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mixer_seq_simple.py
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mixer_seq_simple.py
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# Copyright (c) 2023, Albert Gu, Tri Dao.
import math
from functools import partial
import json
import os
from collections import namedtuple
import torch
import torch.nn as nn
from mamba_ssm.models.config_mamba import MambaConfig
from mamba_ssm.modules.mamba_simple import Mamba, Block
from mamba_ssm.utils.generation import GenerationMixin
from mamba_ssm.utils.hf import load_config_hf, load_state_dict_hf
try:
from mamba_ssm.ops.triton.layernorm import RMSNorm, layer_norm_fn, rms_norm_fn
except ImportError:
RMSNorm, layer_norm_fn, rms_norm_fn = None, None, None
def create_block(
d_model,
ssm_cfg=None,
norm_epsilon=1e-5,
rms_norm=False,
residual_in_fp32=False,
fused_add_norm=False,
layer_idx=None,
device=None,
dtype=None,
):
if ssm_cfg is None:
ssm_cfg = {}
factory_kwargs = {"device": device, "dtype": dtype}
mixer_cls = partial(Mamba, layer_idx=layer_idx, **ssm_cfg, **factory_kwargs)
norm_cls = partial(
nn.LayerNorm if not rms_norm else RMSNorm, eps=norm_epsilon, **factory_kwargs
)
block = Block(
d_model,
mixer_cls,
norm_cls=norm_cls,
fused_add_norm=fused_add_norm,
residual_in_fp32=residual_in_fp32,
)
block.layer_idx = layer_idx
return block
# 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 MixerModel(nn.Module):
def __init__(
self,
d_model: int,
n_layer: int,
vocab_size: int,
ssm_cfg=None,
norm_epsilon: float = 1e-5,
rms_norm: bool = False,
initializer_cfg=None,
fused_add_norm=False,
residual_in_fp32=False,
device=None,
dtype=None,
) -> None:
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
self.residual_in_fp32 = residual_in_fp32
self.embedding = nn.Embedding(vocab_size, d_model, **factory_kwargs)
# We change the order of residual and layer norm:
# Instead of LN -> Attn / MLP -> Add, we do:
# Add -> LN -> Attn / MLP / Mixer, returning both the residual branch (output of Add) and
# the main branch (output of MLP / Mixer). The model definition is unchanged.
# This is for performance reason: we can fuse add + layer_norm.
self.fused_add_norm = fused_add_norm
if self.fused_add_norm:
if layer_norm_fn is None or rms_norm_fn is None:
raise ImportError("Failed to import Triton LayerNorm / RMSNorm kernels")
self.layers = nn.ModuleList(
[
create_block(
d_model,
ssm_cfg=ssm_cfg,
norm_epsilon=norm_epsilon,
rms_norm=rms_norm,
residual_in_fp32=residual_in_fp32,
fused_add_norm=fused_add_norm,
layer_idx=i,
**factory_kwargs,
)
for i in range(n_layer)
]
)
self.norm_f = (nn.LayerNorm if not rms_norm else RMSNorm)(
d_model, eps=norm_epsilon, **factory_kwargs
)
self.apply(
partial(
_init_weights,
n_layer=n_layer,
**(initializer_cfg if initializer_cfg is not None else {}),
)
)
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
return {
i: layer.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
for i, layer in enumerate(self.layers)
}
def forward(self, input_ids, inference_params=None):
hidden_states = self.embedding(input_ids)
residual = None
for layer in self.layers:
hidden_states, residual = layer(
hidden_states, residual, inference_params=inference_params
)
if not self.fused_add_norm:
residual = (hidden_states + residual) if residual is not None else hidden_states
hidden_states = self.norm_f(residual.to(dtype=self.norm_f.weight.dtype))
else:
# Set prenorm=False here since we don't need the residual
fused_add_norm_fn = rms_norm_fn if isinstance(self.norm_f, RMSNorm) else layer_norm_fn
hidden_states = fused_add_norm_fn(
hidden_states,
self.norm_f.weight,
self.norm_f.bias,
eps=self.norm_f.eps,
residual=residual,
prenorm=False,
residual_in_fp32=self.residual_in_fp32,
)
return hidden_states
class MambaLMHeadModel(nn.Module, GenerationMixin):
def __init__(
self,
config: MambaConfig,
initializer_cfg=None,
device=None,
dtype=None,
) -> None:
self.config = config
d_model = config.d_model
n_layer = config.n_layer
vocab_size = config.vocab_size
ssm_cfg = config.ssm_cfg
rms_norm = config.rms_norm
residual_in_fp32 = config.residual_in_fp32
fused_add_norm = config.fused_add_norm
pad_vocab_size_multiple = config.pad_vocab_size_multiple
factory_kwargs = {"device": device, "dtype": dtype}
super().__init__()
if vocab_size % pad_vocab_size_multiple != 0:
vocab_size += pad_vocab_size_multiple - (vocab_size % pad_vocab_size_multiple)
self.backbone = MixerModel(
d_model=d_model,
n_layer=n_layer,
vocab_size=vocab_size,
ssm_cfg=ssm_cfg,
rms_norm=rms_norm,
initializer_cfg=initializer_cfg,
fused_add_norm=fused_add_norm,
residual_in_fp32=residual_in_fp32,
**factory_kwargs,
)
self.lm_head = nn.Linear(d_model, vocab_size, bias=False, **factory_kwargs)
# Initialize weights and apply final processing
self.apply(
partial(
_init_weights,
n_layer=n_layer,
**(initializer_cfg if initializer_cfg is not None else {}),
)
)
self.tie_weights()
def tie_weights(self):
self.lm_head.weight = self.backbone.embedding.weight
def allocate_inference_cache(self, batch_size, max_seqlen, dtype=None, **kwargs):
return self.backbone.allocate_inference_cache(batch_size, max_seqlen, dtype=dtype, **kwargs)
def forward(self, input_ids, position_ids=None, inference_params=None, num_last_tokens=0):
"""
"position_ids" is just to be compatible with Transformer generation. We don't use it.
num_last_tokens: if > 0, only return the logits for the last n tokens
"""
hidden_states = self.backbone(input_ids, inference_params=inference_params)
if num_last_tokens > 0:
hidden_states = hidden_states[:, -num_last_tokens:]
lm_logits = self.lm_head(hidden_states)
CausalLMOutput = namedtuple("CausalLMOutput", ["logits"])
return CausalLMOutput(logits=lm_logits)
@classmethod
def from_pretrained(cls, pretrained_model_name, device=None, dtype=None, **kwargs):
config_data = load_config_hf(pretrained_model_name)
config = MambaConfig(**config_data)
model = cls(config, device=device, dtype=dtype, **kwargs)
model.load_state_dict(load_state_dict_hf(pretrained_model_name, device=device, dtype=dtype))
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)