more wip
This commit is contained in:
Wing Lian
99
src/axolotl/integrations/rrt/cli/convert.py
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99
src/axolotl/integrations/rrt/cli/convert.py
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from pathlib import Path
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from typing import re
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import safetensors
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import torch
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from huggingface_hub import snapshot_download
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from tqdm import tqdm
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from transformers import AutoConfig
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def extract_layer_number(key):
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"""Extract layer number from parameter key."""
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match = re.search(r'layers\.(\d+)\.', key)
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return int(match.group(1)) if match else None
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def iter_parameter_weights(model_path, device="cpu"):
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"""
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iterator over parameter weights in the model shards
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:param model_path: Path to model shards
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:param device: Computing device
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:return: generator yielding (parameter key, parameter weight, layer index) tuples
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"""
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shards = list(model_path.glob('model*.safetensors'))
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if not shards:
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raise ValueError(f"No model shards found in {model_path}")
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for shard in tqdm(shards, desc="Processing shards"):
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with safetensors.safe_open(shard, framework='pt', device=device) as f:
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for key in f.keys():
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layer_idx = extract_layer_number(key)
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weight = f.get_tensor(key)
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yield key, weight, layer_idx
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def iter_recursive_parameter_weights(model_path, modules_to_recurse: list[str], device="cpu", recurse_layers=12):
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# setup placeholder state_dict for recursive weights, need to keep in float32 precision
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# to avoid precision loss when averaging weights across layers
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rrt_avg_model_state_dict = {}
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# iterate over all parameter weights in the model shards
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for key, weight, layer_idx in iter_parameter_weights(model_path):
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# get the matching module name in modules_to_recurse for the current parameter key
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matched_module_name = next(
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(module for module in modules_to_recurse if module in key),
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None
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)
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if matched_module_name is None:
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if "input_layernorm" in key:
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# map to input_layernorm_list in the recursive layers and account for the layer_idx and loop_idx
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yield
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else:
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yield key, weight
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recurse_idx = layer_idx % recurse_layers
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suffix = f"{recurse_idx}.{matched_module_name}"
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prefix = f"model.layers.{suffix}."
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if rrt_avg_model_state_dict.get(suffix) is None:
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# setup as storage for suffix with torch.stack
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rrt_avg_model_state_dict[suffix] = torch.stack([weight.to(torch.float32).detach().cpu()])
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else:
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rrt_avg_model_state_dict[suffix] = torch.cat([rrt_avg_model_state_dict[suffix], weight.to(torch.float32).detach().cpu()])
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for module_name in modules_to_recurse:
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for recurse_idx in range(recurse_layers):
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suffix = f"{recurse_idx}.{module_name}"
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prefix = f"model.layers.{suffix}."
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avg_weight = rrt_avg_model_state_dict[suffix].mean(dim=0)
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yield f"{prefix}.weight", avg_weight
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def convert_llama_to_rrt(model_name, output_dir, recurse_layers: int = 12):
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modules_to_recurse = [
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"self_attn.q_proj",
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"self_attn.k_proj",
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"self_attn.v_proj",
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"self_attn.o_proj",
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"mlp.down_proj",
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"mlp.gate_proj",
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"mlp.up_proj",
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]
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config = AutoConfig.from_pretrained(model_name)
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num_hidden_layers = config.num_hidden_layers
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if num_hidden_layers % recurse_layers != 0:
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raise ValueError(
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f"The number of hidden layers ({num_hidden_layers}) in the model must be "
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f"divisible by the recurse layers ({recurse_layers})"
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)
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model_path = Path(snapshot_download(model_name))
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# create a new state_dict to store the RRT model weights
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rrt_model_state_dict = {}
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for key, weight in iter_recursive_parameter_weights(model_path, modules_to_recurse, device="cpu", recurse_layers=recurse_layers):
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rrt_model_state_dict[key] = weight.to(torch.bfloat16).detach().cpu()
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# split_torch_state_dict_into_shards(...)
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73
src/axolotl/integrations/rrt/modeling/linear.py
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73
src/axolotl/integrations/rrt/modeling/linear.py
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import torch
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import torch.nn.functional as F
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from torch import nn, transpose
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class RelaxedRecursiveDoraLinear(nn.Module):
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"""
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A single linear layer that is "shared" across multiple loop iterations,
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but each iteration has its own DoRA offsets (A_i, B_i, magnitude_i).
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The constructor expects you to specify:
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- in_features, out_features
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- B: number of loop iterations (i.e., how many times we "unroll")
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- fan_in_fan_out: pass True if your underlying base weight is transposed, etc.
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The forward(...) expects an additional argument "loop_idx" in [0..B-1],
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which picks out the iteration-specific DoRA offsets.
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"""
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def __init__(
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self,
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in_features: int,
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out_features: int,
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B: int,
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rank: int,
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fan_in_fan_out: bool = False,
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bias: bool = True,
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use_dora: bool = True,
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):
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super().__init__()
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self.B = B
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self.fan_in_fan_out = fan_in_fan_out
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self.weight_base = nn.Parameter(torch.empty(out_features, in_features))
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self.use_bias = bias
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if self.use_bias:
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self.bias = nn.Parameter(torch.zeros(out_features))
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else:
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self.register_parameter("bias", None)
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self.lora_A_list = nn.ParameterList([nn.Parameter(torch.zeros(rank, in_features)) for _ in range(B)])
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self.lora_B_list = nn.ParameterList([nn.Parameter(torch.zeros(out_features, rank)) for _ in range(B)])
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if use_dora:
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self.lora_magnitude_vector_list = nn.ParameterList([nn.Parameter(torch.ones(out_features)) for _ in range(B)])
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def forward(self, x, loop_idx: int):
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"""
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:param x: hidden state of shape (batch_size, seq_len, in_features)
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:param loop_idx:
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:return:
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"""
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w_base = self.weight_base
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w_base = w_base.to(x.dtype)
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lora_A: torch.Tensor = self.lora_A_list[loop_idx]
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lora_B: torch.Tensor = self.lora_B_list[loop_idx]
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magnitude_vector: torch.Tensor = self.lora_magnitude_vector_list[loop_idx]
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base_out: torch.Tensor = F.linear(x, transpose(w_base, self.fan_in_fan_out), self.bias)
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x_eye: torch.Tensor = torch.eye(lora_A.shape[1], device=lora_A.device, dtype=x.dtype)
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w_dora_full: torch.Tensor = lora_B(lora_A(x_eye))
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lora_out: torch.Tensor = F.linear(x, w_dora_full, bias=None)
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w_dora_norm: torch.Tensor = self.get_weight_norm(w_base, w_dora_full.detach())
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w_dora_norm = w_dora_norm.detach()
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scale_factor = (magnitude_vector / w_dora_norm).unsqueeze(0) # shape [1, out_features]
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result_dora = (scale_factor - 1) * base_out + scale_factor * lora_out
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return result_dora
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@@ -0,0 +1,322 @@
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from typing import Tuple, Optional, Unpack, Callable, Union
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import torch
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from torch import nn
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from transformers import LlamaConfig, Cache, logger, DynamicCache
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from transformers.activations import ACT2FN
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from transformers.modeling_flash_attention_utils import FlashAttentionKwargs
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from transformers.modeling_outputs import BaseModelOutputWithPast
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from transformers.modeling_utils import ALL_ATTENTION_FUNCTIONS
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from transformers.models.llama.modeling_llama import apply_rotary_pos_emb, eager_attention_forward, LlamaRMSNorm, \
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LlamaForCausalLM, LlamaPreTrainedModel, LlamaModel, LlamaRotaryEmbedding
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from axolotl.integrations.rrt.modeling.linear import RelaxedRecursiveDoraLinear
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class RelaxedRecursiveLlamaConfig(LlamaConfig):
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"""
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Configuration for Relaxed Recursive Llama.
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"""
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recurse_layers: int
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rank: int
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class RelaxedRecursiveLlamaMLP(nn.Module):
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def __init__(self, config: RelaxedRecursiveLlamaConfig):
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super().__init__()
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recurse_loops = config.num_layers // config.recurse_layers
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self.config = config
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self.hidden_size = config.hidden_size
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self.intermediate_size = config.intermediate_size
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self.gate_proj = RelaxedRecursiveDoraLinear(self.hidden_size, self.intermediate_size, recurse_loops, config.rank, bias=config.mlp_bias)
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self.up_proj = RelaxedRecursiveDoraLinear(self.hidden_size, self.intermediate_size, recurse_loops, config.rank, bias=config.mlp_bias)
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self.down_proj = RelaxedRecursiveDoraLinear(self.intermediate_size, self.hidden_size, recurse_loops, config.rank, bias=config.mlp_bias)
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self.act_fn = ACT2FN[config.hidden_act]
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def forward(self, x, loop_idx: int):
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down_proj = self.down_proj(self.act_fn(self.gate_proj(x, loop_idx)) * self.up_proj(x, loop_idx), loop_idx)
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return down_proj
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class RelaxedRecursiveLlamaAttention(nn.Module):
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"""
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A single attention layer of the Relaxed Recursive Llama.
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"""
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def __init__(self, config: RelaxedRecursiveLlamaConfig, layer_idx: int):
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super().__init__()
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recurse_loops = config.num_layers // config.recurse_layers
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self.config = config
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self.layer_idx = layer_idx
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self.head_dim = getattr(config, "head_dim", config.hidden_size // config.num_attention_heads)
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self.num_key_value_groups = config.num_attention_heads // config.num_key_value_heads
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self.scaling = self.head_dim**-0.5
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self.attention_dropout = config.attention_dropout
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self.is_causal = True
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self.q_proj = RelaxedRecursiveDoraLinear(
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config.hidden_size, config.num_attention_heads * self.head_dim, recurse_loops, config.rank, bias=config.attention_bias
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)
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self.k_proj = RelaxedRecursiveDoraLinear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, recurse_loops, config.rank, bias=config.attention_bias
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)
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self.v_proj = RelaxedRecursiveDoraLinear(
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config.hidden_size, config.num_key_value_heads * self.head_dim, recurse_loops, config.rank, bias=config.attention_bias
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)
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self.o_proj = RelaxedRecursiveDoraLinear(
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config.num_attention_heads * self.head_dim, config.hidden_size, recurse_loops, config.rank, bias=config.attention_bias
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)
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def forward(
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self,
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hidden_states: torch.Tensor,
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position_embeddings: Tuple[torch.Tensor, torch.Tensor],
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attention_mask: Optional[torch.Tensor],
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loop_idx: int,
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past_key_value: Optional[Cache] = None,
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cache_position: Optional[torch.LongTensor] = None,
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**kwargs: Unpack[FlashAttentionKwargs],
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) -> Tuple[torch.Tensor, Optional[torch.Tensor], Optional[Tuple[torch.Tensor]]]:
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input_shape = hidden_states.shape[:-1]
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hidden_shape = (*input_shape, -1, self.head_dim)
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query_states = self.q_proj(hidden_states, loop_idx).view(hidden_shape).transpose(1, 2)
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key_states = self.k_proj(hidden_states, loop_idx).view(hidden_shape).transpose(1, 2)
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value_states = self.v_proj(hidden_states, loop_idx).view(hidden_shape).transpose(1, 2)
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cos, sin = position_embeddings
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query_states, key_states = apply_rotary_pos_emb(query_states, key_states, cos, sin)
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if past_key_value is not None:
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# sin and cos are specific to RoPE models; cache_position needed for the static cache
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cache_kwargs = {"sin": sin, "cos": cos, "cache_position": cache_position}
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key_states, value_states = past_key_value.update(key_states, value_states, self.layer_idx, cache_kwargs)
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attention_interface: Callable = eager_attention_forward
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if self.config._attn_implementation != "eager":
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if self.config._attn_implementation == "sdpa" and kwargs.get("output_attentions", False):
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logger.warning_once(
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"`torch.nn.functional.scaled_dot_product_attention` does not support `output_attentions=True`. Falling back to "
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'eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.'
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)
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else:
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attention_interface = ALL_ATTENTION_FUNCTIONS[self.config._attn_implementation]
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attn_output, attn_weights = attention_interface(
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self,
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query_states,
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key_states,
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value_states,
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attention_mask,
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dropout=0.0 if not self.training else self.attention_dropout,
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scaling=self.scaling,
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**kwargs,
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)
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attn_output = attn_output.reshape(*input_shape, -1).contiguous()
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attn_output = self.o_proj(attn_output, loop_idx)
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return attn_output, attn_weights
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class RelaxedRecursiveLlamaDecoderLayer(nn.Module):
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"""
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A single layer of the Relaxed Recursive Llama decoder.
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"""
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def __init__(self, config: LlamaConfig, layer_idx: int):
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super().__init__()
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recurse_loops = config.num_layers // config.recurse_layers
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self.hidden_size = config.hidden_size
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self.self_attn = RelaxedRecursiveLlamaAttention(config=config, layer_idx=layer_idx)
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self.mlp = RelaxedRecursiveLlamaMLP(config)
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self.input_layernorm_list = nn.ModuleList([LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) for _ in range(recurse_loops)])
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self.post_attention_layernorm_list = nn.ModuleList([LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps) for _ in range(recurse_loops)])
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def forward(
|
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self,
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hidden_states: torch.Tensor,
|
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loop_idx: int,
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attention_mask: Optional[torch.Tensor] = None,
|
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position_ids: Optional[torch.LongTensor] = None,
|
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past_key_value: Optional[Cache] = None,
|
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output_attentions: Optional[bool] = False,
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use_cache: Optional[bool] = False,
|
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cache_position: Optional[torch.LongTensor] = None,
|
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position_embeddings: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, # necessary, but kept here for BC
|
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**kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> Tuple[torch.FloatTensor, Optional[Tuple[torch.FloatTensor, torch.FloatTensor]]]:
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residual = hidden_states
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|
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hidden_states = self.input_layernorm_list[loop_idx](hidden_states)
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# Self Attention
|
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hidden_states, self_attn_weights = self.self_attn(
|
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hidden_states=hidden_states,
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attention_mask=attention_mask,
|
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loop_idx=loop_idx,
|
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position_ids=position_ids,
|
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past_key_value=past_key_value,
|
||||
output_attentions=output_attentions,
|
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use_cache=use_cache,
|
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cache_position=cache_position,
|
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position_embeddings=position_embeddings,
|
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**kwargs,
|
||||
)
|
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hidden_states = residual + hidden_states
|
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|
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# Fully Connected
|
||||
residual = hidden_states
|
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hidden_states = self.post_attention_layernorm_list[loop_idx](hidden_states)
|
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hidden_states = self.mlp(hidden_states, loop_idx)
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hidden_states = residual + hidden_states
|
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|
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outputs = (hidden_states,)
|
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if output_attentions:
|
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outputs += (self_attn_weights,)
|
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|
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return outputs
|
||||
|
||||
|
||||
class RelaxedRecursiveLlamaModel(LlamaModel):
|
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def __init__(self, config):
|
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super(LlamaModel, self).__init__(config)
|
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self.recurse_loops = config.num_layers // config.recurse_layers
|
||||
self.padding_idx = config.pad_token_id
|
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self.vocab_size = config.vocab_size
|
||||
|
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size, self.padding_idx)
|
||||
self.layers = nn.ModuleList(
|
||||
[RelaxedRecursiveLlamaDecoderLayer(config, layer_idx) for layer_idx in range(config.recurse_layers)]
|
||||
)
|
||||
self.norm = LlamaRMSNorm(config.hidden_size, eps=config.rms_norm_eps)
|
||||
self.rotary_emb = LlamaRotaryEmbedding(config=config)
|
||||
self.gradient_checkpointing = False
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
def forward(
|
||||
self,
|
||||
input_ids: torch.LongTensor = None,
|
||||
attention_mask: Optional[torch.Tensor] = None,
|
||||
position_ids: Optional[torch.LongTensor] = None,
|
||||
past_key_values: Optional[Cache] = None,
|
||||
inputs_embeds: Optional[torch.FloatTensor] = None,
|
||||
use_cache: Optional[bool] = None,
|
||||
output_attentions: Optional[bool] = None,
|
||||
output_hidden_states: Optional[bool] = None,
|
||||
return_dict: Optional[bool] = None,
|
||||
cache_position: Optional[torch.LongTensor] = None,
|
||||
**flash_attn_kwargs: Unpack[FlashAttentionKwargs],
|
||||
) -> Union[Tuple, BaseModelOutputWithPast]:
|
||||
output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
|
||||
output_hidden_states = (
|
||||
output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states
|
||||
)
|
||||
use_cache = use_cache if use_cache is not None else self.config.use_cache
|
||||
return_dict = return_dict if return_dict is not None else self.config.use_return_dict
|
||||
|
||||
if (input_ids is None) ^ (inputs_embeds is not None):
|
||||
raise ValueError("You must specify exactly one of input_ids or inputs_embeds")
|
||||
|
||||
if self.gradient_checkpointing and self.training and use_cache:
|
||||
logger.warning_once(
|
||||
"`use_cache=True` is incompatible with gradient checkpointing. Setting `use_cache=False`."
|
||||
)
|
||||
use_cache = False
|
||||
|
||||
if inputs_embeds is None:
|
||||
inputs_embeds = self.embed_tokens(input_ids)
|
||||
|
||||
if use_cache and past_key_values is None:
|
||||
past_key_values = DynamicCache()
|
||||
|
||||
if cache_position is None:
|
||||
past_seen_tokens = past_key_values.get_seq_length() if past_key_values is not None else 0
|
||||
cache_position = torch.arange(
|
||||
past_seen_tokens, past_seen_tokens + inputs_embeds.shape[1], device=inputs_embeds.device
|
||||
)
|
||||
|
||||
if position_ids is None:
|
||||
position_ids = cache_position.unsqueeze(0)
|
||||
|
||||
causal_mask = self._update_causal_mask(
|
||||
attention_mask, inputs_embeds, cache_position, past_key_values, output_attentions
|
||||
)
|
||||
|
||||
hidden_states = inputs_embeds
|
||||
|
||||
# create position embeddings to be shared across the decoder layers
|
||||
position_embeddings = self.rotary_emb(hidden_states, position_ids)
|
||||
|
||||
# decoder layers
|
||||
all_hidden_states = () if output_hidden_states else None
|
||||
all_self_attns = () if output_attentions else None
|
||||
|
||||
for loop_idx in range(self.recurse_loops):
|
||||
for decoder_layer in self.layers[: self.config.recurse_layers]:
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
if self.gradient_checkpointing and self.training:
|
||||
layer_outputs = self._gradient_checkpointing_func(
|
||||
decoder_layer.__call__,
|
||||
hidden_states,
|
||||
loop_idx,
|
||||
causal_mask,
|
||||
position_ids,
|
||||
past_key_values,
|
||||
output_attentions,
|
||||
use_cache,
|
||||
cache_position,
|
||||
position_embeddings,
|
||||
)
|
||||
else:
|
||||
layer_outputs = decoder_layer(
|
||||
hidden_states,
|
||||
loop_idx,
|
||||
attention_mask=causal_mask,
|
||||
position_ids=position_ids,
|
||||
past_key_value=past_key_values,
|
||||
output_attentions=output_attentions,
|
||||
use_cache=use_cache,
|
||||
cache_position=cache_position,
|
||||
position_embeddings=position_embeddings,
|
||||
**flash_attn_kwargs,
|
||||
)
|
||||
|
||||
hidden_states = layer_outputs[0]
|
||||
|
||||
if output_attentions:
|
||||
all_self_attns += (layer_outputs[1],)
|
||||
|
||||
hidden_states = self.norm(hidden_states)
|
||||
|
||||
# add hidden states from the last decoder layer
|
||||
if output_hidden_states:
|
||||
all_hidden_states += (hidden_states,)
|
||||
|
||||
output = BaseModelOutputWithPast(
|
||||
last_hidden_state=hidden_states,
|
||||
past_key_values=past_key_values if use_cache else None,
|
||||
hidden_states=all_hidden_states,
|
||||
attentions=all_self_attns,
|
||||
)
|
||||
return output if return_dict else output.to_tuple()
|
||||
|
||||
|
||||
class RelaxedRecursiveLlamaForCausalLM(LlamaForCausalLM):
|
||||
def __init__(self, config):
|
||||
super(LlamaForCausalLM, self).__init__(config)
|
||||
self.model = RelaxedRecursiveLlamaModel(config)
|
||||
self.vocab_size = config.vocab_size
|
||||
self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
|
||||
|
||||
# Initialize weights and apply final processing
|
||||
self.post_init()
|
||||
|
||||
|
||||
Reference in New Issue
Block a user