[CI] Use explicit devices in quantization tests (#49512)

Signed-off-by: Andreas Karatzas <Andreas.Karatzas@amd.com>

tests/kernels/quantization/test_block_int8.py

@@ -28,12 +28,6 @@ BLOCK_SIZE = [[128, 128]]
 SEEDS = [0]
 
 
-@pytest.fixture(autouse=True, scope="module")
-def setup_cuda():
-    """Sets the default CUDA device for all tests in this module."""
-    torch.set_default_device("cuda")
-
-
 @pytest.mark.parametrize(
     "M,N,K,block_size,out_dtype,seed",
     itertools.product(M, N, K, BLOCK_SIZE, DTYPES, SEEDS),
@@ -41,22 +35,28 @@ def setup_cuda():
 @torch.inference_mode()
 def test_w8a8_block_int8_matmul(M, N, K, block_size, out_dtype, seed):
     torch.manual_seed(seed)
+    device = current_platform.device_type
     factor_for_scale = 1e-2
     int8_info = torch.iinfo(torch.int8)
     int8_max, int8_min = int8_info.max, int8_info.min
 
-    A_fp32 = (torch.rand(M, K, dtype=torch.float32) - 0.5) * 2 * int8_max
+    A_fp32 = torch.rand(M, K, dtype=torch.float32, device=device)
+    A_fp32 = (A_fp32 - 0.5) * 2 * int8_max
     A_fp8 = A_fp32.clamp(min=int8_min, max=int8_max).to(torch.float8_e4m3fn)
 
-    B_fp32 = (torch.rand(N, K, dtype=torch.float32) - 0.5) * 2 * int8_max
+    B_fp32 = torch.rand(N, K, dtype=torch.float32, device=device)
+    B_fp32 = (B_fp32 - 0.5) * 2 * int8_max
     B_fp8 = B_fp32.clamp(min=int8_min, max=int8_max).to(torch.float8_e4m3fn)
 
     block_n, block_k = block_size[0], block_size[1]
     n_tiles = (N + block_n - 1) // block_n
     k_tiles = (K + block_k - 1) // block_k
 
-    As = torch.rand(M, k_tiles, dtype=torch.float32) * factor_for_scale
-    Bs = torch.rand(n_tiles, k_tiles, dtype=torch.float32) * factor_for_scale
+    As = torch.rand(M, k_tiles, dtype=torch.float32, device=device) * factor_for_scale
+    Bs = (
+        torch.rand(n_tiles, k_tiles, dtype=torch.float32, device=device)
+        * factor_for_scale
+    )
 
     ref_out = native_w8a8_block_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)
     out = w8a8_block_int8_matmul(A_fp8, B_fp8, As, Bs, block_size, out_dtype)

tests/kernels/quantization/test_int8_kernel.py

@@ -82,12 +82,6 @@ def torch_w8a8_per_column_moe(a, w1, w2, w1_s, w2_s, topk, topk_weight, topk_ids
     ).sum(dim=1)
 
 
-@pytest.fixture(autouse=True, scope="module")
-def setup_cuda():
-    """Sets the default CUDA device for all tests in this module."""
-    torch.set_default_device("cuda")
-
-
 DTYPES = [torch.half, torch.bfloat16]
 M = [1, 33]
 N = [128, 1024]
@@ -104,6 +98,7 @@ SEEDS = [0]
 @torch.inference_mode()
 def test_w8a8_fp8_fused_moe(default_vllm_config, M, N, K, E, topk, dtype, seed):
     torch.manual_seed(seed)
+    device = current_platform.device_type
     # Initialize int8 quantization parameters
     factor_for_scale = 1e-2
     int8_max = 127
@@ -111,19 +106,26 @@ def test_w8a8_fp8_fused_moe(default_vllm_config, M, N, K, E, topk, dtype, seed):
 
     # Input tensor
     # M * K
-    a = torch.randn((M, K), dtype=dtype) / 10
+    a = torch.randn((M, K), dtype=dtype, device=device) / 10
 
     # Generate int8 weights
-    w1_fp32 = (torch.rand((E, 2 * N, K), dtype=torch.float32) - 0.5) * 2
+    w1_fp32 = (
+        torch.rand(
+            (E, 2 * N, K),
+            dtype=torch.float32,
+            device=device,
+        )
+        - 0.5
+    ) * 2
     w1 = (w1_fp32 * int8_max).clamp(min=int8_min, max=int8_max).to(torch.int8)
 
-    w2_fp32 = (torch.rand((E, K, N), dtype=torch.float32) - 0.5) * 2
+    w2_fp32 = (torch.rand((E, K, N), dtype=torch.float32, device=device) - 0.5) * 2
     w2 = (w2_fp32 * int8_max).clamp(min=int8_min, max=int8_max).to(torch.int8)
 
     # Generate scale for each column (per-column quantization)
     w1_s = torch.rand(E, 2 * N, device=w1_fp32.device) * factor_for_scale
     w2_s = torch.rand(E, K, device=w2_fp32.device) * factor_for_scale
-    score = torch.randn((M, E), dtype=dtype)
+    score = torch.randn((M, E), dtype=dtype, device=device)
     score = torch.softmax(score, dim=-1, dtype=torch.float32)
     topk_weights, topk_ids = torch.topk(score, topk)