style: apply ruff formatting

bench/generation/metrics/latency.py

@@ -68,7 +68,7 @@ def elapsed_time(self, other):
 
     memory = get_device_memory(device)
     if memory is not None:
-        print(f"Device memory: {memory / (2 ** 30):.4f} GB")
+        print(f"Device memory: {memory / (2**30):.4f} GB")
 
     latencies = []
     input_ids = torch.randint(1, model.config.vocab_size - 1, size=(batch_size, prompt_length)).to(device)
@@ -89,7 +89,7 @@ def elapsed_time(self, other):
 
     if device.type == "cuda":
         peak_memory = torch.cuda.max_memory_allocated()
-        print(f"Peak memory during benchmark: {peak_memory / (2 ** 30):.4f} GB")
+        print(f"Peak memory during benchmark: {peak_memory / (2**30):.4f} GB")
 
     mean_latency = np.mean(latencies) / generation_config.min_new_tokens
     print(f"Average latency per token: {mean_latency} ms")

bench/generation/setup/quanto.py

@@ -64,7 +64,7 @@ def setup(
                 calibrate(model, tokenizer, batch_size, batches=4)
         print("Freezing")
         freeze(model)
-        print(f"Finished: {time.time()-start:.2f}")
+        print(f"Finished: {time.time() - start:.2f}")
     return model, tokenizer
 
 

test/tensor/weights/weight_helpers.py

@@ -32,6 +32,6 @@ def check_weight_qtensor_linear(qweight, batch_size, tokens, use_bias, rel_max_e
     rel_max_err = max_err / mean_val
     # These values were evaluated empirically without any optimized kernels.
     rtol = {"cpu": 1e-2, "cuda": 2e-2, "mps": 1e-2, "xpu": 2e-2}[device.type]
-    assert (
-        rel_max_err < rtol
-    ), f"Maximum error {max_err:.2f} is too high for input of mean value {mean_val:.2f} ({rel_max_err*100:.2f} %)"
+    assert rel_max_err < rtol, (
+        f"Maximum error {max_err:.2f} is too high for input of mean value {mean_val:.2f} ({rel_max_err * 100:.2f} %)"
+    )