Speed up the `kernels/quantization/` tests (#18669)

Signed-off-by: mgoin <mgoin64@gmail.com>

tests/kernels/quantization/test_block_fp8.py

@@ -36,16 +36,16 @@ vllm_config.scheduler_config.max_model_len = 8192
 
 # Test configurations
 DTYPES = [torch.bfloat16]  # [torch.half, torch.bfloat16, torch.float32]
-NUM_TOKENS = [7, 83, 2048]
+NUM_TOKENS = [7, 2050]
 D = [512, 4096, 5120, 13824]
-GROUP_SIZE = [64, 128, 256, 512]
-M = [1, 7, 8, 83, 84, 512, 2048, 4096]
-N = [128, 512, 1024, 4096, 7168, 7748, 13824]
-K = [256, 4096, 5120, 3884, 13824, 16384]
+GROUP_SIZE = [64, 128, 512]
+M = [1, 7, 8, 83, 84, 4096]
+N = [128, 512, 7168, 7748, 13824]
+K = [256, 3884, 4096, 13824, 16384]
 # Deepseek-V3's intermediate size 18432, so N is 18432*2/8=4608 at TP8
 # and its hidden size is 7168.
-M_moe = [1, 2, 7, 83, 128, 512, 2048]
-M_moe_dg = [128, 192, 512, 1335, 2048]
+M_moe = [1, 2, 7, 83, 128, 2048]
+M_moe_dg = [128, 192, 1335, 2048]
 N_moe = [128, 256, 1024, 4608]  # [13824]
 K_moe = [256, 512, 7168]  # [13824]
 BLOCK_SIZE = [[128, 128]]

tests/kernels/quantization/test_gguf.py

@@ -35,11 +35,11 @@ def get_gguf_MoE_tensors(
     return GGUFReader(sample_file).tensors
 
 
-DTYPES = [torch.half, torch.bfloat16, torch.float32]
+DTYPES = [torch.bfloat16]  # [torch.half, torch.bfloat16, torch.float32]
 # Hidden_size for testing, must match the sample file in HF repo,
 # we have `hidden_size = 256, 1024` for test in HF repo currently.
 HIDDEN_SIZES = [256, 1024]
-NUM_TOKENS = [7, 83, 128, 2048]  # Arbitrary values for testing
+NUM_TOKENS = [7, 2050]  # Arbitrary values for testing
 SEEDS = [0]
 QUANT_TYPES = [
     # i-matrix

tests/kernels/quantization/test_triton_scaled_mm.py

@@ -13,8 +13,13 @@ from vllm.platforms import current_platform
 
 device = "cuda"
 
+triton_scaled_mm_module = importlib.import_module(
+    "vllm.model_executor.layers.quantization.compressed_tensors."
+    "triton_scaled_mm")
+triton_scaled_mm = triton_scaled_mm_module.triton_scaled_mm
 
-def scaled_mm_torch(a: torch.Tensor,
+
+def torch_scaled_mm(a: torch.Tensor,
                     b: torch.Tensor,
                     scale_a: torch.Tensor,
                     scale_b: torch.Tensor,
@@ -101,21 +106,8 @@ def test_scaled_mm(M, N, K, in_dtype, out_dtype, use_scalar_scale_a,
     if use_bias:
         bias = torch.rand((N, ), device=device, dtype=out_dtype)
 
-    triton_scaled_mm_module = importlib.import_module(
-        "vllm.model_executor.layers.quantization.compressed_tensors."
-        "triton_scaled_mm")
-    triton_scaled_mm = triton_scaled_mm_module.triton_scaled_mm
-
     c_check = triton_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
 
-    a_cpu = a.cpu()
-    b_cpu = b.cpu()
-    scale_a_cpu = scale_a.cpu()
-    scale_b_cpu = scale_b.cpu()
-    bias_cpu = None if bias is None else bias.cpu()
-
-    c_actual = scaled_mm_torch(a_cpu, b_cpu, scale_a_cpu, scale_b_cpu,
-                               out_dtype, bias_cpu)
+    c_actual = torch_scaled_mm(a, b, scale_a, scale_b, out_dtype, bias)
 
-    c_check_cpu = c_check.cpu()
-    torch.testing.assert_close(c_check_cpu, c_actual, rtol=1e-1, atol=1e-1)
+    torch.testing.assert_close(c_check, c_actual, rtol=1e-1, atol=1e-1)