From 22ad463639818148efc924fdfa5ad048a40e69c7 Mon Sep 17 00:00:00 2001 From: Mohit Deopujari Date: Mon, 2 Oct 2023 17:55:42 -0700 Subject: [PATCH 1/7] Test text generation using prompts --- tests/baselines/bloomz_7b1.json | 12 +++++++ tests/baselines/llama_2_13b_hf.json | 12 +++++++ tests/baselines/llama_2_70b_hf.json | 12 +++++++ tests/test_text_generation_example.py | 49 +++++++++++++++++++++++---- 4 files changed, 78 insertions(+), 7 deletions(-) create mode 100644 tests/baselines/bloomz_7b1.json create mode 100644 tests/baselines/llama_2_13b_hf.json create mode 100644 tests/baselines/llama_2_70b_hf.json mode change 100644 => 100755 tests/test_text_generation_example.py diff --git a/tests/baselines/bloomz_7b1.json b/tests/baselines/bloomz_7b1.json new file mode 100644 index 0000000000..ac148c3fbd --- /dev/null +++ b/tests/baselines/bloomz_7b1.json @@ -0,0 +1,12 @@ +{ + "prompt": { + "model_name_or_path": "/root/software/data/pytorch/bloom/models--bigscience--bloom-7b1/snapshots/e83e90ba86f87f74aa2731cdab25ccf33976bd66/", + "max_new_tokens": 1024, + "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", + "distribution": { + "single_card": { + "throughput": 59.06 + } + } + } +} \ No newline at end of file diff --git a/tests/baselines/llama_2_13b_hf.json b/tests/baselines/llama_2_13b_hf.json new file mode 100644 index 0000000000..5dd3acf8b9 --- /dev/null +++ b/tests/baselines/llama_2_13b_hf.json @@ -0,0 +1,12 @@ +{ + "prompt": { + "model_name_or_path": "/root/czhao/WORK/llama-v2/test-01/13B-hf", + "max_new_tokens": 1024, + "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", + "distribution": { + "single_card": { + "throughput": 128.11 + } + } + } +} \ No newline at end of file diff --git a/tests/baselines/llama_2_70b_hf.json b/tests/baselines/llama_2_70b_hf.json new file mode 100644 index 0000000000..11a5f52eec --- /dev/null +++ b/tests/baselines/llama_2_70b_hf.json @@ -0,0 +1,12 @@ +{ + "prompt": { + "model_name_or_path": "/root/local_dataset/pytorch/huggingface/hub/models--meta-llama--Llama-2-70b-hf/snapshots/bc7d6a85f909e2af7678537df0c771ae7b0e8010", + "max_new_tokens": 1024, + "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", + "distribution": { + "deepspeed": { + "throughput": 59.06 + } + } + } +} \ No newline at end of file diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py old mode 100644 new mode 100755 index 598f2bba21..a28da13f02 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -1,4 +1,5 @@ import json +import math import re import subprocess from pathlib import Path @@ -6,7 +7,7 @@ import pytest -from .test_examples import TIME_PERF_FACTOR +from .test_examples import BASELINE_DIRECTORY, TIME_PERF_FACTOR MODELS_TO_TEST = { @@ -27,10 +28,22 @@ "deepspeed": [ ("bigscience/bloomz-7b1", 27.34439410425298), ], + "prompt": [ + ("bigscience/bloomz-7b1", False), + ("meta-llama/llama-2-13b-hf", False), + ("meta-llama/llama-2-70b-hf", True), + ], } -def _test_text_generation(model_name: str, baseline: float, token: str, deepspeed: bool = False, world_size: int = 8): +def _test_text_generation( + model_name: str, + baseline: float = math.inf, + token: str = "", + deepspeed: bool = False, + world_size: int = 8, + prompt: bool = False, +): command = ["python3"] path_to_example_dir = Path(__file__).resolve().parent.parent / "examples" @@ -43,21 +56,36 @@ def _test_text_generation(model_name: str, baseline: float, token: str, deepspee command += [ f"{path_to_example_dir / 'text-generation' / 'run_generation.py'}", - f"--model_name_or_path {model_name}", "--batch_size 1", "--use_hpu_graphs", "--use_kv_cache", - "--max_new_tokens 100", ] - if not deepspeed: + if prompt or not deepspeed: command.append("--bf16") + if prompt: + path_to_baseline = BASELINE_DIRECTORY / Path(model_name.split("/")[-1].replace("-", "_")).with_suffix(".json") + with path_to_baseline.open("r") as json_file: + baseline = json.load(json_file)["prompt"] + + distribution = "single_card" if not deepspeed else "deepspeed" + + command += [ + f"--max_new_tokens {baseline.get('max_new_tokens')}", + f"--model_name_or_path {baseline.get('model_name_or_path')}", + f"--prompt '{baseline.get('prompt')}'", + "--bucket_size 50", + ] + else: + command += ["--max_new_tokens 100", f"--model_name_or_path {model_name}"] + with TemporaryDirectory() as tmp_dir: command.append(f"--output_dir {tmp_dir}") print(f"\n\nCommand to test: {' '.join(command)}\n") - command.append(f"--token {token.value}") + if not prompt: + command.append(f"--token {token.value}") pattern = re.compile(r"([\"\'].+?[\"\'])|\s") command = [x for y in command for x in re.split(pattern, y) if x] @@ -77,7 +105,9 @@ def _test_text_generation(model_name: str, baseline: float, token: str, deepspee results = json.load(fp) # Ensure performance requirements (throughput) are met - assert results["throughput"] >= (2 - TIME_PERF_FACTOR) * baseline + assert results["throughput"] >= (2 - TIME_PERF_FACTOR) * baseline.get("distribution").get(distribution).get( + "throughput" + ) @pytest.mark.parametrize("model_name, baseline", MODELS_TO_TEST["bf16"]) @@ -88,3 +118,8 @@ def test_text_generation_bf16(model_name: str, baseline: float, token: str): @pytest.mark.parametrize("model_name, baseline", MODELS_TO_TEST["deepspeed"]) def test_text_generation_deepspeed(model_name: str, baseline: float, token: str): _test_text_generation(model_name, baseline, token, deepspeed=True) + + +@pytest.mark.parametrize("model_name, deepspeed", MODELS_TO_TEST["prompt"]) +def test_text_generation_prompt(model_name: str, deepspeed: bool): + _test_text_generation(model_name, deepspeed=deepspeed, prompt=True) From f18646d5740302d940247f61c31b7d7e2aba037a Mon Sep 17 00:00:00 2001 From: Mohit Deopujari Date: Wed, 4 Oct 2023 17:28:05 -0700 Subject: [PATCH 2/7] Update model path --- tests/baselines/llama_2_13b_hf.json | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/baselines/llama_2_13b_hf.json b/tests/baselines/llama_2_13b_hf.json index 5dd3acf8b9..a6eabc1068 100644 --- a/tests/baselines/llama_2_13b_hf.json +++ b/tests/baselines/llama_2_13b_hf.json @@ -1,6 +1,6 @@ { "prompt": { - "model_name_or_path": "/root/czhao/WORK/llama-v2/test-01/13B-hf", + "model_name_or_path": "/root/software/data/pytorch/llama-v2/test-01/13B-hf", "max_new_tokens": 1024, "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", "distribution": { @@ -9,4 +9,4 @@ } } } -} \ No newline at end of file +} From a0f6f96b5b8b6153a083f7ecb173c6d9a6562d72 Mon Sep 17 00:00:00 2001 From: Mohit Deopujari Date: Thu, 5 Oct 2023 14:46:04 -0700 Subject: [PATCH 3/7] Update baseline numbers --- tests/baselines/bloomz_7b1.json | 2 +- tests/baselines/llama_2_13b_hf.json | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/baselines/bloomz_7b1.json b/tests/baselines/bloomz_7b1.json index ac148c3fbd..a00a8a849e 100644 --- a/tests/baselines/bloomz_7b1.json +++ b/tests/baselines/bloomz_7b1.json @@ -5,7 +5,7 @@ "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", "distribution": { "single_card": { - "throughput": 59.06 + "throughput": 31.06 } } } diff --git a/tests/baselines/llama_2_13b_hf.json b/tests/baselines/llama_2_13b_hf.json index a6eabc1068..3899f251f7 100644 --- a/tests/baselines/llama_2_13b_hf.json +++ b/tests/baselines/llama_2_13b_hf.json @@ -5,7 +5,7 @@ "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", "distribution": { "single_card": { - "throughput": 128.11 + "throughput": 70.60 } } } From 17328362932c5af9e947ac6ca1b5fd268d756aa8 Mon Sep 17 00:00:00 2001 From: Mohit Deopujari Date: Thu, 5 Oct 2023 16:26:08 -0700 Subject: [PATCH 4/7] Update test_text_generation_example.py Disabling deepspeed prompt case for now --- tests/test_text_generation_example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py index a28da13f02..e20cd3a4ac 100755 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -31,7 +31,7 @@ "prompt": [ ("bigscience/bloomz-7b1", False), ("meta-llama/llama-2-13b-hf", False), - ("meta-llama/llama-2-70b-hf", True), + #("meta-llama/llama-2-70b-hf", True), ], } From a0280b099ce756d381b9f3c255240f423150dc82 Mon Sep 17 00:00:00 2001 From: Mohit Deopujari Date: Fri, 6 Oct 2023 15:23:47 -0700 Subject: [PATCH 5/7] Fixed styling --- tests/test_text_generation_example.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py index e20cd3a4ac..a2a713d3fc 100755 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -31,7 +31,7 @@ "prompt": [ ("bigscience/bloomz-7b1", False), ("meta-llama/llama-2-13b-hf", False), - #("meta-llama/llama-2-70b-hf", True), + # ("meta-llama/llama-2-70b-hf", True), ], } From 077be842f665ae64af5e97834558b444d0d4722a Mon Sep 17 00:00:00 2001 From: Mohit Deopujari Date: Tue, 10 Oct 2023 12:03:13 -0700 Subject: [PATCH 6/7] Enabled accuracy checks --- tests/baselines/bloomz_7b1.json | 3 ++- tests/baselines/llama_2_13b_hf.json | 3 ++- tests/test_text_generation_example.py | 24 ++++++++++++++++++------ 3 files changed, 22 insertions(+), 8 deletions(-) diff --git a/tests/baselines/bloomz_7b1.json b/tests/baselines/bloomz_7b1.json index a00a8a849e..f84f5e566c 100644 --- a/tests/baselines/bloomz_7b1.json +++ b/tests/baselines/bloomz_7b1.json @@ -7,6 +7,7 @@ "single_card": { "throughput": 31.06 } - } + }, + "expected_output": "'While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve' developing novel algorithms and architectures that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding across multiple layers of the network stack.Introduction\nThe study of the dynamics of the classical and quantum mechanical systems is a very important and interesting subject. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe" } } \ No newline at end of file diff --git a/tests/baselines/llama_2_13b_hf.json b/tests/baselines/llama_2_13b_hf.json index 3899f251f7..d51dc053d3 100644 --- a/tests/baselines/llama_2_13b_hf.json +++ b/tests/baselines/llama_2_13b_hf.json @@ -7,6 +7,7 @@ "single_card": { "throughput": 70.60 } - } + }, + "expected_output": "'While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve' Home > News > featured news > The Future of Work: How AI, Automation, and Remote Work Are Redefining the Modern Workplace\nThe Future of Work: How AI, Automation, and Remote Work Are Redefining the Modern Workplace\nForbes | September 10, 2020\nThe modern workplace is undergoing a significant transformation, driven by advances in artificial intelligence (AI), automation, and remote work. These changes are redefining the way we work, where we work, and how we collaborate with each other. Here are some key trends that are shaping the future of work:\n1. Remote Work: With the rise of remote work, employees are no longer tied to a specific location. Instead, they can work from anywhere, at any time, as long as they have a stable internet connection. This has led to a more flexible and autonomous work environment, where employees can choose the work style that suits them best.\n2. AI and Automation: AI and automation are increasingly being used to automate repetitive tasks, freeing up employees to focus on more creative and strategic work. AI-powered tools like chatbots, virtual assistants, and machine learning algorithms are being used to streamline processes, improve efficiency, and enhance decision-making.\n3. Virtual Collaboration: With remote work becoming the norm, virtual collaboration tools are becoming more important than ever. Virtual collaboration platforms like Zoom, Slack, and Microsoft Teams are being used to facilitate communication, collaboration, and teamwork among remote employees.\n4. Skills Shift: As AI and automation take over routine tasks, there is a growing need for skills that are more human-centric, such as creativity, empathy, and problem-solving. Employees will need to develop these skills to remain relevant in the modern workplace.\n5. Lifelong Learning: With the pace of technological change accelerating, lifelong learning is becoming more important than ever. Employees will need to continuously update their skills and knowledge to remain relevant in the modern workplace.\n6. Flexible Work Arrangements: With the rise of remote work, flexible work arrangements are becoming more common. Employers are offering flexible work schedules, compressed workweeks, and other arrangements to accommodate the needs of their employees.\n7. Well-being and Mental Health: With the pressure to perform at work increasing, there is a growing need for employers to prioritize the well-being and mental health of their employees. Employers are offering wellness programs, mental health resources, and other support services to help employees manage stress and maintain their overall well-being.\n8. Diversity, Equity, and Inclusion: As the modern workplace becomes more diverse, there is a growing need for employers to prioritize diversity, equity, and inclusion. Employers are implementing diversity and inclusion initiatives, such as unconscious bias training and diversity and inclusion committees, to create a more inclusive work environment.\n9. Virtual Reality and Augmented Reality: Virtual reality (VR) and augmented reality (AR) are being used to enhance training, collaboration, and communication in the modern workplace. VR and AR can simulate real-world environments, allowing employees to practice and learn new skills in a safe and controlled environment.\n10. Blockchain and Trust: Blockchain technology is being used to enhance trust and transparency in the modern workplace. Blockchain can be used to verify credentials, track supply chains, and ensure the authenticity of data.\nIn conclusion, the future of work is being shaped by AI, automation, remote work, and other emerging technologies. As these technologies continue to evolve, it is important for employers and employees to adapt and embrace the changes that are happening in the modern workplace. By doing so, we can create a more flexible, inclusive, and productive work environment that benefits everyone. Home > News > Industry News > The Importance of Properly Sizing Your Solar Panel System\nThe Importance of Properly Sizing Your Solar Panel System\nSizing a solar panel system is a critical step in the installation process. Proper sizing ensures that the system meets your energy needs and provides the best possible return on investment. Here are some reasons why properly sizing your solar panel system is important:\n1. Energy Production: A solar panel system that is too small will not produce enough energy to meet your needs, while a system that is too large will waste money and resources. Proper sizing ensures that your system produces" } } diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py index a2a713d3fc..7b2a7e2972 100755 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -43,6 +43,7 @@ def _test_text_generation( deepspeed: bool = False, world_size: int = 8, prompt: bool = False, + check_accuracy: bool = False, ): command = ["python3"] path_to_example_dir = Path(__file__).resolve().parent.parent / "examples" @@ -61,7 +62,7 @@ def _test_text_generation( "--use_kv_cache", ] - if prompt or not deepspeed: + if (prompt or not deepspeed) and not check_accuracy: command.append("--bf16") if prompt: @@ -75,8 +76,12 @@ def _test_text_generation( f"--max_new_tokens {baseline.get('max_new_tokens')}", f"--model_name_or_path {baseline.get('model_name_or_path')}", f"--prompt '{baseline.get('prompt')}'", - "--bucket_size 50", ] + # Bigger bucket_size with FP32 to avoid OOM + if check_accuracy: + command += ["--bucket_size 250"] + else: + command += ["--bucket_size 50"] else: command += ["--max_new_tokens 100", f"--model_name_or_path {model_name}"] @@ -104,10 +109,14 @@ def _test_text_generation( with open(Path(tmp_dir) / "results.json") as fp: results = json.load(fp) - # Ensure performance requirements (throughput) are met - assert results["throughput"] >= (2 - TIME_PERF_FACTOR) * baseline.get("distribution").get(distribution).get( - "throughput" - ) + # Ensure accuracy requirements are met + if check_accuracy: + assert results["output"][0] == baseline.get("expected_output") + else: + # Ensure performance requirements (throughput) are met + assert results["throughput"] >= (2 - TIME_PERF_FACTOR) * baseline.get("distribution").get( + distribution + ).get("throughput") @pytest.mark.parametrize("model_name, baseline", MODELS_TO_TEST["bf16"]) @@ -122,4 +131,7 @@ def test_text_generation_deepspeed(model_name: str, baseline: float, token: str) @pytest.mark.parametrize("model_name, deepspeed", MODELS_TO_TEST["prompt"]) def test_text_generation_prompt(model_name: str, deepspeed: bool): + # check performance only in bf16 _test_text_generation(model_name, deepspeed=deepspeed, prompt=True) + # check accuracy of generated output only in fp32 + _test_text_generation(model_name, deepspeed=deepspeed, prompt=True, check_accuracy=True) From 30afc0a9ed3b3450fea3f332fcf6d8e671310e75 Mon Sep 17 00:00:00 2001 From: Mohit Deopujari Date: Tue, 12 Dec 2023 14:07:35 -0800 Subject: [PATCH 7/7] Added gaudi2 config in the baseline json --- tests/baselines/bloomz_7b1.json | 22 ++++++++++++---------- tests/baselines/llama_2_13b_hf.json | 22 ++++++++++++---------- tests/baselines/llama_2_70b_hf.json | 16 +++++++++------- tests/test_text_generation_example.py | 4 +++- 4 files changed, 36 insertions(+), 28 deletions(-) diff --git a/tests/baselines/bloomz_7b1.json b/tests/baselines/bloomz_7b1.json index f84f5e566c..21e112fcd5 100644 --- a/tests/baselines/bloomz_7b1.json +++ b/tests/baselines/bloomz_7b1.json @@ -1,13 +1,15 @@ { - "prompt": { - "model_name_or_path": "/root/software/data/pytorch/bloom/models--bigscience--bloom-7b1/snapshots/e83e90ba86f87f74aa2731cdab25ccf33976bd66/", - "max_new_tokens": 1024, - "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", - "distribution": { - "single_card": { - "throughput": 31.06 - } - }, - "expected_output": "'While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve' developing novel algorithms and architectures that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding across multiple layers of the network stack.Introduction\nThe study of the dynamics of the classical and quantum mechanical systems is a very important and interesting subject. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe" + "gaudi2": { + "prompt": { + "model_name_or_path": "/root/software/data/pytorch/bloom/models--bigscience--bloom-7b1/snapshots/e83e90ba86f87f74aa2731cdab25ccf33976bd66/", + "max_new_tokens": 1024, + "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", + "distribution": { + "single_card": { + "throughput": 31.06 + } + }, + "expected_output": "'While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve' developing novel algorithms and architectures that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding across multiple layers of the network stack.Introduction\nThe study of the dynamics of the classical and quantum mechanical systems is a very important and interesting subject. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe the motion of bodies in the absence of electromagnetic fields. The classical mechanics is a very important branch of physics, which is used to describe" + } } } \ No newline at end of file diff --git a/tests/baselines/llama_2_13b_hf.json b/tests/baselines/llama_2_13b_hf.json index d51dc053d3..cdeb1eecc4 100644 --- a/tests/baselines/llama_2_13b_hf.json +++ b/tests/baselines/llama_2_13b_hf.json @@ -1,13 +1,15 @@ { - "prompt": { - "model_name_or_path": "/root/software/data/pytorch/llama-v2/test-01/13B-hf", - "max_new_tokens": 1024, - "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", - "distribution": { - "single_card": { - "throughput": 70.60 - } - }, - "expected_output": "'While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve' Home > News > featured news > The Future of Work: How AI, Automation, and Remote Work Are Redefining the Modern Workplace\nThe Future of Work: How AI, Automation, and Remote Work Are Redefining the Modern Workplace\nForbes | September 10, 2020\nThe modern workplace is undergoing a significant transformation, driven by advances in artificial intelligence (AI), automation, and remote work. These changes are redefining the way we work, where we work, and how we collaborate with each other. Here are some key trends that are shaping the future of work:\n1. Remote Work: With the rise of remote work, employees are no longer tied to a specific location. Instead, they can work from anywhere, at any time, as long as they have a stable internet connection. This has led to a more flexible and autonomous work environment, where employees can choose the work style that suits them best.\n2. AI and Automation: AI and automation are increasingly being used to automate repetitive tasks, freeing up employees to focus on more creative and strategic work. AI-powered tools like chatbots, virtual assistants, and machine learning algorithms are being used to streamline processes, improve efficiency, and enhance decision-making.\n3. Virtual Collaboration: With remote work becoming the norm, virtual collaboration tools are becoming more important than ever. Virtual collaboration platforms like Zoom, Slack, and Microsoft Teams are being used to facilitate communication, collaboration, and teamwork among remote employees.\n4. Skills Shift: As AI and automation take over routine tasks, there is a growing need for skills that are more human-centric, such as creativity, empathy, and problem-solving. Employees will need to develop these skills to remain relevant in the modern workplace.\n5. Lifelong Learning: With the pace of technological change accelerating, lifelong learning is becoming more important than ever. Employees will need to continuously update their skills and knowledge to remain relevant in the modern workplace.\n6. Flexible Work Arrangements: With the rise of remote work, flexible work arrangements are becoming more common. Employers are offering flexible work schedules, compressed workweeks, and other arrangements to accommodate the needs of their employees.\n7. Well-being and Mental Health: With the pressure to perform at work increasing, there is a growing need for employers to prioritize the well-being and mental health of their employees. Employers are offering wellness programs, mental health resources, and other support services to help employees manage stress and maintain their overall well-being.\n8. Diversity, Equity, and Inclusion: As the modern workplace becomes more diverse, there is a growing need for employers to prioritize diversity, equity, and inclusion. Employers are implementing diversity and inclusion initiatives, such as unconscious bias training and diversity and inclusion committees, to create a more inclusive work environment.\n9. Virtual Reality and Augmented Reality: Virtual reality (VR) and augmented reality (AR) are being used to enhance training, collaboration, and communication in the modern workplace. VR and AR can simulate real-world environments, allowing employees to practice and learn new skills in a safe and controlled environment.\n10. Blockchain and Trust: Blockchain technology is being used to enhance trust and transparency in the modern workplace. Blockchain can be used to verify credentials, track supply chains, and ensure the authenticity of data.\nIn conclusion, the future of work is being shaped by AI, automation, remote work, and other emerging technologies. As these technologies continue to evolve, it is important for employers and employees to adapt and embrace the changes that are happening in the modern workplace. By doing so, we can create a more flexible, inclusive, and productive work environment that benefits everyone. Home > News > Industry News > The Importance of Properly Sizing Your Solar Panel System\nThe Importance of Properly Sizing Your Solar Panel System\nSizing a solar panel system is a critical step in the installation process. Proper sizing ensures that the system meets your energy needs and provides the best possible return on investment. Here are some reasons why properly sizing your solar panel system is important:\n1. Energy Production: A solar panel system that is too small will not produce enough energy to meet your needs, while a system that is too large will waste money and resources. Proper sizing ensures that your system produces" + "gaudi2": { + "prompt": { + "model_name_or_path": "/root/software/data/pytorch/llama-v2/test-01/13B-hf", + "max_new_tokens": 1024, + "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", + "distribution": { + "single_card": { + "throughput": 70.60 + } + }, + "expected_output": "'While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve' Home > News > featured news > The Future of Work: How AI, Automation, and Remote Work Are Redefining the Modern Workplace\nThe Future of Work: How AI, Automation, and Remote Work Are Redefining the Modern Workplace\nForbes | September 10, 2020\nThe modern workplace is undergoing a significant transformation, driven by advances in artificial intelligence (AI), automation, and remote work. These changes are redefining the way we work, where we work, and how we collaborate with each other. Here are some key trends that are shaping the future of work:\n1. Remote Work: With the rise of remote work, employees are no longer tied to a specific location. Instead, they can work from anywhere, at any time, as long as they have a stable internet connection. This has led to a more flexible and autonomous work environment, where employees can choose the work style that suits them best.\n2. AI and Automation: AI and automation are increasingly being used to automate repetitive tasks, freeing up employees to focus on more creative and strategic work. AI-powered tools like chatbots, virtual assistants, and machine learning algorithms are being used to streamline processes, improve efficiency, and enhance decision-making.\n3. Virtual Collaboration: With remote work becoming the norm, virtual collaboration tools are becoming more important than ever. Virtual collaboration platforms like Zoom, Slack, and Microsoft Teams are being used to facilitate communication, collaboration, and teamwork among remote employees.\n4. Skills Shift: As AI and automation take over routine tasks, there is a growing need for skills that are more human-centric, such as creativity, empathy, and problem-solving. Employees will need to develop these skills to remain relevant in the modern workplace.\n5. Lifelong Learning: With the pace of technological change accelerating, lifelong learning is becoming more important than ever. Employees will need to continuously update their skills and knowledge to remain relevant in the modern workplace.\n6. Flexible Work Arrangements: With the rise of remote work, flexible work arrangements are becoming more common. Employers are offering flexible work schedules, compressed workweeks, and other arrangements to accommodate the needs of their employees.\n7. Well-being and Mental Health: With the pressure to perform at work increasing, there is a growing need for employers to prioritize the well-being and mental health of their employees. Employers are offering wellness programs, mental health resources, and other support services to help employees manage stress and maintain their overall well-being.\n8. Diversity, Equity, and Inclusion: As the modern workplace becomes more diverse, there is a growing need for employers to prioritize diversity, equity, and inclusion. Employers are implementing diversity and inclusion initiatives, such as unconscious bias training and diversity and inclusion committees, to create a more inclusive work environment.\n9. Virtual Reality and Augmented Reality: Virtual reality (VR) and augmented reality (AR) are being used to enhance training, collaboration, and communication in the modern workplace. VR and AR can simulate real-world environments, allowing employees to practice and learn new skills in a safe and controlled environment.\n10. Blockchain and Trust: Blockchain technology is being used to enhance trust and transparency in the modern workplace. Blockchain can be used to verify credentials, track supply chains, and ensure the authenticity of data.\nIn conclusion, the future of work is being shaped by AI, automation, remote work, and other emerging technologies. As these technologies continue to evolve, it is important for employers and employees to adapt and embrace the changes that are happening in the modern workplace. By doing so, we can create a more flexible, inclusive, and productive work environment that benefits everyone. Home > News > Industry News > The Importance of Properly Sizing Your Solar Panel System\nThe Importance of Properly Sizing Your Solar Panel System\nSizing a solar panel system is a critical step in the installation process. Proper sizing ensures that the system meets your energy needs and provides the best possible return on investment. Here are some reasons why properly sizing your solar panel system is important:\n1. Energy Production: A solar panel system that is too small will not produce enough energy to meet your needs, while a system that is too large will waste money and resources. Proper sizing ensures that your system produces" + } } } diff --git a/tests/baselines/llama_2_70b_hf.json b/tests/baselines/llama_2_70b_hf.json index 11a5f52eec..c8d356f213 100644 --- a/tests/baselines/llama_2_70b_hf.json +++ b/tests/baselines/llama_2_70b_hf.json @@ -1,11 +1,13 @@ { - "prompt": { - "model_name_or_path": "/root/local_dataset/pytorch/huggingface/hub/models--meta-llama--Llama-2-70b-hf/snapshots/bc7d6a85f909e2af7678537df0c771ae7b0e8010", - "max_new_tokens": 1024, - "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", - "distribution": { - "deepspeed": { - "throughput": 59.06 + "gaudi2": { + "prompt": { + "model_name_or_path": "/root/local_dataset/pytorch/huggingface/hub/models--meta-llama--Llama-2-70b-hf/snapshots/bc7d6a85f909e2af7678537df0c771ae7b0e8010", + "max_new_tokens": 1024, + "prompt": "While the proposed deep learning framework for CSI feedback based on superimposed coding offers a promising solution to the challenges of massive MIMO and mmWave systems, there are several research areas that can be further explored or improved. In this section, we discuss potential future research directions and enhancements to the current work. Adaptive Compression and Prediction Techniques: One potential improvement to the current deep learning framework is the development of adaptive compression and prediction techniques that can dynamically adjust the level of CSI compression and prediction based on the current channel conditions and system requirements. By incorporating adaptivity into the model, the framework could further optimize the trade-off between feedback overhead, latency, and CSI accuracy. Future research could investigate reinforcement learning or other online learning algorithms to enable such adaptive behavior in the deep learning model. Multi-antenna and Multi-user Superimposed Coding: The current work focuses on single-antenna systems and does not fully explore the potential of superimposed coding in multi-antenna and multi-user scenarios. Future research could extend the proposed framework to handle multiple antennas and users, investigating the impact of superimposed coding on system performance and the interactions between users. This would require the development of novel superimposed coding techniques that consider the spatial dimensions and user interference in multi-antenna and multi-user systems. Robustness to Channel Model Mismatch: The proposed deep learning framework assumes a specific channel model during the training phase. However, in practical wireless communication systems, the actual channel model may deviate from the assumed model. Future research could investigate the robustness of the proposed framework to channel model mismatch and develop techniques to improve its adaptability to different channel models. One potential approach is to incorporate unsupervised or semi-supervised learning methods into the framework, allowing the model to learn from partially labeled or unlabeled data obtained from the actual channel. Integration of Advanced Deep Learning Techniques: The current work employs a conventional encoder-decoder deep learning architecture for CSI feedback. Future research could explore the integration of advanced deep learning techniques, such as attention mechanisms, recurrent neural networks, and graph neural networks, to further improve the performance of the framework. These advanced techniques could enhance the models ability to capture complex channel patterns, temporal dependencies, and spatial correlations, resulting in more accurate CSI feedback and reduced overhead. Joint Optimization of Communication and Sensing: Massive MIMO and mmWave systems can potentially serve dual roles as communication and sensing systems. Future research could investigate the joint optimization of communication and sensing tasks in the context of deep learning-based CSI feedback and superimposed coding. This would involve developing novel architectures and algorithms that can efficiently balance the resource allocation and performance trade-offs between communication and sensing tasks while leveraging the benefits of superimposed coding. Hardware-aware Deep Learning Models: In practical implementations, the performance of deep learning models is affected by the hardware constraints of the devices, such as processing power, memory, and energy consumption. Future research could focus on the development of hardware-aware deep learning models for CSI feedback that consider these constraints during the design and training phases. This would require the investigation of model compression techniques, energy-efficient neural architectures, and quantization methods to enable efficient and low-complexity CSI feedback in resource-constrained devices. Cross-layer Optimization and Co-design: The proposed deep learning framework for CSI feedback primarily focuses on the physical layer of the wireless communication system. Future research could explore cross-layer optimization and co-design, integrating the proposed framework with upper-layer protocols and functionalities, such as Medium Access Control, routing, and Quality of Service. This would involve developing novel algorithms and models that can jointly optimize the performance of the entire communication system, leveraging the benefits of deep learning-based CSI feedback and superimposed coding across multiple layers of the network stack. Federated Learning and Distributed CSI Feedback: In large-scale wireless networks, such as the Internet of Things and 5G, 6G networks, centralized CSI feedback may not be feasible or efficient due to the high overhead and latency requirements. Future research could investigate the application of federated learning and distributed CSI feedback techniques to the proposed deep learning framework. This would involve developing novel algorithms and architectures that allow multiple devices to collaboratively learn and share CSI feedback, reducing the overall overhead and improving the scalability of the system. Security and Privacy Considerations: As deep learning models are integrated into wireless communication systems, security and privacy concerns become increasingly important. Future research could explore potential vulnerabilities in the proposed deep learning framework for CSI feedback, such as adversarial attacks, and develop countermeasures to ensure the integrity and confidentiality of the CSI. This would require the investigation of techniques such as adversarial training, secure multi-party computation, and differential privacy to protect the deep learning models and the underlying communication system. Experimental Validation and Testbed Development: While the current work evaluates the performance of the proposed deep learning framework through simulations and real-world experiments, future research could focus on developing a comprehensive testbed to validate the framework under various realistic scenarios and conditions. This would involve the design and implementation of hardware and software components that can accurately emulate the characteristics of massive MIMO and mmWave systems, as well as the integration of the deep learning framework with existing wireless communication platforms. In conclusion, the deep learning framework for CSI feedback based on superimposed coding presents a promising solution to the challenges of massive MIMO and mmWave systems. However, several research areas can be further explored or improved to enhance the performance, robustness, and applicability of the framework. By investigating these future research directions, the proposed framework can contribute to the development of more efficient, scalable, and secure wireless communication systems that can meet the ever-growing demands of modern applications and services. Integration with Edge and Cloud Computing: With the advent of edge computing and the continued growth of cloud computing, the processing capabilities of wireless communication systems are expanding. Future research could explore the integration of the proposed deep learning framework for CSI feedback with edge and cloud computing paradigms. This would involve", + "distribution": { + "deepspeed": { + "throughput": 59.06 + } } } } diff --git a/tests/test_text_generation_example.py b/tests/test_text_generation_example.py index 7b2a7e2972..f131a30d89 100755 --- a/tests/test_text_generation_example.py +++ b/tests/test_text_generation_example.py @@ -1,3 +1,4 @@ +import os import json import math import re @@ -67,8 +68,9 @@ def _test_text_generation( if prompt: path_to_baseline = BASELINE_DIRECTORY / Path(model_name.split("/")[-1].replace("-", "_")).with_suffix(".json") + device = "gaudi2" if os.environ.get("GAUDI2_CI", "0") == "1" else "gaudi" with path_to_baseline.open("r") as json_file: - baseline = json.load(json_file)["prompt"] + baseline = json.load(json_file)[device]["prompt"] distribution = "single_card" if not deepspeed else "deepspeed"