convert_hf_to_gguf: Add support for RWKV v6
Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
This commit is contained in:
Molly Sophia
parent
20f1789dfb
commit
8d2eca3507
3 changed files with 332 additions and 82 deletions
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@ -2716,6 +2716,79 @@ class StarCoder2Model(Model):
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model_arch = gguf.MODEL_ARCH.STARCODER2
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model_arch = gguf.MODEL_ARCH.STARCODER2
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@Model.register("Rwkv6ForCausalLM")
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class RwkvModel(Model):
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model_arch = gguf.MODEL_ARCH.RWKV
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def set_vocab(self):
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assert (self.dir_model / "rwkv_vocab_v20230424.txt").is_file()
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vocab_size = self.hparams.get("vocab_size", 65536)
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tokens: list[bytes] = ['<s>'.encode("utf-8")]
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toktypes: list[int] = [gguf.TokenType.CONTROL]
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with open(self.dir_model / "rwkv_vocab_v20230424.txt", "r", encoding="utf-8") as f:
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lines = f.readlines()
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for line in lines:
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x = eval(line[line.index(' '):line.rindex(' ')])
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x = x.encode("utf-8") if isinstance(x, str) else x
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assert isinstance(x, bytes)
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assert len(x) == int(line[line.rindex(' '):])
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token_text: str = ""
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for b in x:
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token_text += f"\\x{b:02x}"
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tokens.append(token_text.encode("utf-8"))
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toktypes.append(gguf.TokenType.NORMAL)
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remainder = vocab_size - len(tokens)
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assert remainder >= 0
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for i in range(remainder):
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tokens.append(f"<unused {i}>".encode("utf-8"))
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toktypes.append(gguf.TokenType.UNUSED)
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self.gguf_writer.add_tokenizer_model("rwkv")
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self.gguf_writer.add_token_list(tokens)
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self.gguf_writer.add_token_types(toktypes)
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def set_gguf_parameters(self):
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block_count = self.hparams["num_hidden_layers"]
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head_size = self.hparams["head_size"]
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hidden_size = self.hparams["hidden_size"]
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layer_norm_eps = self.hparams["layer_norm_epsilon"]
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# RWKV isn't context limited
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self.gguf_writer.add_context_length(1048576)
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self.gguf_writer.add_embedding_length(hidden_size)
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self.gguf_writer.add_block_count(block_count)
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self.gguf_writer.add_head_count(0)
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self.gguf_writer.add_layer_norm_eps(layer_norm_eps)
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self.gguf_writer.add_feed_forward_length(0) # required by llama.cpp
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# temporarlily reuse mamba hparams
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self.gguf_writer.add_ssm_inner_size(hidden_size)
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self.gguf_writer.add_ssm_conv_kernel(3)
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self.gguf_writer.add_ssm_state_size(head_size)
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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del bid # unused
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new_name = self.map_tensor_name(name)
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if not (new_name.endswith(".weight") or new_name.endswith(".bias")):
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new_name += ".weight"
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if new_name.endswith("time_mix_w1.weight") or new_name.endswith("time_mix_decay_w1.weight") or new_name.endswith("time_mix_decay_w2.weight"):
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data_torch = data_torch.transpose(0, 1)
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if new_name.endswith("time_mix_w2.weight"):
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data_torch = data_torch.permute(0, 2, 1)
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rescale_every_n_layers = self.hparams["rescale_every"]
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if rescale_every_n_layers > 0:
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if new_name.endswith("time_mix_output.weight") or new_name.endswith("channel_mix_value.weight"):
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data_torch = data_torch.div_(2 ** int(bid // rescale_every_n_layers))
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yield (new_name, data_torch)
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@Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
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@Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
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class MambaModel(Model):
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class MambaModel(Model):
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model_arch = gguf.MODEL_ARCH.MAMBA
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model_arch = gguf.MODEL_ARCH.MAMBA
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@ -207,6 +207,7 @@ class MODEL_ARCH(IntEnum):
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GEMMA = auto()
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GEMMA = auto()
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GEMMA2 = auto()
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GEMMA2 = auto()
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STARCODER2 = auto()
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STARCODER2 = auto()
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RWKV = auto()
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MAMBA = auto()
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MAMBA = auto()
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XVERSE = auto()
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XVERSE = auto()
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COMMAND_R = auto()
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COMMAND_R = auto()
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@ -270,6 +271,29 @@ class MODEL_TENSOR(IntEnum):
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SSM_A = auto()
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SSM_A = auto()
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SSM_D = auto()
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SSM_D = auto()
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SSM_OUT = auto()
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SSM_OUT = auto()
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TIME_MIX_W1 = auto()
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TIME_MIX_W2 = auto()
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TIME_MIX_LERP_X = auto()
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TIME_MIX_LERP_K = auto()
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TIME_MIX_LERP_V = auto()
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TIME_MIX_LERP_R = auto()
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TIME_MIX_LERP_G = auto()
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TIME_MIX_LERP_W = auto()
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TIME_MIX_FIRST = auto()
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TIME_MIX_DECAY = auto()
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TIME_MIX_DECAY_W1 = auto()
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TIME_MIX_DECAY_W2 = auto()
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TIME_MIX_KEY = auto()
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TIME_MIX_VALUE = auto()
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TIME_MIX_RECEPTANCE = auto()
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TIME_MIX_GATE = auto()
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TIME_MIX_LN = auto()
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TIME_MIX_OUTPUT = auto()
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CHANNEL_MIX_LERP_K = auto()
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CHANNEL_MIX_LERP_R = auto()
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CHANNEL_MIX_KEY = auto()
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CHANNEL_MIX_RECEPTANCE = auto()
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CHANNEL_MIX_VALUE = auto()
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ATTN_Q_A = auto()
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ATTN_Q_A = auto()
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ATTN_Q_B = auto()
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ATTN_Q_B = auto()
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ATTN_KV_A_MQA = auto()
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ATTN_KV_A_MQA = auto()
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@ -337,6 +361,7 @@ MODEL_ARCH_NAMES: dict[MODEL_ARCH, str] = {
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MODEL_ARCH.GEMMA: "gemma",
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MODEL_ARCH.GEMMA: "gemma",
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MODEL_ARCH.GEMMA2: "gemma2",
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MODEL_ARCH.GEMMA2: "gemma2",
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MODEL_ARCH.STARCODER2: "starcoder2",
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MODEL_ARCH.STARCODER2: "starcoder2",
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MODEL_ARCH.RWKV: "rwkv",
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.MAMBA: "mamba",
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MODEL_ARCH.XVERSE: "xverse",
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MODEL_ARCH.XVERSE: "xverse",
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MODEL_ARCH.COMMAND_R: "command-r",
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MODEL_ARCH.COMMAND_R: "command-r",
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@ -400,6 +425,29 @@ TENSOR_NAMES: dict[MODEL_TENSOR, str] = {
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MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a",
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MODEL_TENSOR.SSM_A: "blk.{bid}.ssm_a",
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MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d",
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MODEL_TENSOR.SSM_D: "blk.{bid}.ssm_d",
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MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out",
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MODEL_TENSOR.SSM_OUT: "blk.{bid}.ssm_out",
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MODEL_TENSOR.TIME_MIX_W1: "blk.{bid}.time_mix_w1",
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MODEL_TENSOR.TIME_MIX_W2: "blk.{bid}.time_mix_w2",
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MODEL_TENSOR.TIME_MIX_LERP_X: "blk.{bid}.time_mix_lerp_x",
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MODEL_TENSOR.TIME_MIX_LERP_K: "blk.{bid}.time_mix_lerp_k",
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MODEL_TENSOR.TIME_MIX_LERP_V: "blk.{bid}.time_mix_lerp_v",
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MODEL_TENSOR.TIME_MIX_LERP_R: "blk.{bid}.time_mix_lerp_r",
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MODEL_TENSOR.TIME_MIX_LERP_G: "blk.{bid}.time_mix_lerp_g",
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MODEL_TENSOR.TIME_MIX_LERP_W: "blk.{bid}.time_mix_lerp_w",
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MODEL_TENSOR.TIME_MIX_FIRST: "blk.{bid}.time_mix_first",
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MODEL_TENSOR.TIME_MIX_DECAY: "blk.{bid}.time_mix_decay",
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MODEL_TENSOR.TIME_MIX_DECAY_W1: "blk.{bid}.time_mix_decay_w1",
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MODEL_TENSOR.TIME_MIX_DECAY_W2: "blk.{bid}.time_mix_decay_w2",
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MODEL_TENSOR.TIME_MIX_KEY: "blk.{bid}.time_mix_key",
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MODEL_TENSOR.TIME_MIX_VALUE: "blk.{bid}.time_mix_value",
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MODEL_TENSOR.TIME_MIX_RECEPTANCE: "blk.{bid}.time_mix_receptance",
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MODEL_TENSOR.TIME_MIX_GATE: "blk.{bid}.time_mix_gate",
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MODEL_TENSOR.TIME_MIX_LN: "blk.{bid}.time_mix_ln",
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MODEL_TENSOR.TIME_MIX_OUTPUT: "blk.{bid}.time_mix_output",
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MODEL_TENSOR.CHANNEL_MIX_LERP_K: "blk.{bid}.channel_mix_lerp_k",
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MODEL_TENSOR.CHANNEL_MIX_LERP_R: "blk.{bid}.channel_mix_lerp_r",
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MODEL_TENSOR.CHANNEL_MIX_KEY: "blk.{bid}.channel_mix_key",
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MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: "blk.{bid}.channel_mix_receptance",
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MODEL_TENSOR.CHANNEL_MIX_VALUE: "blk.{bid}.channel_mix_value",
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MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a",
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MODEL_TENSOR.ATTN_Q_A: "blk.{bid}.attn_q_a",
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MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b",
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MODEL_TENSOR.ATTN_Q_B: "blk.{bid}.attn_q_b",
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MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa",
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MODEL_TENSOR.ATTN_KV_A_MQA: "blk.{bid}.attn_kv_a_mqa",
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@ -856,6 +904,37 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_UP,
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],
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],
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MODEL_ARCH.RWKV: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.TOKEN_EMBD_NORM,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT,
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MODEL_TENSOR.ATTN_NORM,
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MODEL_TENSOR.ATTN_NORM_2,
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MODEL_TENSOR.TIME_MIX_W1,
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MODEL_TENSOR.TIME_MIX_W2,
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MODEL_TENSOR.TIME_MIX_LERP_X,
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MODEL_TENSOR.TIME_MIX_LERP_K,
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MODEL_TENSOR.TIME_MIX_LERP_V,
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MODEL_TENSOR.TIME_MIX_LERP_R,
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MODEL_TENSOR.TIME_MIX_LERP_G,
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MODEL_TENSOR.TIME_MIX_LERP_W,
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MODEL_TENSOR.TIME_MIX_FIRST,
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MODEL_TENSOR.TIME_MIX_DECAY,
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MODEL_TENSOR.TIME_MIX_DECAY_W1,
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MODEL_TENSOR.TIME_MIX_DECAY_W2,
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MODEL_TENSOR.TIME_MIX_KEY,
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MODEL_TENSOR.TIME_MIX_VALUE,
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MODEL_TENSOR.TIME_MIX_RECEPTANCE,
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MODEL_TENSOR.TIME_MIX_GATE,
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MODEL_TENSOR.TIME_MIX_LN,
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MODEL_TENSOR.TIME_MIX_OUTPUT,
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MODEL_TENSOR.CHANNEL_MIX_LERP_K,
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MODEL_TENSOR.CHANNEL_MIX_LERP_R,
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MODEL_TENSOR.CHANNEL_MIX_KEY,
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MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE,
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MODEL_TENSOR.CHANNEL_MIX_VALUE,
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],
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MODEL_ARCH.MAMBA: [
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MODEL_ARCH.MAMBA: [
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.TOKEN_EMBD,
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MODEL_TENSOR.OUTPUT_NORM,
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MODEL_TENSOR.OUTPUT_NORM,
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@ -27,6 +27,7 @@ class TensorNameMap:
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"embedding.word_embeddings", # chatglm
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"embedding.word_embeddings", # chatglm
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"transformer.token_embeddings", # openelm
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"transformer.token_embeddings", # openelm
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"shared", # t5
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"shared", # t5
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"rwkv.embeddings", # rwkv
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),
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),
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# Token type embeddings
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# Token type embeddings
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@ -40,6 +41,7 @@ class TensorNameMap:
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"embeddings.LayerNorm", # bert
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"embeddings.LayerNorm", # bert
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"emb_ln", # nomic-bert
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"emb_ln", # nomic-bert
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"transformer.norm", # openelm
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"transformer.norm", # openelm
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"rwkv.blocks.0.pre_ln", # rwkv
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),
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),
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# Position embeddings
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# Position embeddings
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"word_embeddings_for_head", # persimmon
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"word_embeddings_for_head", # persimmon
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"lm_head.linear", # phi2
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"lm_head.linear", # phi2
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"output_layer", # chatglm
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"output_layer", # chatglm
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"head", # rwkv
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),
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),
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# Output norm
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# Output norm
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"encoder.final_layernorm", # chatglm
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"encoder.final_layernorm", # chatglm
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"transformer.norm", # openelm
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"transformer.norm", # openelm
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"model.norm", # nemotron
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"model.norm", # nemotron
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"rwkv.ln_out", # rwkv
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),
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),
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# Rope frequencies
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# Rope frequencies
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@ -108,12 +112,14 @@ class TensorNameMap:
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"transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
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"transformer.blocks.{bid}.norm_attn_norm.norm_1", # dbrx
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"encoder.layers.{bid}.input_layernorm", # chatglm
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"encoder.layers.{bid}.input_layernorm", # chatglm
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"transformer.layers.{bid}.attn_norm", # openelm
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"transformer.layers.{bid}.attn_norm", # openelm
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"rwkv.blocks.{bid}.ln1", # rwkv
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),
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),
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# Attention norm 2
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# Attention norm 2
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MODEL_TENSOR.ATTN_NORM_2: (
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MODEL_TENSOR.ATTN_NORM_2: (
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"transformer.h.{bid}.ln_attn", # falcon40b
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"transformer.h.{bid}.ln_attn", # falcon40b
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"encoder.layer.{bid}.layer_norm_1", # jina-v2-code
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"encoder.layer.{bid}.layer_norm_1", # jina-v2-code
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"rwkv.blocks.{bid}.ln2", # rwkv
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),
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),
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# Attention query-key-value
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# Attention query-key-value
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@ -434,6 +440,98 @@ class TensorNameMap:
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"backbone.layers.{bid}.mixer.out_proj",
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"backbone.layers.{bid}.mixer.out_proj",
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),
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),
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MODEL_TENSOR.TIME_MIX_W1: (
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"rwkv.blocks.{bid}.attention.time_maa_w1", # rwkv v6
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),
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MODEL_TENSOR.TIME_MIX_W2: (
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"rwkv.blocks.{bid}.attention.time_maa_w2", # rwkv v6
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),
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MODEL_TENSOR.TIME_MIX_LERP_X: (
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"rwkv.blocks.{bid}.attention.time_maa_x", # rwkv v6
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),
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MODEL_TENSOR.TIME_MIX_LERP_K: (
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"rwkv.blocks.{bid}.attention.time_maa_k", # rwkv v6
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),
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MODEL_TENSOR.TIME_MIX_LERP_V: (
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"rwkv.blocks.{bid}.attention.time_maa_v", # rwkv v6
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),
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MODEL_TENSOR.TIME_MIX_LERP_R: (
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"rwkv.blocks.{bid}.attention.time_maa_r", # rwkv v6
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),
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MODEL_TENSOR.TIME_MIX_LERP_G: (
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"rwkv.blocks.{bid}.attention.time_maa_g", # rwkv v6
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|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_LERP_W: (
|
||||||
|
"rwkv.blocks.{bid}.attention.time_maa_w", # rwkv v6
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_FIRST: (
|
||||||
|
"rwkv.blocks.{bid}.attention.time_faaaa", # rwkv v6
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_DECAY: (
|
||||||
|
"rwkv.blocks.{bid}.attention.time_decay", # rwkv v6
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_DECAY_W1: (
|
||||||
|
"rwkv.blocks.{bid}.attention.time_decay_w1", # rwkv v6
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_DECAY_W2: (
|
||||||
|
"rwkv.blocks.{bid}.attention.time_decay_w2", # rwkv v6
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_KEY: (
|
||||||
|
"rwkv.blocks.{bid}.attention.key", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_VALUE: (
|
||||||
|
"rwkv.blocks.{bid}.attention.value", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_RECEPTANCE: (
|
||||||
|
"rwkv.blocks.{bid}.attention.receptance", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_GATE: (
|
||||||
|
"rwkv.blocks.{bid}.attention.gate", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_LN: (
|
||||||
|
"rwkv.blocks.{bid}.attention.ln_x", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.TIME_MIX_OUTPUT: (
|
||||||
|
"rwkv.blocks.{bid}.attention.output", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.CHANNEL_MIX_LERP_K: (
|
||||||
|
"rwkv.blocks.{bid}.feed_forward.time_maa_k", # rwkv v6
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.CHANNEL_MIX_LERP_R: (
|
||||||
|
"rwkv.blocks.{bid}.feed_forward.time_maa_r", # rwkv v6
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.CHANNEL_MIX_KEY: (
|
||||||
|
"rwkv.blocks.{bid}.feed_forward.key", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: (
|
||||||
|
"rwkv.blocks.{bid}.feed_forward.receptance", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
|
MODEL_TENSOR.CHANNEL_MIX_VALUE: (
|
||||||
|
"rwkv.blocks.{bid}.feed_forward.value", # rwkv
|
||||||
|
),
|
||||||
|
|
||||||
MODEL_TENSOR.ATTN_Q_A: (
|
MODEL_TENSOR.ATTN_Q_A: (
|
||||||
"model.layers.{bid}.self_attn.q_a_proj", # deepseek2
|
"model.layers.{bid}.self_attn.q_a_proj", # deepseek2
|
||||||
),
|
),
|
||||||
|
|
|
||||||
Loading…
Add table
Add a link
Reference in a new issue