Following this instruction :
1. Strictly adhere to PEP-8 standards, ensuring code is clean, well-structured, and follows Python's style guide for readability.
2. Use appropriate modules like `typing` for type hints, ensuring code clarity, maintainability, and leveraging Python's standard libraries effectively.
3. Focus on time, space, robustness, scalability; write optimized, efficient code that can handle large datasets and scale seamlessly.
4. Implement comprehensive error handling, catching exceptions gracefully, providing meaningful error messages, and ensuring code robustness under unexpected conditions.
5. Ensure the developer agent writes advanced, maintainable Python code, balancing performance and readability while adhering to best practices.



TASK :user model (torch.nn module) generalised any module , we apply below algorithm,note activation 8 bit , remaining all 1.5bit follow algorithm

BitNet b1.58: A Detailed Explanation
BitNet b1.58 is a variant of the BitNet architecture, a type of Transformer model designed for Large Language Models (LLMs). This variant uses a ternary system where each parameter (weight) can be -1, 0, or 1, as opposed to the standard 16-bit floating values (FP16 or BF16) used in conventional LLMs. This results in significant cost reductions in terms of:

Latency
Memory usage
Throughput
Energy consumption
while maintaining comparable performance to full-precision models.

Advantages of BitNet b1.58
Pareto Improvement: BitNet b1.58 provides a Pareto improvement by reducing inference costs while maintaining model performance. This means it achieves a better balance between cost and performance compared to traditional LLMs.
Simplified Computation: BitNet b1.58 replaces floating-point operations with integer additions for matrix multiplication, leading to significant energy savings and faster computation.
Reduced Memory Footprint: It has a lower memory footprint, enabling faster and more efficient loading of weights, and making it suitable for deployment on resource-constrained devices.
Enhanced Modeling Capability: The introduction of '0' in the weight values allows for explicit feature filtering, improving the model's ability to capture important information.
Open-Source Compatibility: BitNet b1.58 incorporates LLaMA-alike components (RMSNorm, SwiGLU, rotary embedding), ensuring easy integration with popular open-source LLM frameworks.
Quantization Function
The key to achieving 1.58-bit precision lies in the quantization function used to constrain the weights to the ternary set {-1, 0, 1}. BitNet b1.58 uses the following quantization function:

Equation 1:

W
~
=
R
o
u
n
d
C
l
i
p
(
W
γ
+
ϵ
,
−
1
,
1
)
W
~
 =RoundClip( 
γ+ϵ
W
​
 ,−1,1)

Equation 2:

R
o
u
n
d
C
l
i
p
(
x
,
a
,
b
)
=
m
a
x
(
a
,
m
i
n
(
b
,
r
o
u
n
d
(
x
)
)
)
RoundClip(x,a,b)=max(a,min(b,round(x)))

Equation 3:

γ
=
1
n
m
∑
i
j
∣
W
i
j
∣
γ= 
nm
1
​
 ∑ 
ij
​
 ∣W 
ij
​
 ∣

Explanation:

The weight matrix (W) is scaled by its average absolute value (γ) plus a small constant (ϵ).
The RoundClip function then rounds each scaled value to the nearest integer within the range of -1 to 1.
This quantization process ensures that the weights are represented using only three values, effectively reducing the model's memory footprint and computational complexity.

Performance and Cost Comparisons
BitNet b1.58 has been shown to match the performance of full-precision LLaMA LLMs in terms of both perplexity and end-task accuracy, starting from a model size of 3B.

Key findings:

BitNet b1.58 models are significantly faster and consume less memory than their FP16 LLaMA counterparts across different model sizes. For example, the 70B BitNet b1.58 model is 4.1 times faster than the 70B LLaMA LLM.
The energy consumption of BitNet b1.58 models is drastically lower due to the use of integer additions instead of floating-point operations. It saves approximately 71.4 times the energy for matrix multiplication on 7nm chips.
BitNet b1.58 models achieve higher throughput by supporting larger batch sizes. The 70B model can handle 11 times the batch size of the 70B LLaMA LLM, leading to an 8.9 times increase in throughput.
These results highlight the significant cost advantages of using BitNet b1.58 without compromising performance.

Future Applications
The development of BitNet b1.58 has opened up new possibilities for LLM applications, especially in resource-constrained environments:

1-bit Mixture-of-Experts (MoE) LLMs: BitNet b1.58 can address the high memory consumption and communication overhead challenges associated with MoE models, making them more practical for deployment.
Native Support for Long Sequences: The reduced memory footprint of BitNet b1.58 allows for handling longer input sequences, which is crucial for tasks requiring extensive context.
LLMs on Edge and Mobile Devices: The efficient nature of BitNet b1.58 enables the deployment of powerful LLMs on edge and mobile devices, expanding the possibilities for AI applications in these environments.
Specialized Hardware for 1-bit LLMs: The unique computation paradigm of BitNet b1.58 necessitates the development of new hardware specifically optimized for 1-bit LLMs, potentially leading to even greater efficiency and performance gains.
BitNet b1.58 represents a significant step towards a new era of LLMs that are both powerful and cost-effective. Its innovative approach to model quantization opens up numerous opportunities for research, development, and deployment of LLMs across various domains