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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