fix(server): fix quantization python requirements (#708)

server/poetry.lock

@@ -624,6 +624,14 @@ python-versions = ">=3.8"
 [package.dependencies]
 mpmath = ">=0.19"
 
+[[package]]
+name = "texttable"
+version = "1.6.7"
+description = "module to create simple ASCII tables"
+category = "main"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "tokenizers"
 version = "0.13.3"
@@ -810,7 +818,7 @@ bnb = ["bitsandbytes"]
 [metadata]
 lock-version = "1.1"
 python-versions = "^3.9"
-content-hash = "65afc4bfa07da4b1427d269fa745939da3851eaede9a8478f5a4bf5949d32cc9"
+content-hash = "c2e0d926748a7d420909c6bd21e17cf060bc7acdd788ae93e3ec1809a4b84529"
 
 [metadata.files]
 accelerate = [
@@ -1484,6 +1492,10 @@ sympy = [
     {file = "sympy-1.12-py3-none-any.whl", hash = "sha256:c3588cd4295d0c0f603d0f2ae780587e64e2efeedb3521e46b9bb1d08d184fa5"},
     {file = "sympy-1.12.tar.gz", hash = "sha256:ebf595c8dac3e0fdc4152c51878b498396ec7f30e7a914d6071e674d49420fb8"},
 ]
+texttable = [
+    {file = "texttable-1.6.7-py2.py3-none-any.whl", hash = "sha256:b7b68139aa8a6339d2c320ca8b1dc42d13a7831a346b446cb9eb385f0c76310c"},
+    {file = "texttable-1.6.7.tar.gz", hash = "sha256:290348fb67f7746931bcdfd55ac7584ecd4e5b0846ab164333f0794b121760f2"},
+]
 tokenizers = [
     {file = "tokenizers-0.13.3-cp310-cp310-macosx_10_11_x86_64.whl", hash = "sha256:f3835c5be51de8c0a092058a4d4380cb9244fb34681fd0a295fbf0a52a5fdf33"},
     {file = "tokenizers-0.13.3-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:4ef4c3e821730f2692489e926b184321e887f34fb8a6b80b8096b966ba663d07"},

server/pyproject.toml

@@ -28,6 +28,7 @@ tokenizers = "0.13.3"
 huggingface-hub = "^0.14.1"
 transformers = "4.29.2"
 einops = "^0.6.1"
+texttable = "^1.6.7"
 
 [tool.poetry.extras]
 accelerate = ["accelerate"]

server/requirements.txt

@@ -35,6 +35,7 @@ requests==2.31.0 ; python_version >= "3.9" and python_version < "4.0"
 safetensors==0.3.1 ; python_version >= "3.9" and python_version < "4.0"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "4.0"
 setuptools==68.0.0 ; python_version >= "3.9" and python_version < "4.0"
+texttable==1.6.7 ; python_version >= "3.9" and python_version < "4.0"
 tokenizers==0.13.3 ; python_version >= "3.9" and python_version < "4.0"
 tqdm==4.65.0 ; python_version >= "3.9" and python_version < "4.0"
 transformers==4.29.2 ; python_version >= "3.9" and python_version < "4.0"

server/text_generation_server/models/flash_rw.py

@@ -61,7 +61,6 @@ class FlashRWSharded(FlashCausalLM):
         if config.quantize == "gptq":
             weights._set_gptq_params(model_id)
 
-
         model = FlashRWForCausalLM(config, weights)
 
         torch.distributed.barrier(group=self.process_group)

server/text_generation_server/server.py

@@ -105,21 +105,21 @@ class TextGenerationService(generate_pb2_grpc.TextGenerationServiceServicer):
 
 
 def serve(
-        model_id: str,
-        revision: Optional[str],
-        sharded: bool,
-        quantize: Optional[str],
-        dtype: Optional[str],
-        trust_remote_code: bool,
-        uds_path: Path,
+    model_id: str,
+    revision: Optional[str],
+    sharded: bool,
+    quantize: Optional[str],
+    dtype: Optional[str],
+    trust_remote_code: bool,
+    uds_path: Path,
 ):
     async def serve_inner(
-            model_id: str,
-            revision: Optional[str],
-            sharded: bool = False,
-            quantize: Optional[str] = None,
-            dtype: Optional[str] = None,
-            trust_remote_code: bool = False,
+        model_id: str,
+        revision: Optional[str],
+        sharded: bool = False,
+        quantize: Optional[str] = None,
+        dtype: Optional[str] = None,
+        trust_remote_code: bool = False,
     ):
         unix_socket_template = "unix://{}-{}"
         if sharded:

server/text_generation_server/utils/gptq/quantize.py

-import argparse
 import time
-import numpy as np
-import torch
 import torch.nn as nn
 import math
 import json
 import os
+import torch
+import transformers
 
 from texttable import Texttable
 from transformers import AutoModelForCausalLM, AutoConfig, AutoTokenizer
-import transformers
 from huggingface_hub import HfApi
-import numpy as np
-import torch
 from accelerate import init_empty_weights
 from text_generation_server.utils import initialize_torch_distributed, Weights
 from text_generation_server.utils.hub import weight_files