fix test which required accelerate, apply style (#194)

src/compressed_tensors/quantization/lifecycle/frozen.py

@@ -14,7 +14,6 @@
 
 
 from compressed_tensors.quantization.quant_config import QuantizationStatus
-from compressed_tensors.quantization.utils import is_kv_cache_quant_scheme
 from torch.nn import Module
 
 

src/compressed_tensors/quantization/observers/helpers.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 from collections import Counter
-from typing import Optional, Tuple
+from typing import Tuple
 
 import torch
 from compressed_tensors.quantization.quant_args import (

src/compressed_tensors/quantization/observers/mse.py

@@ -70,7 +70,9 @@ def calculate_mse_min_max(
             absolute_min_val = torch.amin(observed, dim=reduce_dims, keepdims=True)
             absolute_max_val = torch.amax(observed, dim=reduce_dims, keepdims=True)
 
-        best = torch.full_like(absolute_min_val, torch.finfo(absolute_min_val.dtype).max)
+        best = torch.full_like(
+            absolute_min_val, torch.finfo(absolute_min_val.dtype).max
+        )
         min_val = torch.ones_like(absolute_min_val)
         max_val = torch.zeros_like(absolute_max_val)
         for i in range(int(self.maxshrink * self.grid)):

tests/test_quantization/lifecycle/test_apply.py

@@ -236,14 +236,8 @@ def get_sample_tinyllama_quant_config(status: str = "frozen"):
 def test_apply_quantization_status(caplog, ignore, should_raise_warning):
     import logging
 
-    from transformers import AutoModelForCausalLM
-
     # load a dense, unquantized tiny llama model
-    model_name = "TinyLlama/TinyLlama-1.1B-intermediate-step-1431k-3T"
-    model = AutoModelForCausalLM.from_pretrained(
-        model_name, device_map="cpu", torch_dtype="auto"
-    )
-
+    model = get_tinyllama_model()
     quantization_config_dict = {
         "quant_method": "sparseml",
         "format": "pack-quantized",

tests/test_quantization/test_observers/test_mse.py

@@ -27,7 +27,7 @@
     ],
 )
 def test_mse_observer(symmetric, expected_scale, expected_zero_point):
-    tensor = torch.tensor([1., 1., 1., 1., 1.])
+    tensor = torch.tensor([1.0, 1.0, 1.0, 1.0, 1.0])
     num_bits = 8
     weights = QuantizationArgs(num_bits=num_bits, symmetric=symmetric, observer="mse")