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MODEL_ARCH.GPTJ: "gptj",
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MODEL_ARCH.GPTNEOX: "gptneox",
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MODEL_ARCH.MPT: "mpt",
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MODEL_ARCH.STARCODER: "starcoder",
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MODEL_ARCH.REFACT: "refact",
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MODEL_ARCH.BERT: "bert",
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MODEL_ARCH.NOMIC_BERT: "nomic-bert",
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MODEL_ARCH.JINA_BERT_V2: "jina-bert-v2",
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MODEL_ARCH.BLOOM: "bloom",
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MODEL_ARCH.STABLELM: "stablelm",
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MODEL_ARCH.QWEN: "qwen",
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MODEL_ARCH.QWEN2: "qwen2",
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MODEL_ARCH.QWEN2MOE: "qwen2moe",
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MODEL_ARCH.QWEN2VL: "qwen2vl",
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MODEL_ARCH.PHI2: "phi2",
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MODEL_ARCH.PHI3: "phi3",
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MODEL_ARCH.PLAMO: "plamo",
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MODEL_ARCH.CODESHELL: "codeshell",
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MODEL_ARCH.ORION: "orion",
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MODEL_ARCH.INTERNLM2: "internlm2",
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MODEL_ARCH.MINICPM: "minicpm",
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MODEL_ARCH.MINICPM3: "minicpm3",
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MODEL_ARCH.GEMMA: "gemma",
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MODEL_ARCH.GEMMA2: "gemma2",
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MODEL_ARCH.STARCODER2: "starcoder2",
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MODEL_ARCH.RWKV6: "rwkv6",
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.XVERSE: "xverse",
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MODEL_ARCH.COMMAND_R: "command-r",
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MODEL_ARCH.DBRX: "dbrx",
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MODEL_ARCH.OLMO: "olmo",
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MODEL_ARCH.OLMO2: "olmo2",
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MODEL_ARCH.OLMOE: "olmoe",
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MODEL_ARCH.OPENELM: "openelm",
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MODEL_ARCH.ARCTIC: "arctic",
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MODEL_ARCH.DEEPSEEK: "deepseek",
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MODEL_ARCH.DEEPSEEK2: "deepseek2",
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MODEL_ARCH.CHATGLM: "chatglm",
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MODEL_ARCH.BITNET: "bitnet",
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MODEL_ARCH.T5: "t5",
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MODEL_ARCH.T5ENCODER: "t5encoder",
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MODEL_ARCH.JAIS: "jais",
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MODEL_ARCH.NEMOTRON: "nemotron",
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MODEL_ARCH.EXAONE: "exaone",
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MODEL_ARCH.GRANITE: "granite",
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MODEL_ARCH.GRANITE_MOE: "granitemoe",
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MODEL_ARCH.CHAMELEON: "chameleon",
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MODEL_ARCH.WAVTOKENIZER_DEC: "wavtokenizer-dec",
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}
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TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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@ -534,6 +563,22 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.ENC_OUTPUT_NORM: "enc.output_norm",
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MODEL_TENSOR.CLS: "cls",
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MODEL_TENSOR.CLS_OUT: "cls.output",
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MODEL_TENSOR.CONV1D: "conv1d",
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MODEL_TENSOR.CONVNEXT_DW: "convnext.{bid}.dw",
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MODEL_TENSOR.CONVNEXT_NORM: "convnext.{bid}.norm",
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MODEL_TENSOR.CONVNEXT_PW1: "convnext.{bid}.pw1",
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MODEL_TENSOR.CONVNEXT_PW2: "convnext.{bid}.pw2",
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MODEL_TENSOR.CONVNEXT_GAMMA: "convnext.{bid}.gamma",
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MODEL_TENSOR.POSNET_CONV1: "posnet.{bid}.conv1",
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MODEL_TENSOR.POSNET_CONV2: "posnet.{bid}.conv2",
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MODEL_TENSOR.POSNET_NORM: "posnet.{bid}.norm",
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MODEL_TENSOR.POSNET_NORM1: "posnet.{bid}.norm1",
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MODEL_TENSOR.POSNET_NORM2: "posnet.{bid}.norm2",
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MODEL_TENSOR.POSNET_ATTN_NORM: "posnet.{bid}.attn_norm",
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MODEL_TENSOR.POSNET_ATTN_Q: "posnet.{bid}.attn_q",
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MODEL_TENSOR.POSNET_ATTN_K: "posnet.{bid}.attn_k",
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MODEL_TENSOR.POSNET_ATTN_V: "posnet.{bid}.attn_v",
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MODEL_TENSOR.POSNET_ATTN_OUT: "posnet.{bid}.attn_output",
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}
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MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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@ -1372,6 +1417,28 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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],
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MODEL_ARCH.WAVTOKENIZER_DEC: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.TOKEN_EMBD_NORM,
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MODEL_TENSOR.CONV1D,
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MODEL_TENSOR.CONVNEXT_DW,
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MODEL_TENSOR.CONVNEXT_NORM,
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MODEL_TENSOR.CONVNEXT_PW1,
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MODEL_TENSOR.CONVNEXT_PW2,
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MODEL_TENSOR.CONVNEXT_GAMMA,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.POSNET_CONV1,
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MODEL_TENSOR.POSNET_CONV2,
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MODEL_TENSOR.POSNET_NORM,
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MODEL_TENSOR.POSNET_NORM1,
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MODEL_TENSOR.POSNET_NORM2,
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MODEL_TENSOR.POSNET_ATTN_NORM,
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MODEL_TENSOR.POSNET_ATTN_Q,
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MODEL_TENSOR.POSNET_ATTN_K,
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MODEL_TENSOR.POSNET_ATTN_V,
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MODEL_TENSOR.POSNET_ATTN_OUT,
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],
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# TODO
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}
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@ -631,6 +631,21 @@ class GGUFWriter:
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def add_embedding_length(self, length: int) -> None:
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self.add_uint32(Keys.LLM.EMBEDDING_LENGTH.format(arch=self.arch), length)
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def add_features_length(self, length: int) -> None:
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self.add_uint32(Keys.LLM.FEATURES_LENGTH.format(arch=self.arch), length)
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def add_posnet_embedding_length(self, length: int) -> None:
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self.add_uint32(Keys.PosNet.EMBEDDING_LENGTH.format(arch=self.arch), length)
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def add_posnet_block_count(self, length: int) -> None:
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self.add_uint32(Keys.PosNet.BLOCK_COUNT.format(arch=self.arch), length)
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def add_convnext_embedding_length(self, length: int) -> None:
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self.add_uint32(Keys.ConvNext.EMBEDDING_LENGTH.format(arch=self.arch), length)
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def add_convnext_block_count(self, length: int) -> None:
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self.add_uint32(Keys.ConvNext.BLOCK_COUNT.format(arch=self.arch), length)
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def add_block_count(self, length: int) -> None:
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self.add_uint32(Keys.LLM.BLOCK_COUNT.format(arch=self.arch), length)
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@ -727,6 +742,12 @@ class GGUFWriter:
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def add_layer_norm_rms_eps(self, value: float) -> None:
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self.add_float32(Keys.Attention.LAYERNORM_RMS_EPS.format(arch=self.arch), value)
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def add_group_norm_eps(self, value: float) -> None:
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self.add_float32(Keys.Attention.GROUPNORM_EPS.format(arch=self.arch), value)
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def add_group_norm_groups(self, value: int) -> None:
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self.add_uint32(Keys.Attention.GROUPNORM_GROUPS.format(arch=self.arch), value)
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def add_causal_attention(self, value: bool) -> None:
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self.add_bool(Keys.Attention.CAUSAL.format(arch=self.arch), value)
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@ -42,6 +42,7 @@ class TensorNameMap:
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"emb_ln", # nomic-bert
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"transformer.norm", # openelm
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"rwkv.blocks.0.pre_ln", # rwkv
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"backbone.norm", # wavtokenizer
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),
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# Position embeddings
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@ -60,6 +61,7 @@ class TensorNameMap:
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"lm_head.linear", # phi2
|
||||
"output_layer", # chatglm
|
||||
"head", # rwkv
|
||||
"head.out", # wavtokenizer
|
||||
),
|
||||
|
||||
# Output norm
|
||||
|
|
@ -80,6 +82,7 @@ class TensorNameMap:
|
|||
"transformer.norm", # openelm
|
||||
"model.norm", # nemotron
|
||||
"rwkv.ln_out", # rwkv
|
||||
"backbone.final_layer_norm", # wavtokenizer
|
||||
),
|
||||
|
||||
# Rope frequencies
|
||||
|
|
@ -90,6 +93,10 @@ class TensorNameMap:
|
|||
|
||||
MODEL_TENSOR.ROPE_FACTORS_LONG: (),
|
||||
MODEL_TENSOR.ROPE_FACTORS_SHORT: (),
|
||||
|
||||
MODEL_TENSOR.CONV1D: (
|
||||
"backbone.embed", # roberta
|
||||
),
|
||||
}
|
||||
|
||||
block_mappings_cfg: dict[MODEL_TENSOR, tuple[str, ...]] = {
|
||||
|
|
@ -681,6 +688,8 @@ class TensorNameMap:
|
|||
"encoder.block.{bid}.layer.1.DenseReluDense.wo", # t5
|
||||
),
|
||||
|
||||
############################################################################
|
||||
# TODO: these do not belong to block_mappings_cfg - move them to mappings_cfg
|
||||
MODEL_TENSOR.ENC_OUTPUT_NORM: (
|
||||
"encoder.final_layer_norm", # t5
|
||||
),
|
||||
|
|
@ -693,6 +702,67 @@ class TensorNameMap:
|
|||
MODEL_TENSOR.CLS_OUT: (
|
||||
"classifier.out_proj", # roberta
|
||||
),
|
||||
#############################################################################
|
||||
|
||||
MODEL_TENSOR.CONVNEXT_DW: (
|
||||
"backbone.convnext.{bid}.dwconv", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.CONVNEXT_NORM: (
|
||||
"backbone.convnext.{bid}.norm", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.CONVNEXT_PW1: (
|
||||
"backbone.convnext.{bid}.pwconv1", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.CONVNEXT_PW2: (
|
||||
"backbone.convnext.{bid}.pwconv2", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.CONVNEXT_GAMMA: (
|
||||
"backbone.convnext.{bid}.gamma", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_CONV1: (
|
||||
"backbone.posnet.{bid}.conv1", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_CONV2: (
|
||||
"backbone.posnet.{bid}.conv2", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_NORM: (
|
||||
"backbone.posnet.{bid}.norm", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_NORM1: (
|
||||
"backbone.posnet.{bid}.norm1", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_NORM2: (
|
||||
"backbone.posnet.{bid}.norm2", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_ATTN_NORM: (
|
||||
"backbone.posnet.{bid}.norm", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_ATTN_Q: (
|
||||
"backbone.posnet.{bid}.q", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_ATTN_K: (
|
||||
"backbone.posnet.{bid}.k", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_ATTN_V: (
|
||||
"backbone.posnet.{bid}.v", # wavtokenizer
|
||||
),
|
||||
|
||||
MODEL_TENSOR.POSNET_ATTN_OUT: (
|
||||
"backbone.posnet.{bid}.proj_out", # wavtokenizer
|
||||
),
|
||||
}
|
||||
|
||||
# architecture-specific block mappings
|
||||
|
|
|
|||
|
|
@ -136,7 +136,7 @@ def compare_tensors(t1: np.ndarray, t2: np.ndarray, qtype: GGMLQuantizationType)
|
|||
logger.debug(f"Sample bad block ({diff_bits[bad_block_id]} differing bits):\n{t1[bad_block_id]}\nReference:\n{t2[bad_block_id]}")
|
||||
|
||||
sum_diff_bits = np.sum(diff_bits)
|
||||
logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits/(x.size * 8):.6f}%)")
|
||||
logger.debug(f"{sum_diff_bits} bits differ ({100 * sum_diff_bits / (x.size * 8):.6f}%)")
|
||||
return False
|
||||
|
||||
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue