rwkv7: converter script simplification
Signed-off-by: Molly Sophia <mollysophia379@gmail.com>
This commit is contained in:
Molly Sophia
parent
2175aebdb1
commit
922ebbe93d
3 changed files with 98 additions and 104 deletions
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@ -3480,7 +3480,7 @@ class RWKV6Qwen2Model(Rwkv6Model):
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yield (new_name, data)
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@Model.register("Rwkv7ForCausalLM")
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@Model.register("Rwkv7ForCausalLM", "RWKV7ForCausalLM")
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class Rwkv7Model(Rwkv6Model):
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model_arch = gguf.MODEL_ARCH.RWKV7
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@ -3489,16 +3489,26 @@ class Rwkv7Model(Rwkv6Model):
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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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try:
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head_size = self.hparams["head_size"]
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layer_norm_eps = self.hparams["layer_norm_epsilon"]
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except KeyError:
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head_size = self.hparams["head_dim"]
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layer_norm_eps = self.hparams["norm_eps"]
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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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intermediate_size = self.hparams["intermediate_size"] if self.hparams["intermediate_size"] is not None else (hidden_size * 4)
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# ICLR: In-Context-Learning-Rate
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lora_rank_decay = self.hparams["lora_rank_decay"] if self.hparams["lora_rank_decay"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
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lora_rank_iclr = self.hparams["lora_rank_iclr"] if self.hparams["lora_rank_iclr"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
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lora_rank_value_residual_mix = self.hparams["lora_rank_value_residual_mix"] if self.hparams["lora_rank_value_residual_mix"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
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lora_rank_gate = self.hparams["lora_rank_gate"] if self.hparams["lora_rank_gate"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)
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try:
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lora_rank_decay = self.hparams["lora_rank_decay"] if self.hparams["lora_rank_decay"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
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lora_rank_iclr = self.hparams["lora_rank_iclr"] if self.hparams["lora_rank_iclr"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
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lora_rank_value_residual_mix = self.hparams["lora_rank_value_residual_mix"] if self.hparams["lora_rank_value_residual_mix"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
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lora_rank_gate = self.hparams["lora_rank_gate"] if self.hparams["lora_rank_gate"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)
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except KeyError:
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lora_rank_decay = self.hparams["decay_low_rank_dim"] if self.hparams["decay_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
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lora_rank_iclr = self.hparams["a_low_rank_dim"] if self.hparams["a_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.8)
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lora_rank_value_residual_mix = self.hparams["v_low_rank_dim"] if self.hparams["v_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.5, 1.3)
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lora_rank_gate = self.hparams["gate_low_rank_dim"] if self.hparams["gate_low_rank_dim"] is not None else self.calc_lora_rank(hidden_size, 0.8, 0.6)
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# RWKV isn't context limited
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self.gguf_writer.add_context_length(1048576)
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@ -3517,17 +3527,43 @@ class Rwkv7Model(Rwkv6Model):
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self.gguf_writer.add_head_count(0)
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lerp_weights: dict[int, dict[str, Tensor]] = {}
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lora_needs_transpose: bool = True
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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# unify tensor names here to make life easier
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name = name.replace("blocks", "layers").replace("ffn", "feed_forward")
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name = name.replace("self_attn", "attention").replace("attn", "attention")
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name = name.replace("time_mixer.", "")
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# lora layer names in fla-hub's impl
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if "_lora.lora" in name:
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self.lora_needs_transpose = False
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name = name.replace("_lora.lora.0.weight", "1.weight")
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name = name.replace("_lora.lora.2.weight", "2.weight")
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name = name.replace("_lora.lora.2.bias", "0.weight")
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name = name.replace("feed_forward_norm", "ln2")
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name = name.replace("g_norm", "ln_x")
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if "attention.v" in name and "value" not in self.map_tensor_name(name) and bid == 0:
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# some models have dummy v0/v1/v2 on first layer while others don't
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# ignore them all since they are not used
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return
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if bid is not None and "attention.x_" in name:
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try:
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self.lerp_weights[bid][name] = data_torch
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except KeyError:
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self.lerp_weights[bid] = {name: data_torch}
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if all(f"model.blocks.{bid}.attention.x_{i}" in self.lerp_weights[bid].keys() for i in ["r", "w", "k", "v", "a", "g"]):
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if "attention.x_x" in name:
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# already concatenated
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new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
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data = torch.stack([self.lerp_weights[bid][f"model.blocks.{bid}.attention.x_{i}"].squeeze(0) for i in ["r", "w", "k", "v", "a", "g"]], dim=0)
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data = data_torch.reshape(6, 1, -1)
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yield (new_name, data)
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else:
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try:
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self.lerp_weights[bid][name] = data_torch
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except KeyError:
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self.lerp_weights[bid] = {name: data_torch}
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if all(f"model.layers.{bid}.attention.x_{i}" in self.lerp_weights[bid].keys() for i in ["r", "w", "k", "v", "a", "g"]):
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new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
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data = torch.stack([self.lerp_weights[bid][f"model.layers.{bid}.attention.x_{i}"].squeeze(0) for i in ["r", "w", "k", "v", "a", "g"]], dim=0)
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yield (new_name, data)
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return
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else:
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data_torch = data_torch.squeeze()
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@ -3536,7 +3572,7 @@ class Rwkv7Model(Rwkv6Model):
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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 any(
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if self.lora_needs_transpose and any(
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new_name.endswith(t) for t in [
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"time_mix_w1.weight", "time_mix_w2.weight",
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"time_mix_a1.weight", "time_mix_a2.weight",
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@ -3558,7 +3594,7 @@ class Rwkv7Model(Rwkv6Model):
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@Model.register("RwkvHybridForCausalLM")
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class ARwkv7Model(Model):
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class ARwkv7Model(Rwkv7Model):
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model_arch = gguf.MODEL_ARCH.ARWKV7
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def set_vocab(self):
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@ -3599,41 +3635,6 @@ class ARwkv7Model(Model):
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# required by llama.cpp, unused
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self.gguf_writer.add_head_count(0)
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lerp_weights: dict[int, dict[str, Tensor]] = {}
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def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
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if bid is not None and "self_attn.time_mixer.x_" in name:
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try:
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self.lerp_weights[bid][name] = data_torch
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except KeyError:
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self.lerp_weights[bid] = {name: data_torch}
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if all(f"model.layers.{bid}.self_attn.time_mixer.x_{i}" in self.lerp_weights[bid].keys() for i in ["r", "w", "k", "v", "a", "g"]):
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new_name = f"blk.{bid}.time_mix_lerp_fused.weight"
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data = torch.stack([self.lerp_weights[bid][f"model.layers.{bid}.self_attn.time_mixer.x_{i}"].squeeze(0) for i in ["r", "w", "k", "v", "a", "g"]], dim=0)
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yield (new_name, data)
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return
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else:
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data_torch = data_torch.squeeze()
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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 any(
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new_name.endswith(t) for t in [
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"time_mix_w1.weight", "time_mix_w2.weight",
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"time_mix_a1.weight", "time_mix_a2.weight",
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"time_mix_v1.weight", "time_mix_v2.weight",
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"time_mix_g1.weight", "time_mix_g2.weight",
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]
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):
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data_torch = data_torch.transpose(0, 1)
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if 'r_k' in new_name:
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data_torch = data_torch.flatten()
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yield (new_name, data_torch)
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@Model.register("MambaForCausalLM", "MambaLMHeadModel", "FalconMambaForCausalLM")
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class MambaModel(Model):
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@ -44,6 +44,7 @@ class TensorNameMap:
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"transformer.norm", # openelm
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"rwkv.blocks.0.pre_ln", # rwkv6
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"model.pre_ln", # rwkv7
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"model.layers.0.pre_norm", # rwkv7
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"backbone.norm", # wavtokenizer
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),
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@ -126,7 +127,7 @@ class TensorNameMap:
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"encoder.layers.{bid}.input_layernorm", # chatglm
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"transformer.layers.{bid}.attn_norm", # openelm
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"rwkv.blocks.{bid}.ln1", # rwkv6
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"model.blocks.{bid}.ln1", # rwkv7
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"model.layers.{bid}.ln1", # rwkv7
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),
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# Attention norm 2
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@ -134,7 +135,7 @@ class TensorNameMap:
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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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"rwkv.blocks.{bid}.ln2", # rwkv6
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"model.blocks.{bid}.ln2", # rwkv7
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"model.layers.{bid}.ln2", # rwkv7
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),
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# Attention query-key-value
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@ -468,77 +469,63 @@ class TensorNameMap:
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),
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MODEL_TENSOR.TIME_MIX_W0: (
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"model.blocks.{bid}.attention.w0", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.w0", # arwkv7
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"model.layers.{bid}.attention.w0", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_W1: (
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"rwkv.blocks.{bid}.attention.time_maa_w1", # rwkv6
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"model.layers.{bid}.self_attn.time_maa_w1", # rwkv6qwen2
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"model.blocks.{bid}.attention.w1", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.w1", # arwkv7
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"model.layers.{bid}.attention.w1", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_W2: (
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"rwkv.blocks.{bid}.attention.time_maa_w2", # rwkv6
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"model.layers.{bid}.self_attn.time_maa_w2", # rwkv6qwen2
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"model.blocks.{bid}.attention.w2", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.w2", # arwkv7
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"model.layers.{bid}.attention.w2", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_A0: (
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"model.blocks.{bid}.attention.a0", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.a0", # arwkv7
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"model.layers.{bid}.attention.a0", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_A1: (
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"model.blocks.{bid}.attention.a1", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.a1", # arwkv7
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"model.layers.{bid}.attention.a1", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_A2: (
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"model.blocks.{bid}.attention.a2", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.a2", # arwkv7
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"model.layers.{bid}.attention.a2", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_V0: (
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"model.blocks.{bid}.attention.v0", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.v0", # arwkv7
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"model.layers.{bid}.attention.v0", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_V1: (
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"model.blocks.{bid}.attention.v1", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.v1", # arwkv7
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"model.layers.{bid}.attention.v1", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_V2: (
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"model.blocks.{bid}.attention.v2", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.v2", # arwkv7
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"model.layers.{bid}.attention.v2", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_G1: (
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"model.blocks.{bid}.attention.g1", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.g1", # arwkv7
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"model.layers.{bid}.attention.g1", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_G2: (
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"model.blocks.{bid}.attention.g2", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.g2", # arwkv7
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"model.layers.{bid}.attention.g2", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_K_K: (
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"model.blocks.{bid}.attention.k_k", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.k_k", # arwkv7
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"model.layers.{bid}.attention.k_k", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_K_A: (
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"model.blocks.{bid}.attention.k_a", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.k_a", # arwkv7
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"model.layers.{bid}.attention.k_a", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_R_K: (
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"model.blocks.{bid}.attention.r_k", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.r_k", # arwkv7
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"model.layers.{bid}.attention.r_k", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_LERP_X: (
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@ -591,47 +578,46 @@ class TensorNameMap:
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),
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MODEL_TENSOR.TIME_MIX_KEY: (
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"rwkv.blocks.{bid}.attention.key", # rwkv6
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"model.layers.{bid}.self_attn.k_proj", # rwkv6qwen2
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"model.blocks.{bid}.attention.key", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.key.weight", # arwkv7
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"rwkv.blocks.{bid}.attention.key", # rwkv6
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"model.layers.{bid}.self_attn.k_proj", # rwkv6qwen2
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"model.layers.{bid}.attention.key", # rwkv7
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"model.layers.{bid}.attention.k_proj", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_VALUE: (
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"rwkv.blocks.{bid}.attention.value", # rwkv6
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"model.layers.{bid}.self_attn.v_proj", # rwkv6qwen2
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"model.blocks.{bid}.attention.value", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.value.weight", # arwkv7
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"rwkv.blocks.{bid}.attention.value", # rwkv6
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"model.layers.{bid}.self_attn.v_proj", # rwkv6qwen2
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"model.layers.{bid}.attention.value", # rwkv7
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"model.layers.{bid}.attention.v_proj", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_RECEPTANCE: (
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"rwkv.blocks.{bid}.attention.receptance", # rwkv6
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"model.layers.{bid}.self_attn.q_proj", # rwkv6qwen2
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"model.blocks.{bid}.attention.receptance", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.receptance.weight", # arwkv7
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"rwkv.blocks.{bid}.attention.receptance", # rwkv6
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"model.layers.{bid}.self_attn.q_proj", # rwkv6qwen2
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"model.layers.{bid}.attention.receptance", # rwkv7
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"model.layers.{bid}.attention.r_proj", # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_GATE: (
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"rwkv.blocks.{bid}.attention.gate", # rwkv6
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"model.layers.{bid}.self_attn.gate", # rwkv6qwen2
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"model.layers.{bid}.self_attn.time_mixer.gate.weight", # arwkv7
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),
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MODEL_TENSOR.TIME_MIX_LN: (
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"rwkv.blocks.{bid}.attention.ln_x", # rwkv6
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"model.blocks.{bid}.attention.ln_x" # rwkv7
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"model.layers.{bid}.attention.ln_x" # rwkv7
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),
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MODEL_TENSOR.TIME_MIX_OUTPUT: (
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"rwkv.blocks.{bid}.attention.output", # rwkv
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"model.layers.{bid}.self_attn.o_proj", # rwkv6qwen2
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"model.blocks.{bid}.attention.output", # rwkv7
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"model.layers.{bid}.self_attn.time_mixer.output.weight", # arwkv7
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"rwkv.blocks.{bid}.attention.output", # rwkv
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"model.layers.{bid}.self_attn.o_proj", # rwkv6qwen2
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"model.layers.{bid}.attention.output", # rwkv7
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"model.layers.{bid}.attention.o_proj", # rwkv7
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),
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MODEL_TENSOR.CHANNEL_MIX_LERP_K: (
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"rwkv.blocks.{bid}.feed_forward.time_maa_k", # rwkv6
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"model.blocks.{bid}.feed_forward.x_k", # rwkv7
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"model.layers.{bid}.feed_forward.x_k", # rwkv7
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),
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MODEL_TENSOR.CHANNEL_MIX_LERP_R: (
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|
@ -640,7 +626,7 @@ class TensorNameMap:
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|||
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MODEL_TENSOR.CHANNEL_MIX_KEY: (
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"rwkv.blocks.{bid}.feed_forward.key", # rwkv6
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"model.blocks.{bid}.feed_forward.key", # rwkv7
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"model.layers.{bid}.feed_forward.key", # rwkv7
|
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),
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MODEL_TENSOR.CHANNEL_MIX_RECEPTANCE: (
|
||||
|
|
@ -649,7 +635,7 @@ class TensorNameMap:
|
|||
|
||||
MODEL_TENSOR.CHANNEL_MIX_VALUE: (
|
||||
"rwkv.blocks.{bid}.feed_forward.value", # rwkv6
|
||||
"model.blocks.{bid}.feed_forward.value", # rwkv7
|
||||
"model.layers.{bid}.feed_forward.value", # rwkv7
|
||||
),
|
||||
|
||||
MODEL_TENSOR.ATTN_Q_A: (
|
||||
|
|
|
|||
|
|
@ -3396,9 +3396,16 @@ bool llama_model::load_tensors(llama_model_loader & ml) {
|
|||
layer.time_mix_a1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A1, "weight", i), {n_embd, n_lora_iclr}, 0);
|
||||
layer.time_mix_a2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_A2, "weight", i), {n_lora_iclr, n_embd}, 0);
|
||||
|
||||
layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
|
||||
layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);
|
||||
layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);
|
||||
if (i == 0) {
|
||||
// actually not used
|
||||
layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
|
||||
layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_iclr}, 0);
|
||||
layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_iclr, n_embd}, 0);
|
||||
} else {
|
||||
layer.time_mix_v0 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V0, "weight", i), {n_embd}, 0);
|
||||
layer.time_mix_v1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V1, "weight", i), {n_embd, n_lora_value_res_mix}, 0);
|
||||
layer.time_mix_v2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_V2, "weight", i), {n_lora_value_res_mix, n_embd}, 0);
|
||||
}
|
||||
|
||||
layer.time_mix_g1 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G1, "weight", i), {n_embd, n_lora_gate}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
layer.time_mix_g2 = create_tensor(tn(LLM_TENSOR_TIME_MIX_G2, "weight", i), {n_lora_gate, n_embd}, llama_model_loader::TENSOR_NOT_REQUIRED);
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue