Add AWQ quantization inference support (#1019) (#1054)

# Add AWQ quantization inference support

Fixes
https://github.com/huggingface/text-generation-inference/issues/781

This PR (partially) adds support for AWQ quantization for inference.
More information on AWQ [here](https://arxiv.org/abs/2306.00978). In
general, AWQ is faster and more accurate than GPTQ, which is currently
supported by TGI.

This PR installs 4-bit GEMM custom CUDA kernels released by AWQ authors
(in `requirements.txt`, just one line change).

Quick way to test this PR would be bring up TGI as follows:

```
text-generation-server download-weights abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq

text-generation-launcher \
--huggingface-hub-cache ~/.cache/huggingface/hub/ \
--model-id abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq \
--trust-remote-code --port 8080 \
--max-input-length 2048 --max-total-tokens 4096 --max-batch-prefill-tokens 4096 \
--quantize awq
```

Please note:
* This PR was tested with FlashAttention v2 and vLLM.
* This PR adds support for AWQ inference, not quantizing the models.
That needs to be done outside of TGI, instructions

[here](f084f40bd9).
* This PR only adds support for `FlashLlama` models for now.
* Multi-GPU setup has not been tested. 
* No integration tests have been added so far, will add later if
maintainers are interested in this change.
* This PR can be tested on any of the models released

[here](https://huggingface.co/abhinavkulkarni?sort_models=downloads#models).

Please refer to the linked issue for benchmarks for

[abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq](https://huggingface.co/abhinavkulkarni/meta-llama-Llama-2-7b-chat-hf-w4-g128-awq)
vs

[TheBloke/Llama-2-7b-Chat-GPTQ](https://huggingface.co/TheBloke/Llama-2-7b-Chat-GPTQ).

Please note, AWQ has released faster (and in case of Llama, fused)
kernels for 4-bit GEMM, currently at the top of the `main` branch at
https://github.com/mit-han-lab/llm-awq, but this PR uses an older commit
that has been tested to work. We can switch to latest commit later on.

## Who can review?

@OlivierDehaene OR @Narsil

---------



# What does this PR do?

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Fixes # (issue)


## Before submitting
- [ ] This PR fixes a typo or improves the docs (you can dismiss the
other checks if that's the case).
- [ ] Did you read the [contributor
guideline](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md#start-contributing-pull-requests),
      Pull Request section?
- [ ] Was this discussed/approved via a Github issue or the
[forum](https://discuss.huggingface.co/)? Please add a link
      to it if that's the case.
- [ ] Did you make sure to update the documentation with your changes?
Here are the
[documentation
guidelines](https://github.com/huggingface/transformers/tree/main/docs),
and
[here are tips on formatting
docstrings](https://github.com/huggingface/transformers/tree/main/docs#writing-source-documentation).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have
passed. Feel free to tag
members/contributors who may be interested in your PR.

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---------

Co-authored-by: Abhinav M Kulkarni <abhinavkulkarni@gmail.com>
Co-authored-by: Abhinav Kulkarni <abhinav@concentric.ai>

Dockerfile

@@ -111,22 +111,22 @@ RUN make build-flash-attention-v2
 
 # Build Transformers exllama kernels
 FROM kernel-builder as exllama-kernels-builder
-
 WORKDIR /usr/src
-
 COPY server/exllama_kernels/ .
-
-
 # Build specific version of transformers
 RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" python setup.py build
 
+# Build Transformers awq kernels
+FROM kernel-builder as awq-kernels-builder
+WORKDIR /usr/src
+COPY server/Makefile-awq Makefile
+# Build specific version of transformers
+RUN TORCH_CUDA_ARCH_LIST="8.0;8.6+PTX" make build-awq
+
 # Build Transformers CUDA kernels
 FROM kernel-builder as custom-kernels-builder
-
 WORKDIR /usr/src
-
 COPY server/custom_kernels/ .
-
 # Build specific version of transformers
 RUN python setup.py build
 
@@ -175,6 +175,8 @@ COPY --from=flash-att-v2-builder /usr/src/flash-attention-v2/build/lib.linux-x86
 COPY --from=custom-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
 # Copy build artifacts from exllama kernels builder
 COPY --from=exllama-kernels-builder /usr/src/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
+# Copy build artifacts from awq kernels builder
+COPY --from=awq-kernels-builder /usr/src/llm-awq/awq/kernels/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages
 
 # Copy builds artifacts from vllm builder
 COPY --from=vllm-builder /usr/src/vllm/build/lib.linux-x86_64-cpython-39 /opt/conda/lib/python3.9/site-packages

docs/source/basic_tutorials/preparing_model.md

@@ -4,7 +4,7 @@ Text Generation Inference improves the model in several aspects.
 
 ## Quantization
 
-TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes) and [GPT-Q](https://arxiv.org/abs/2210.17323) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes` or `gptq` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq). To get more information about quantization, please refer to (./conceptual/quantization.md)
+TGI supports [bits-and-bytes](https://github.com/TimDettmers/bitsandbytes#bitsandbytes), [GPT-Q](https://arxiv.org/abs/2210.17323) and [AWQ](https://arxiv.org/abs/2306.00978) quantization. To speed up inference with quantization, simply set `quantize` flag to `bitsandbytes`, `gptq` or `awq` depending on the quantization technique you wish to use. When using GPT-Q quantization, you need to point to one of the models [here](https://huggingface.co/models?search=gptq) when using AWQ quantization, you need to point to one of the models [here](https://huggingface.co/models?search=awq). To get more information about quantization, please refer to (./conceptual/quantization.md)
 
 
 ## RoPE Scaling

integration-tests/models/__snapshots__/test_flash_awq/test_flash_llama_awq.json

@@ -0,0 +1,104 @@
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+        "text": "Learning"
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+        "logprob": -0.7548828,
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+}

integration-tests/models/__snapshots__/test_flash_awq/test_flash_llama_awq_all_params.json

@@ -0,0 +1,99 @@
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+        "logprob": -10.90625,
+        "text": "Deep"
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+        "logprob": -2.65625,
+        "text": "Learning"
+      },
+      {
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+        "logprob": -4.8085938,
+        "text": "?"
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+    "tokens": [
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+        "text": " I"
+      }
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+  },
+  "generated_text": "What is Deep Learning?\nThis question has been asked many times and I"
+}

integration-tests/models/__snapshots__/test_flash_awq/test_flash_llama_awq_load.json

@@ -0,0 +1,418 @@
+[
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+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
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+          "text": "Learning"
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+          "logprob": -0.7548828,
+          "text": "?"
+        }
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+      "seed": null,
+      "tokens": [
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+          "logprob": -0.62646484,
+          "special": false,
+          "text": " Machine"
+        }
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+    },
+    "generated_text": "\nWhat is the difference between Deep Learning and Machine"
+  },
+  {
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+          "text": "Learning"
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+          "text": " Machine"
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+    },
+    "generated_text": "\nWhat is the difference between Deep Learning and Machine"
+  },
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+          "text": "<s>"
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+          "logprob": -7.703125,
+          "text": "What"
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+        {
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+          "logprob": -1.4765625,
+          "text": "is"
+        },
+        {
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+          "logprob": -9.390625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.8652344,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.7548828,
+          "text": "?"
+        }
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+      "seed": null,
+      "tokens": [
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+          "special": false,
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+          "logprob": -2.4550781,
+          "special": false,
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+          "special": false,
+          "text": " the"
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+          "special": false,
+          "text": " difference"
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+          "logprob": -0.026504517,
+          "special": false,
+          "text": " between"
+        },
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+          "id": 21784,
+          "logprob": -1.4287109,
+          "special": false,
+          "text": " Deep"
+        },
+        {
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+          "logprob": -0.15856934,
+          "special": false,
+          "text": " Learning"
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+          "logprob": -0.17456055,
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+        {
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+          "logprob": -0.62646484,
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+          "text": " Machine"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\nWhat is the difference between Deep Learning and Machine"
+  }
+]

integration-tests/models/__snapshots__/test_flash_awq_sharded/test_flash_llama_awq_load_sharded.json

@@ -0,0 +1,418 @@
+[
+  {
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+      "generated_tokens": 10,
+      "prefill": [
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+          "text": "<s>"
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+        {
+          "id": 1724,
+          "logprob": -7.6914062,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -1.4746094,
+          "text": "is"
+        },
+        {
+          "id": 21784,
+          "logprob": -9.390625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.8623047,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.7558594,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.9228516,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 5618,
+          "logprob": -2.4609375,
+          "special": false,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -0.57177734,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 278,
+          "logprob": -1.5722656,
+          "special": false,
+          "text": " the"
+        },
+        {
+          "id": 4328,
+          "logprob": -1.5859375,
+          "special": false,
+          "text": " difference"
+        },
+        {
+          "id": 1546,
+          "logprob": -0.02633667,
+          "special": false,
+          "text": " between"
+        },
+        {
+          "id": 21784,
+          "logprob": -1.4335938,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -0.15991211,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 322,
+          "logprob": -0.17456055,
+          "special": false,
+          "text": " and"
+        },
+        {
+          "id": 6189,
+          "logprob": -0.62060547,
+          "special": false,
+          "text": " Machine"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\nWhat is the difference between Deep Learning and Machine"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 1724,
+          "logprob": -7.6914062,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -1.4746094,
+          "text": "is"
+        },
+        {
+          "id": 21784,
+          "logprob": -9.390625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.8623047,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.7558594,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.9228516,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 5618,
+          "logprob": -2.4609375,
+          "special": false,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -0.57177734,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 278,
+          "logprob": -1.5722656,
+          "special": false,
+          "text": " the"
+        },
+        {
+          "id": 4328,
+          "logprob": -1.5859375,
+          "special": false,
+          "text": " difference"
+        },
+        {
+          "id": 1546,
+          "logprob": -0.02633667,
+          "special": false,
+          "text": " between"
+        },
+        {
+          "id": 21784,
+          "logprob": -1.4335938,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -0.15991211,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 322,
+          "logprob": -0.17456055,
+          "special": false,
+          "text": " and"
+        },
+        {
+          "id": 6189,
+          "logprob": -0.62060547,
+          "special": false,
+          "text": " Machine"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\nWhat is the difference between Deep Learning and Machine"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 1724,
+          "logprob": -7.6914062,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -1.4746094,
+          "text": "is"
+        },
+        {
+          "id": 21784,
+          "logprob": -9.390625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.8623047,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.7558594,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.9228516,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 5618,
+          "logprob": -2.4609375,
+          "special": false,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -0.57177734,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 278,
+          "logprob": -1.5722656,
+          "special": false,
+          "text": " the"
+        },
+        {
+          "id": 4328,
+          "logprob": -1.5859375,
+          "special": false,
+          "text": " difference"
+        },
+        {
+          "id": 1546,
+          "logprob": -0.02633667,
+          "special": false,
+          "text": " between"
+        },
+        {
+          "id": 21784,
+          "logprob": -1.4335938,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -0.15991211,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 322,
+          "logprob": -0.17456055,
+          "special": false,
+          "text": " and"
+        },
+        {
+          "id": 6189,
+          "logprob": -0.62060547,
+          "special": false,
+          "text": " Machine"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\nWhat is the difference between Deep Learning and Machine"
+  },
+  {
+    "details": {
+      "best_of_sequences": null,
+      "finish_reason": "length",
+      "generated_tokens": 10,
+      "prefill": [
+        {
+          "id": 1,
+          "logprob": null,
+          "text": "<s>"
+        },
+        {
+          "id": 1724,
+          "logprob": -7.6914062,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -1.4746094,
+          "text": "is"
+        },
+        {
+          "id": 21784,
+          "logprob": -9.390625,
+          "text": "Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -1.8623047,
+          "text": "Learning"
+        },
+        {
+          "id": 29973,
+          "logprob": -0.7558594,
+          "text": "?"
+        }
+      ],
+      "seed": null,
+      "tokens": [
+        {
+          "id": 13,
+          "logprob": -1.9228516,
+          "special": false,
+          "text": "\n"
+        },
+        {
+          "id": 5618,
+          "logprob": -2.4609375,
+          "special": false,
+          "text": "What"
+        },
+        {
+          "id": 338,
+          "logprob": -0.57177734,
+          "special": false,
+          "text": " is"
+        },
+        {
+          "id": 278,
+          "logprob": -1.5722656,
+          "special": false,
+          "text": " the"
+        },
+        {
+          "id": 4328,
+          "logprob": -1.5859375,
+          "special": false,
+          "text": " difference"
+        },
+        {
+          "id": 1546,
+          "logprob": -0.02633667,
+          "special": false,
+          "text": " between"
+        },
+        {
+          "id": 21784,
+          "logprob": -1.4335938,
+          "special": false,
+          "text": " Deep"
+        },
+        {
+          "id": 29257,
+          "logprob": -0.15991211,
+          "special": false,
+          "text": " Learning"
+        },
+        {
+          "id": 322,
+          "logprob": -0.17456055,
+          "special": false,
+          "text": " and"
+        },
+        {
+          "id": 6189,
+          "logprob": -0.62060547,
+          "special": false,
+          "text": " Machine"
+        }
+      ],
+      "top_tokens": null
+    },
+    "generated_text": "\nWhat is the difference between Deep Learning and Machine"
+  }
+]

integration-tests/models/__snapshots__/test_flash_awq_sharded/test_flash_llama_awq_sharded.json

@@ -0,0 +1,104 @@
+{
+  "details": {
+    "best_of_sequences": null,
+    "finish_reason": "length",
+    "generated_tokens": 10,
+    "prefill": [
+      {
+        "id": 1,
+        "logprob": null,
+        "text": "<s>"
+      },
+      {
+        "id": 1724,
+        "logprob": -7.6914062,
+        "text": "What"
+      },
+      {
+        "id": 338,
+        "logprob": -1.4746094,
+        "text": "is"
+      },
+      {
+        "id": 21784,
+        "logprob": -9.390625,
+        "text": "Deep"
+      },
+      {
+        "id": 29257,
+        "logprob": -1.8623047,
+        "text": "Learning"
+      },
+      {
+        "id": 29973,
+        "logprob": -0.7558594,
+        "text": "?"
+      }
+    ],
+    "seed": null,
+    "tokens": [
+      {
+        "id": 13,
+        "logprob": -1.9228516,
+        "special": false,
+        "text": "\n"
+      },
+      {
+        "id": 5618,
+        "logprob": -2.4609375,
+        "special": false,
+        "text": "What"
+      },
+      {
+        "id": 338,
+        "logprob": -0.57177734,
+        "special": false,
+        "text": " is"
+      },
+      {
+        "id": 278,
+        "logprob": -1.5722656,
+        "special": false,
+        "text": " the"
+      },
+      {
+        "id": 4328,
+        "logprob": -1.5927734,
+        "special": false,
+        "text": " difference"
+      },
+      {
+        "id": 1546,
+        "logprob": -0.026428223,
+        "special": false,
+        "text": " between"
+      },
+      {
+        "id": 21784,
+        "logprob": -1.4267578,
+        "special": false,
+        "text": " Deep"
+      },
+      {
+        "id": 29257,
+        "logprob": -0.16015625,
+        "special": false,
+        "text": " Learning"
+      },
+      {
+        "id": 322,
+        "logprob": -0.17382812,
+        "special": false,
+        "text": " and"
+      },
+      {
+        "id": 6189,
+        "logprob": -0.62060547,
+        "special": false,
+        "text": " Machine"
+      }
+    ],
+    "top_tokens": null
+  },
+  "generated_text": "\nWhat is the difference between Deep Learning and Machine"
+}

integration-tests/models/test_flash_awq.py

@@ -0,0 +1,64 @@
+import pytest
+
+
+@pytest.fixture(scope="module")
+def flash_llama_awq_handle(launcher):
+    with launcher("abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq", num_shard=1, quantize="awq") as handle:
+        yield handle
+
+
+@pytest.fixture(scope="module")
+async def flash_llama_awq(flash_llama_awq_handle):
+    await flash_llama_awq_handle.health(300)
+    return flash_llama_awq_handle.client
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_awq(flash_llama_awq, response_snapshot):
+    response = await flash_llama_awq.generate(
+        "What is Deep Learning?", max_new_tokens=10, decoder_input_details=True
+    )
+
+    assert response.details.generated_tokens == 10
+    assert response.generated_text == "\nWhat is the difference between Deep Learning and Machine"
+    assert response == response_snapshot
+
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_awq_all_params(flash_llama_awq, response_snapshot):
+    response = await flash_llama_awq.generate(
+        "What is Deep Learning?",
+        max_new_tokens=10,
+        repetition_penalty=1.2,
+        return_full_text=True,
+        temperature=0.5,
+        top_p=0.9,
+        top_k=10,
+        truncate=5,
+        typical_p=0.9,
+        watermark=True,
+        decoder_input_details=True,
+        seed=0,
+    )
+
+    assert response.details.generated_tokens == 10
+    assert response == response_snapshot
+
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_awq_load(
+    flash_llama_awq, generate_load, response_snapshot
+):
+    responses = await generate_load(
+        flash_llama_awq, "What is Deep Learning?", max_new_tokens=10, n=4
+    )
+
+    assert len(responses) == 4
+    assert all([r.generated_text ==  "\nWhat is the difference between Deep Learning and Machine" for r in responses])
+
+    assert responses == response_snapshot
+
+

integration-tests/models/test_flash_awq_sharded.py

@@ -0,0 +1,36 @@
+import pytest
+
+@pytest.fixture(scope="module")
+def flash_llama_awq_handle_sharded(launcher):
+    with launcher("abhinavkulkarni/codellama-CodeLlama-7b-Python-hf-w4-g128-awq", num_shard=2, quantize="awq") as handle:
+        yield handle
+
+@pytest.fixture(scope="module")
+async def flash_llama_awq_sharded(flash_llama_awq_handle_sharded):
+    await flash_llama_awq_handle_sharded.health(300)
+    return flash_llama_awq_handle_sharded.client
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_awq_sharded(flash_llama_awq_sharded, response_snapshot):
+    response = await flash_llama_awq_sharded.generate(
+        "What is Deep Learning?", max_new_tokens=10, decoder_input_details=True
+    )
+
+    assert response.details.generated_tokens == 10
+    assert response.generated_text == "\nWhat is the difference between Deep Learning and Machine"
+    assert response == response_snapshot
+
+@pytest.mark.asyncio
+@pytest.mark.private
+async def test_flash_llama_awq_load_sharded(
+    flash_llama_awq_sharded, generate_load, response_snapshot
+):
+    responses = await generate_load(
+        flash_llama_awq_sharded, "What is Deep Learning?", max_new_tokens=10, n=4
+    )
+
+    assert len(responses) == 4
+    assert all([r.generated_text ==  "\nWhat is the difference between Deep Learning and Machine" for r in responses])
+
+    assert responses == response_snapshot

launcher/src/main.rs

@@ -25,6 +25,7 @@ enum Quantization {
     BitsandbytesNF4,
     BitsandbytesFP4,
     Gptq,
+    Awq,
 }
 
 impl std::fmt::Display for Quantization {
@@ -43,6 +44,9 @@ impl std::fmt::Display for Quantization {
             Quantization::Gptq => {
                 write!(f, "gptq")
             }
+            Quantization::Awq => {
+                write!(f, "awq")
+            }
         }
     }
 }

server/.gitignore

@@ -159,3 +159,4 @@ safetensors
 flash-attention/
 flash-attention-v2/
 vllm/
+llm-awq/

server/Makefile

@@ -1,6 +1,7 @@
 include Makefile-flash-att
 include Makefile-flash-att-v2
 include Makefile-vllm
+include Makefile-awq
 
 unit-tests:
 	pytest -s -vv -m "not private" tests

server/Makefile-awq

@@ -0,0 +1,13 @@
+awq_commit := f084f40bd996f3cf3a0633c1ad7d9d476c318aaa
+
+awq: 
+	rm -rf llm-awq
+	git clone https://github.com/mit-han-lab/llm-awq
+
+build-awq: awq
+	cd llm-awq/ && git fetch && git checkout $(awq_commit)
+	cd llm-awq/awq/kernels && python setup.py build
+
+install-awq: build-awq
+	pip uninstall awq_inference_engine -y || true
+	cd llm-awq/awq/kernels && python setup.py install

server/poetry.lock

@@ -323,19 +323,19 @@ files = [
 
 [[package]]
 name = "datasets"
-version = "2.14.4"
+version = "2.14.5"
 description = "HuggingFace community-driven open-source library of datasets"
 optional = true
 python-versions = ">=3.8.0"
 files = [
-    {file = "datasets-2.14.4-py3-none-any.whl", hash = "sha256:29336bd316a7d827ccd4da2236596279b20ca2ac78f64c04c9483da7cbc2459b"},
+    {file = "datasets-2.14.5-py3-none-any.whl", hash = "sha256:dd4155091034cba04d5a28711f2ed3944275ed15c5d0c5a2d0b6b9ea34a2bdfe"},
-    {file = "datasets-2.14.4.tar.gz", hash = "sha256:ef29c2b5841de488cd343cfc26ab979bff77efa4d2285af51f1ad7db5c46a83b"},
+    {file = "datasets-2.14.5.tar.gz", hash = "sha256:b738a86540ab8e1a7806c8a3790b67be0056318d0c5d5a58a1b0dbdd76c0f568"},
 ]
 
 [package.dependencies]
 aiohttp = "*"
 dill = ">=0.3.0,<0.3.8"
-fsspec = {version = ">=2021.11.1", extras = ["http"]}
+fsspec = {version = ">=2023.1.0,<2023.9.0", extras = ["http"]}
 huggingface-hub = ">=0.14.0,<1.0.0"
 multiprocess = "*"
 numpy = ">=1.17"
@@ -421,21 +421,19 @@ test = ["pytest (>=6)"]
 
 [[package]]
 name = "filelock"
-version = "3.12.3"
+version = "3.12.4"
 description = "A platform independent file lock."
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "filelock-3.12.3-py3-none-any.whl", hash = "sha256:f067e40ccc40f2b48395a80fcbd4728262fab54e232e090a4063ab804179efeb"},
+    {file = "filelock-3.12.4-py3-none-any.whl", hash = "sha256:08c21d87ded6e2b9da6728c3dff51baf1dcecf973b768ef35bcbc3447edb9ad4"},
-    {file = "filelock-3.12.3.tar.gz", hash = "sha256:0ecc1dd2ec4672a10c8550a8182f1bd0c0a5088470ecd5a125e45f49472fac3d"},
+    {file = "filelock-3.12.4.tar.gz", hash = "sha256:2e6f249f1f3654291606e046b09f1fd5eac39b360664c27f5aad072012f8bcbd"},
 ]
 
-[package.dependencies]
-typing-extensions = {version = ">=4.7.1", markers = "python_version < \"3.11\""}
-
 [package.extras]
 docs = ["furo (>=2023.7.26)", "sphinx (>=7.1.2)", "sphinx-autodoc-typehints (>=1.24)"]
 testing = ["covdefaults (>=2.3)", "coverage (>=7.3)", "diff-cover (>=7.7)", "pytest (>=7.4)", "pytest-cov (>=4.1)", "pytest-mock (>=3.11.1)", "pytest-timeout (>=2.1)"]
+typing = ["typing-extensions (>=4.7.1)"]
 
 [[package]]
 name = "frozenlist"
@@ -582,148 +580,148 @@ testing = ["protobuf (>=4.21.9)"]
 
 [[package]]
 name = "grpcio"
-version = "1.57.0"
+version = "1.58.0"
 description = "HTTP/2-based RPC framework"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "grpcio-1.57.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:092fa155b945015754bdf988be47793c377b52b88d546e45c6a9f9579ac7f7b6"},
+    {file = "grpcio-1.58.0-cp310-cp310-linux_armv7l.whl", hash = "sha256:3e6bebf1dfdbeb22afd95650e4f019219fef3ab86d3fca8ebade52e4bc39389a"},
-    {file = "grpcio-1.57.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:2f7349786da979a94690cc5c2b804cab4e8774a3cf59be40d037c4342c906649"},
+    {file = "grpcio-1.58.0-cp310-cp310-macosx_12_0_universal2.whl", hash = "sha256:cde11577d5b6fd73a00e6bfa3cf5f428f3f33c2d2878982369b5372bbc4acc60"},
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 ]
 
 [package.dependencies]
-grpcio = ">=1.57.0"
+grpcio = ">=1.58.0"
 protobuf = ">=4.21.6,<5.0dev"
 setuptools = "*"
 
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+    {file = "protobuf-4.24.3-py3-none-any.whl", hash = "sha256:f6f8dc65625dadaad0c8545319c2e2f0424fede988368893ca3844261342c11a"},
-    {file = "protobuf-4.24.2.tar.gz", hash = "sha256:7fda70797ddec31ddfa3576cbdcc3ddbb6b3078b737a1a87ab9136af0570cd6e"},
+    {file = "protobuf-4.24.3.tar.gz", hash = "sha256:12e9ad2ec079b833176d2921be2cb24281fa591f0b119b208b788adc48c2561d"},
 ]
 
 [[package]]
@@ -1517,13 +1531,13 @@ numpy = ">=1.16.6"
 
 [[package]]
 name = "pytest"
-version = "7.4.0"
+version = "7.4.2"
 description = "pytest: simple powerful testing with Python"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "pytest-7.4.0-py3-none-any.whl", hash = "sha256:78bf16451a2eb8c7a2ea98e32dc119fd2aa758f1d5d66dbf0a59d69a3969df32"},
+    {file = "pytest-7.4.2-py3-none-any.whl", hash = "sha256:1d881c6124e08ff0a1bb75ba3ec0bfd8b5354a01c194ddd5a0a870a48d99b002"},
-    {file = "pytest-7.4.0.tar.gz", hash = "sha256:b4bf8c45bd59934ed84001ad51e11b4ee40d40a1229d2c79f9c592b0a3f6bd8a"},
+    {file = "pytest-7.4.2.tar.gz", hash = "sha256:a766259cfab564a2ad52cb1aae1b881a75c3eb7e34ca3779697c23ed47c47069"},
 ]
 
 [package.dependencies]
@@ -1553,13 +1567,13 @@ six = ">=1.5"
 
 [[package]]
 name = "pytz"
-version = "2023.3"
+version = "2023.3.post1"
 description = "World timezone definitions, modern and historical"
 optional = true
 python-versions = "*"
 files = [
-    {file = "pytz-2023.3-py2.py3-none-any.whl", hash = "sha256:a151b3abb88eda1d4e34a9814df37de2a80e301e68ba0fd856fb9b46bfbbbffb"},
+    {file = "pytz-2023.3.post1-py2.py3-none-any.whl", hash = "sha256:ce42d816b81b68506614c11e8937d3aa9e41007ceb50bfdcb0749b921bf646c7"},
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+    {file = "pytz-2023.3.post1.tar.gz", hash = "sha256:7b4fddbeb94a1eba4b557da24f19fdf9db575192544270a9101d8509f9f43d7b"},
 ]
 
 [[package]]
@@ -1916,19 +1930,19 @@ files = [
 
 [[package]]
 name = "setuptools"
-version = "68.1.2"
+version = "68.2.2"
 description = "Easily download, build, install, upgrade, and uninstall Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "setuptools-68.2.2-py3-none-any.whl", hash = "sha256:b454a35605876da60632df1a60f736524eb73cc47bbc9f3f1ef1b644de74fd2a"},
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+    {file = "setuptools-68.2.2.tar.gz", hash = "sha256:4ac1475276d2f1c48684874089fefcd83bd7162ddaafb81fac866ba0db282a87"},
 ]
 
 [package.extras]
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+docs = ["furo", "jaraco.packaging (>=9.3)", "jaraco.tidelift (>=1.4)", "pygments-github-lexers (==0.0.5)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-favicon", "sphinx-hoverxref (<2)", "sphinx-inline-tabs", "sphinx-lint", "sphinx-notfound-page (>=1,<2)", "sphinx-reredirects", "sphinxcontrib-towncrier"]
 testing = ["build[virtualenv]", "filelock (>=3.4.0)", "flake8-2020", "ini2toml[lite] (>=0.9)", "jaraco.develop (>=7.21)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pip (>=19.1)", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=2.2)", "pytest-mypy (>=0.9.1)", "pytest-perf", "pytest-ruff", "pytest-timeout", "pytest-xdist", "tomli-w (>=1.0.0)", "virtualenv (>=13.0.0)", "wheel"]
-testing-integration = ["build[virtualenv]", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
+testing-integration = ["build[virtualenv] (>=1.0.3)", "filelock (>=3.4.0)", "jaraco.envs (>=2.2)", "jaraco.path (>=3.2.0)", "packaging (>=23.1)", "pytest", "pytest-enabler", "pytest-xdist", "tomli", "virtualenv (>=13.0.0)", "wheel"]
 
 [[package]]
 name = "six"
@@ -2092,13 +2106,13 @@ telegram = ["requests"]
 
 [[package]]
 name = "transformers"
-version = "4.32.1"
+version = "4.33.2"
 description = "State-of-the-art Machine Learning for JAX, PyTorch and TensorFlow"
 optional = false
 python-versions = ">=3.8.0"
 files = [
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+    {file = "transformers-4.33.2-py3-none-any.whl", hash = "sha256:5a9a757bea5b5a1b94796805bcb5978b552208a3ac193f46edda66be6f4a5488"},
-    {file = "transformers-4.32.1.tar.gz", hash = "sha256:1edc8ae1de357d97c3d36b04412aa63d55e6fc0c4b39b419a7d380ed947d2252"},
+    {file = "transformers-4.33.2.tar.gz", hash = "sha256:47dd36f302afec86d9cdcacab61bbd0296e6bb02e64d2ed7855daaab14ee290e"},
 ]
 
 [package.dependencies]
@@ -2115,16 +2129,16 @@ tqdm = ">=4.27"
 
 [package.extras]
 accelerate = ["accelerate (>=0.20.3)"]
-agents = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.9,!=1.12.0)"]
+agents = ["Pillow (<10.0.0)", "accelerate (>=0.20.3)", "datasets (!=2.5.0)", "diffusers", "opencv-python", "sentencepiece (>=0.1.91,!=0.1.92)", "torch (>=1.10,!=1.12.0)"]
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 audio = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 codecarbon = ["codecarbon (==1.2.0)"]
 deepspeed = ["accelerate (>=0.20.3)", "deepspeed (>=0.9.3)"]
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 docs-specific = ["hf-doc-builder"]
 fairscale = ["fairscale (>0.3)"]
 flax = ["flax (>=0.4.1,<=0.7.0)", "jax (>=0.4.1,<=0.4.13)", "jaxlib (>=0.4.1,<=0.4.13)", "optax (>=0.0.8,<=0.1.4)"]
@@ -2147,15 +2161,15 @@ sigopt = ["sigopt"]
 sklearn = ["scikit-learn"]
 speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 testing = ["GitPython (<3.1.19)", "beautifulsoup4", "black (>=23.1,<24.0)", "cookiecutter (==1.7.3)", "datasets (!=2.5.0)", "dill (<0.3.5)", "evaluate (>=0.2.0)", "faiss-cpu", "hf-doc-builder (>=0.3.0)", "nltk", "parameterized", "protobuf", "psutil", "pytest (>=7.2.0)", "pytest-timeout", "pytest-xdist", "rjieba", "rouge-score (!=0.0.7,!=0.0.8,!=0.1,!=0.1.1)", "sacrebleu (>=1.4.12,<2.0.0)", "sacremoses", "timeout-decorator"]
-tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx"]
+tf = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
-tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.14)", "tensorflow-text (<2.14)", "tf2onnx"]
+tf-cpu = ["keras-nlp (>=0.3.1)", "onnxconverter-common", "tensorflow-cpu (>=2.6,<2.15)", "tensorflow-text (<2.15)", "tf2onnx"]
 tf-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)"]
 timm = ["timm"]
 tokenizers = ["tokenizers (>=0.11.1,!=0.11.3,<0.14)"]
-torch = ["accelerate (>=0.20.3)", "torch (>=1.9,!=1.12.0)"]
+torch = ["accelerate (>=0.20.3)", "torch (>=1.10,!=1.12.0)"]
 torch-speech = ["kenlm", "librosa", "phonemizer", "pyctcdecode (>=0.4.0)", "torchaudio"]
 torch-vision = ["Pillow (<10.0.0)", "torchvision"]
-torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.9,!=1.12.0)", "tqdm (>=4.27)"]
+torchhub = ["filelock", "huggingface-hub (>=0.15.1,<1.0)", "importlib-metadata", "numpy (>=1.17)", "packaging (>=20.0)", "protobuf", "regex (!=2019.12.17)", "requests", "sentencepiece (>=0.1.91,!=0.1.92)", "tokenizers (>=0.11.1,!=0.11.3,<0.14)", "torch (>=1.10,!=1.12.0)", "tqdm (>=4.27)"]
 video = ["av (==9.2.0)", "decord (==0.6.0)"]
 vision = ["Pillow (<10.0.0)"]
 
@@ -2181,13 +2195,13 @@ test = ["black (>=22.3.0,<23.0.0)", "coverage (>=5.2,<6.0)", "isort (>=5.0.6,<6.
 
 [[package]]
 name = "typing-extensions"
-version = "4.7.1"
+version = "4.8.0"
-description = "Backported and Experimental Type Hints for Python 3.7+"
+description = "Backported and Experimental Type Hints for Python 3.8+"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
-    {file = "typing_extensions-4.7.1-py3-none-any.whl", hash = "sha256:440d5dd3af93b060174bf433bccd69b0babc3b15b1a8dca43789fd7f61514b36"},
+    {file = "typing_extensions-4.8.0-py3-none-any.whl", hash = "sha256:8f92fc8806f9a6b641eaa5318da32b44d401efaac0f6678c9bc448ba3605faa0"},
-    {file = "typing_extensions-4.7.1.tar.gz", hash = "sha256:b75ddc264f0ba5615db7ba217daeb99701ad295353c45f9e95963337ceeeffb2"},
+    {file = "typing_extensions-4.8.0.tar.gz", hash = "sha256:df8e4339e9cb77357558cbdbceca33c303714cf861d1eef15e1070055ae8b7ef"},
 ]
 
 [[package]]
@@ -2203,13 +2217,13 @@ files = [
 
 [[package]]
 name = "urllib3"
-version = "2.0.4"
+version = "2.0.5"
 description = "HTTP library with thread-safe connection pooling, file post, and more."
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "urllib3-2.0.4-py3-none-any.whl", hash = "sha256:de7df1803967d2c2a98e4b11bb7d6bd9210474c46e8a0401514e3a42a75ebde4"},
+    {file = "urllib3-2.0.5-py3-none-any.whl", hash = "sha256:ef16afa8ba34a1f989db38e1dbbe0c302e4289a47856990d0682e374563ce35e"},
-    {file = "urllib3-2.0.4.tar.gz", hash = "sha256:8d22f86aae8ef5e410d4f539fde9ce6b2113a001bb4d189e0aed70642d602b11"},
+    {file = "urllib3-2.0.5.tar.gz", hash = "sha256:13abf37382ea2ce6fb744d4dad67838eec857c9f4f57009891805e0b5e123594"},
 ]
 
 [package.extras]

server/pyproject.toml

@@ -54,5 +54,7 @@ priority = "explicit"
 markers = ["private: marks tests as requiring an admin hf token (deselect with '-m \"not private\"')"]
 
 [build-system]
-requires = ["poetry-core>=1.0.0"]
+requires = [
+    "poetry-core>=1.0.0",
+]
 build-backend = "poetry.core.masonry.api"

server/text_generation_server/cli.py

@@ -17,6 +17,7 @@ class Quantization(str, Enum):
     bitsandbytes_nf4 = "bitsandbytes-nf4"
     bitsandbytes_fp4 = "bitsandbytes-fp4"
     gptq = "gptq"
+    awq = "awq"
 
 
 class Dtype(str, Enum):

server/text_generation_server/models/__init__.py

@@ -268,6 +268,10 @@ def get_model(
         raise ValueError(
             "gptq quantization is not supported for AutoModel, you can try to quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
         )
+    if quantize == "awq":
+        raise ValueError(
+            "awq quantization is not supported for AutoModel"
+        )
     elif (quantize == "bitsandbytes-fp4") or (quantize == "bitsandbytes-nf4"):
         raise ValueError(
             "4bit quantization is not supported for AutoModel"

server/text_generation_server/models/flash_llama.py

@@ -62,7 +62,7 @@ class FlashLlama(FlashCausalLM):
 
         filenames = weight_files(model_id, revision=revision, extension=".safetensors")
         weights = Weights(filenames, device, dtype, process_group=self.process_group)
-        if config.quantize == "gptq":
+        if config.quantize in ["gptq", "awq"]:
             weights._set_gptq_params(model_id)
 
         model = FlashLlamaForCausalLM(config, weights)

server/text_generation_server/utils/awq/quantize/qmodule.py

@@ -0,0 +1,48 @@
+# Copied logic from https://github.com/mit-han-lab/llm-awq/blob/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa/awq/quantize/qmodule.py
+
+import math
+import torch
+import torch.nn as nn
+import awq_inference_engine  # with CUDA kernels
+
+
+# class ScaledActivation(nn.Module):
+#     def __init__(self, module, scales):
+#         super().__init__()
+#         self.act = module
+#         self.scales = nn.Parameter(scales.data)
+#     
+#     def forward(self, x):
+#         return self.act(x) / self.scales.view(1, 1, -1).to(x.device)
+
+
+class WQLinear(nn.Module):
+    def __init__(self, w_bit, group_size, qweight, qzeros, scales, bias):
+        super().__init__()
+        
+        if w_bit not in [4]:
+            raise NotImplementedError("Only 4-bit are supported for now.")
+        
+        self.in_features = qweight.shape[0]
+        self.out_features = qweight.shape[1] * 32 // w_bit
+
+        self.w_bit = w_bit
+        self.group_size = group_size if group_size != -1 else self.in_features
+        # quick sanity check (make sure aligment)
+        assert self.in_features % self.group_size == 0
+        assert self.out_features % (32 // self.w_bit) == 0
+
+        self.qweight = qweight
+        self.qzeros = qzeros
+        self.scales = scales
+        if bias:
+            self.bias = bias
+        else:
+            self.bias = None
+
+    @torch.no_grad()
+    def forward(self, x):
+        out_shape = x.shape[:-1] + (self.out_features, )
+        out = awq_inference_engine.gemm_forward_cuda(x.reshape(-1, x.shape[-1]), self.qweight, self.scales, self.qzeros, 8)
+        out = out + self.bias if self.bias is not None else out
+        return out.reshape(out_shape)

server/text_generation_server/utils/layers.py

@@ -18,6 +18,13 @@ from accelerate import init_empty_weights
 
 from text_generation_server.utils.gptq.quant_linear import QuantLinear
 
+
+HAS_AWQ = True
+try: 
+    from text_generation_server.utils.awq.quantize.qmodule import WQLinear
+except ImportError:
+    HAS_AWQ = False
+
 try:
     major, _minor = torch.cuda.get_device_capability()
 except Exception:
@@ -248,6 +255,14 @@ def get_linear(weight, bias, quantize):
                 bits,
                 groupsize,
             )
+    elif quantize == "awq":
+        try:
+            qweight, qzeros, scales, _, bits, groupsize, _ = weight
+        except Exception:
+            raise NotImplementedError(
+                f"The passed weight is not `awq` compatible, loader needs to be updated."
+            )
+        linear = WQLinear(w_bit=bits, group_size=groupsize, qweight=qweight, qzeros=qzeros, scales=scales, bias=bias is not None)
     else:
         raise NotImplementedError(f"Quantization `{quantize}` is not implemented yet.")
     return linear
@@ -283,8 +298,8 @@ class TensorParallelHead(SuperLayer):
             weight = weights.get_tensor(f"{prefix}.weight")
             should_gather = False
 
-        # GPTQ doesn't quantize heads (nor embeddings)
+        # GPTQ and AWQ don't quantize heads (nor embeddings)
-        if config.quantize == "gptq":
+        if config.quantize in ["gptq", "awq"]:
             quantize = None
         else:
             quantize = config.quantize

server/text_generation_server/utils/weights.py

@@ -135,18 +135,21 @@ class Weights:
         Highly specific when the underlying tensor is a simple cat of Q,K,V instead of being
         already alternating Q,K,V within the main tensor
         """
-        if quantize == "gptq":
+        if quantize in ["gptq", "awq"]:
             try:
                 qweight = self._get_qweight(f"{prefix}.qweight") 
             except RuntimeError:
                 raise RuntimeError(
-                    "Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
+                    f"Cannot load `{quantize}` weight, make sure the model is already quantized."
                 )
 
             qzeros = self._get_qweight(f"{prefix}.qzeros") 
             scales = self._get_qweight(f"{prefix}.scales") 
             scales = scales.to(dtype=self.dtype)
+            if quantize == "gptq":
                 g_idx = self.get_tensor(f"{prefix}.g_idx")
+            else:
+                g_idx = None
 
             bits, groupsize = self._get_gptq_params()
             weight = (qweight, qzeros, scales, g_idx, bits, groupsize, False)
@@ -171,14 +174,14 @@ class Weights:
         return weight
 
     def get_multi_weights_col(self, prefixes: List[str], quantize: str, dim: int):
-        if quantize == "gptq":
+        if quantize in ["gptq", "awq"]:
             try:
                 qweight = torch.cat(
                     [self.get_sharded(f"{p}.qweight", dim=1) for p in prefixes], dim=1
                 )
             except RuntimeError:
                 raise RuntimeError(
-                    "Cannot load `gptq` weight, make sure the model is already quantized, or quantize it with `text-generation-server quantize ORIGINAL_MODEL_ID NEW_MODEL_ID`"
+                    f"Cannot load `{quantize}` weight, make sure the model is already quantized"
                 )
 
             qzeros = torch.cat(
@@ -187,10 +190,14 @@ class Weights:
             scales = torch.cat(
                 [self.get_sharded(f"{p}.scales", dim=1) for p in prefixes], dim=1
             )
+
+            if quantize == "gptq":
                 w = [self.get_tensor(f"{p}.g_idx") for p in prefixes]
                 for w2 in w[1:]:
                     torch.testing.assert_close(w2, w[0])
                 g_idx = w[0]
+            else:
+                g_idx = None
 
             bits, groupsize = self._get_gptq_params()
             weight = (qweight, qzeros, scales, g_idx, bits, groupsize, False)
@@ -281,6 +288,22 @@ class Weights:
                 scales = self.get_tensor(f"{prefix}.scales")
                 g_idx = self.get_sharded(f"{prefix}.g_idx", dim=0)
 
+            weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
+        elif quantize == "awq":
+            bits, groupsize = self._get_gptq_params()
+
+            try:
+                qweight = self.get_sharded(f"{prefix}.qweight", dim=0)
+            except RuntimeError:
+                raise RuntimeError(
+                    "Cannot load `awq` weight, make sure the model is already quantized"
+                )
+
+            qzeros = self.get_sharded(f"{prefix}.qzeros", dim=0)
+            scales = self.get_sharded(f"{prefix}.scales", dim=0)
+            g_idx = None
+            use_exllama = False
+            
             weight = (qweight, qzeros, scales, g_idx, bits, groupsize, use_exllama)
         else:
             weight = self.get_sharded(f"{prefix}.weight", dim=1)
@@ -321,5 +344,16 @@ class Weights:
                     data = json.load(f)
                 self.gptq_bits = data["bits"]
                 self.gptq_groupsize = data["group_size"]
+            except Exception:
+                filename = "quant_config.json"
+                try:
+                    if os.path.exists(os.path.join(model_id, filename)):
+                        filename = os.path.join(model_id, filename)
+                    else:
+                        filename = hf_hub_download(model_id, filename=filename)
+                    with open(filename, "r") as f:
+                        data = json.load(f)
+                    self.gptq_bits = data["w_bit"]
+                    self.gptq_groupsize = data["q_group_size"]
                 except Exception:
                     pass