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import logging
from typing import Literal, Optional, Union
import functools
from functools import partial
import torch
import torch.nn as nn
from torch import Tensor
import math
import os
from mamba_block import MambaBlock, MambaDecoder
from mamba_config import MambaConfig
from hf_utils import *
import os, json
from transformers.utils import WEIGHTS_NAME, CONFIG_NAME
from transformers.utils.hub import cached_file
# https://github.com/huggingface/transformers/blob/c28d04e9e252a1a099944e325685f14d242ecdcd/src/transformers/models/gpt2/modeling_gpt2.py#L454
def _init_weights(
module,
n_layer,
initializer_range=0.02, # Now only used for embedding layer.
rescale_prenorm_residual=True,
n_residuals_per_layer=1, # Change to 2 if we have MLP
):
if isinstance(module, nn.Linear):
if module.bias is not None:
if not getattr(module.bias, "_no_reinit", False):
nn.init.zeros_(module.bias)
elif isinstance(module, nn.Embedding):
nn.init.normal_(module.weight, std=initializer_range)
if rescale_prenorm_residual:
# Reinitialize selected weights subject to the OpenAI GPT-2 Paper Scheme:
# > A modified initialization which accounts for the accumulation on the residual path with model depth. Scale
# > the weights of residual layers at initialization by a factor of 1/√N where N is the # of residual layers.
# > -- GPT-2 :: https://openai.com/blog/better-language-models/
#
# Reference (Megatron-LM): https://github.com/NVIDIA/Megatron-LM/blob/main/megatron/model/gpt_model.py
for name, p in module.named_parameters():
if name in ["out_proj.weight", "fc2.weight"]:
# Special Scaled Initialization --> There are 2 Layer Norms per Transformer Block
# Following Pytorch init, except scale by 1/sqrt(2 * n_layer)
# We need to reinit p since this code could be called multiple times
# Having just p *= scale would repeatedly scale it down
nn.init.kaiming_uniform_(p, a=math.sqrt(5))
with torch.no_grad():
p /= math.sqrt(n_residuals_per_layer * n_layer)
class MambaModel(nn.Module):
def __init__(
self,
config: MambaConfig,
max_sequence_length: int,
pre_process: bool = True,
post_process: bool = True,
fp16_lm_cross_entropy: bool = False,
parallel_output: bool = True,
share_embeddings_and_output_weights: bool = True,
initializer_cfg = None,
) -> None:
super().__init__()
self.config: MambaConfig = config
self.max_sequence_length = max_sequence_length
self.pre_process = pre_process
self.post_process = post_process
self.fp16_lm_cross_entropy = fp16_lm_cross_entropy
self.parallel_output = parallel_output
self.share_embeddings_and_output_weights = share_embeddings_and_output_weights
if self.pre_process:
self.embedding = nn.Embedding(self.config.vocab_size, self.config.hidden_size)
self.decoder = MambaDecoder(
config = self.config,
pre_process = self.pre_process,
post_process = self.post_process,
)
if post_process:
self.output_layer = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias = self.config.add_bias_linear)
if self.share_embeddings_and_output_weights and (self.pre_process or self.post_process):
self.initialize_last_stage_with_word_embeddings()
# apply weight initialization
self.apply(
partial(
_init_weights,
n_layer=self.config.num_layers,
**(initializer_cfg if initializer_cfg is not None else {}),
)
)
def initialize_last_stage_with_word_embeddings(self):
with torch.no_grad():
self.output_layer.weight = self.embedding.weight
def forward(
self,
input_ids,
position_ids = None,
decoder_input: Tensor = None,
labels: Tensor = None,
inference_params=None,
) -> Tensor:
if decoder_input is not None:
pass
elif self.pre_process:
decoder_input = self.embedding(input_ids)
else:
decoder_input = None
hidden_states = self.decoder(
hidden_states=decoder_input,
residual=None,
inference_params=inference_params,
)
if not self.post_process:
return hidden_states
logits = self.output_layer(hidden_states)
return logits.contiguous()
@classmethod
def from_pretrained(cls, pretrained_model_name = None, checkpoint_name=None, config_name=None, **kwargs):
if pretrained_model_name is not None:
json_config = load_config_hf(pretrained_model_name)
loaded = load_state_dict_hf(pretrained_model_name)
elif checkpoint_name is not None and config_name is not None:
with open(config_name, 'r') as f:
jsonstr = f.read()
json_config = json.loads(jsonstr)
loaded = torch.load(checkpoint_name, map_location='cpu')
else:
return
model_state_dict = loaded["model"]
config = MambaConfig(
num_layers=json_config['num_layers'],
hidden_size=json_config['hidden_size'],
state_size=json_config['state_size'],
conv_dimension=json_config['conv_dimension'],
vocab_size=json_config['vocab_size'],
expansion_factor=json_config['expansion_factor'],
mamba_moe_layers=json_config['mamba_moe_layers'],
ffn_hidden_size=json_config['ffn_hidden_size'],
bias = json_config['add_bias_linear'],
add_bias_linear = json_config['add_bias_linear'],
gated_linear_unit = json_config['swiglu']
)
model = MambaModel(config=config, max_sequence_length=json_config['max_sequence_length'], **kwargs)
# make keys match
model_state_dict["embedding.weight"] = model_state_dict["embedding.word_embeddings.weight"].clone()
model_state_dict["output_layer.weight"] = model_state_dict["embedding.word_embeddings.weight"].clone()
model_state_dict["embedding.word_embeddings.weight"] = None
model_state_dict.pop("embedding.word_embeddings.weight")
model.load_state_dict(loaded["model"])
return model
def save_pretrained(self, save_directory):
"""
Minimal implementation of save_pretrained for MambaLMHeadModel.
Save the model and its configuration file to a directory.
"""
# Ensure save_directory exists
if not os.path.exists(save_directory):
os.makedirs(save_directory)
# Save the model's state_dict
model_path = os.path.join(save_directory, 'pytorch_model.bin')
torch.save(self.state_dict(), model_path)
# Save the configuration of the model
config_path = os.path.join(save_directory, 'config.json')
with open(config_path, 'w') as f:
json.dump(self.config.__dict__, f)
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