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](https://github.com/mit-han-lab/llm-awq/tree/f084f40bd996f3cf3a0633c1ad7d9d476c318aaa).
* 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?
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).
- [ ] Did you write any new necessary tests?


## Who can review?

Anyone in the community is free to review the PR once the tests have
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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>

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)
-            g_idx = self.get_tensor(f"{prefix}.g_idx")
+            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
             )
-            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]
+
+            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)
@@ -322,4 +345,15 @@ class Weights:
                 self.gptq_bits = data["bits"]
                 self.gptq_groupsize = data["group_size"]
             except Exception:
-                pass
+                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