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internlm-chat-20b-4bit

Text Generation
Transformers
PyTorch
internlm
feature-extraction
custom_code
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xet
Community
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86edf4c
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1 Parent(s): 5bb97d2

Fix error and update benchmark in README

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  1. README.md +25 -9
README.md CHANGED
@@ -12,7 +12,7 @@ pipeline_tag: text-generation
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  Before proceeding with the inference of `internlm-chat-20b-4bit`, please ensure that lmdeploy is installed.
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  ```shell
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- pip install 'lmdeploy>=0.0.9'
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  ```
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  ## Inference
@@ -31,7 +31,7 @@ As demonstrated in the command below, first convert the model's layout using `tu
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  # Convert the model's layout and store it in the default path, ./workspace.
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  python3 -m lmdeploy.serve.turbomind.deploy \
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  --model-name internlm-chat-20b \
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- --model-path ./internlm-chat-20b \
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  --model-format awq \
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  --group-size 128
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@@ -44,7 +44,7 @@ python3 -m lmdeploy.turbomind.chat ./workspace
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  If you wish to interact with the model via web UI, please initiate the gradio server as indicated below:
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  ```shell
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- python3 -m lmdeploy.serve.turbomind ./workspace --server_name {ip_addr} --server_port {port}
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  ```
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  Subsequently, you can open the website `http://{ip_addr}:{port}` in your browser and interact with the model.
@@ -65,12 +65,10 @@ We conducted benchmarks on `internlm-chat-20b-4bit`. And `token_throughput` was
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  **Note**: The `session_len` in `workspace/triton_models/weights/config.ini` is changed to `2056` in our test.
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- | batch | tensor parallel | prompt_tokens | completion_tokens | thr_per_proc(token/s) | thr_per_node(token/s) | rpm (req/min) | mem_per_proc(GB) | mem_per_gpu(GB) | mem_per_node(GB) |
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- |-------|-----------------|---------------|-------------------|-----------------------|-----------------------|---------------|------------------|-----------------|------------------|
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- | 1 | 1 | 256 | 512 | 79.12 | 632.98 | - | 15.67 | 15.67 | 125.35 |
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- | 16 | 1 | 256 | 512 | 708.76 | 5670.1 | 220.23 | 51.48 | 51.48 | 411.85 |
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-
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-
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  ### token throughput
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@@ -84,6 +82,24 @@ python benchmark/profile_generation.py \
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  ```
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  You will find the `token_throughput` metrics in `./token_throughput.csv`
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  ### request throughput
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  Before proceeding with the inference of `internlm-chat-20b-4bit`, please ensure that lmdeploy is installed.
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  ```shell
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+ pip install 'lmdeploy>=0.0.11'
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  ```
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  ## Inference
 
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  # Convert the model's layout and store it in the default path, ./workspace.
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  python3 -m lmdeploy.serve.turbomind.deploy \
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  --model-name internlm-chat-20b \
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+ --model-path ./internlm-chat-20b-4bit \
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  --model-format awq \
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  --group-size 128
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  If you wish to interact with the model via web UI, please initiate the gradio server as indicated below:
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  ```shell
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+ python3 -m lmdeploy.serve.gradio.app ./workspace --server_name {ip_addr} --server_port {port}
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  ```
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  Subsequently, you can open the website `http://{ip_addr}:{port}` in your browser and interact with the model.
 
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  **Note**: The `session_len` in `workspace/triton_models/weights/config.ini` is changed to `2056` in our test.
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+ | batch | tensor parallel | prompt_tokens | completion_tokens | thr_per_proc(token/s) | rpm (req/min) | mem_per_proc(GB) |
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+ |-------|-----------------|---------------|-------------------|-----------------------|---------------|------------------|
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+ | 1 | 1 | 256 | 512 | 88.77 | - | 15.65 |
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+ | 16 | 1 | 256 | 512 | 792.7 | 220.23 | 51.46 |
 
 
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  ### token throughput
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  ```
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  You will find the `token_throughput` metrics in `./token_throughput.csv`
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+ | batch | prompt_tokens | completion_tokens | thr_per_proc(token/s) | thr_per_node(token/s) | rpm(req/min) | mem_per_proc(GB) | mem_per_gpu(GB) | mem_per_node(GB) |
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+ |-------|---------------|-------------------|-----------------------|-----------------------|--------------|------------------|-----------------|------------------|
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+ | 1 | 256 | 512 | 88.77 | 710.12 | - | 15.65 | 15.65 | 125.21 |
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+ | 1 | 512 | 512 | 83.89 | 671.15 | - | 15.68 | 15.68 | 125.46 |
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+ | 1 | 512 | 1024 | 80.19 | 641.5 | - | 15.68 | 15.68 | 125.46 |
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+ | 1 | 1024 | 1024 | 72.34 | 578.74 | - | 15.75 | 15.75 | 125.96 |
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+ | 1 | 1 | 2048 | 80.69 | 645.55 | - | 15.62 | 15.62 | 124.96 |
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+ | 8 | 256 | 512 | 565.21 | 4521.67 | - | 32.37 | 32.37 | 258.96 |
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+ | 8 | 512 | 512 | 489.04 | 3912.33 | - | 32.62 | 32.62 | 260.96 |
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+ | 8 | 512 | 1024 | 467.23 | 3737.84 | - | 32.62 | 32.62 | 260.96 |
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+ | 8 | 1024 | 1024 | 383.4 | 3067.19 | - | 33.06 | 33.06 | 264.46 |
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+ | 8 | 1 | 2048 | 487.74 | 3901.93 | - | 32.12 | 32.12 | 256.96 |
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+ | 16 | 256 | 512 | 792.7 | 6341.6 | - | 51.46 | 51.46 | 411.71 |
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+ | 16 | 512 | 512 | 639.4 | 5115.17 | - | 51.93 | 51.93 | 415.46 |
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+ | 16 | 512 | 1024 | 591.39 | 4731.09 | - | 51.93 | 51.93 | 415.46 |
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+ | 16 | 1024 | 1024 | 449.11 | 3592.85 | - | 52.06 | 52.06 | 416.46 |
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+ | 16 | 1 | 2048 | 620.5 | 4964.02 | - | 51 | 51 | 407.96 |
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+
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  ### request throughput
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