[DCU] fix some bugs in test_numerics.py

tests/pytorch/test_numerics.py

@@ -56,7 +56,7 @@ from utils import ModelConfig, reset_rng_states, get_available_attention_backend
 # Only run FP8 tests on supported devices.
 fp8_available, reason_for_no_fp8 = FP8GlobalStateManager.is_fp8_available()
 mxfp8_available, reason_for_no_mxfp8 = FP8GlobalStateManager.is_mxfp8_available()
-fp8_block_scaling_available, _ = FP8GlobalStateManager.is_fp8_block_scaling_available()
+fp8_block_scaling_available, reason_for_no_fp8_block_scaling = FP8GlobalStateManager.is_fp8_block_scaling_available()
 
 sm_80plus = get_device_compute_capability() >= (8, 0)
 
@@ -606,6 +606,13 @@ def _test_e2e_selective_recompute(
 def test_gpt_selective_activation_recompute(dtype, bs, model, fp8, recipe, fp8_model_params):
     if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
         pytest.skip("FP8 parameters are not supported in debug mode.")
+    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
+        pytest.skip(reason_for_no_fp8_block_scaling)
+    if fp8 and not fp8_available:
+        pytest.skip(reason_for_no_fp8)
+    if recipe.mxfp8() and not mxfp8_available:
+        pytest.skip(reason_for_no_mxfp8)
+
 
     config = model_configs[model]
 
@@ -714,8 +721,15 @@ def _test_e2e_full_recompute(
 def test_gpt_full_activation_recompute(
     dtype, bs, model, fp8, recipe, fp8_model_params, use_reentrant
 ):
+    if fp8 and not fp8_available:
+        pytest.skip(reason_for_no_fp8)
+    if recipe.mxfp8() and not mxfp8_available:
+        pytest.skip(reason_for_no_mxfp8)
     if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
         pytest.skip("FP8 parameters are not supported in debug mode.")
+    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
+        pytest.skip(reason_for_no_fp8_block_scaling)
+
 
     config = model_configs[model]
 
@@ -1301,9 +1315,14 @@ def test_linear_accuracy_save_original_input(dtype, model, recipe):
     fuse_wgrad_accumulation = True
     fp8_model_params = False
     fp8 = recipe is not None
-
+    if fp8 and not fp8_available:
+        pytest.skip(reason_for_no_fp8)
     if fp8 and recipe.delayed():
         pytest.skip("DelayedScaling recipe is not supported with save_original_input")
+    if fp8 and recipe.mxfp8() and not mxfp8_available:
+        pytest.skip(reason_for_no_mxfp8)
+    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
+        pytest.skip(reason_for_no_fp8_block_scaling)
 
     config = model_configs[model]
     if config.max_seqlen_q % 16 != 0 and fp8:
@@ -1818,6 +1837,12 @@ def test_grouped_linear_accuracy(
     use_cutlass=False,
 ):
     fp8 = recipe is not None
+    if fp8 and not fp8_available:
+        pytest.skip(reason_for_no_fp8)
+    if fp8 and recipe.mxfp8() and not mxfp8_available:
+        pytest.skip(reason_for_no_mxfp8)
+    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
+        pytest.skip(reason_for_no_fp8_block_scaling)
     if fp8 and fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
         pytest.skip("FP8 parameters are not supported in debug mode.")
 
@@ -1863,7 +1888,8 @@ def test_grouped_linear_accuracy(
                 weight_i = getattr(grouped_linear, f"weight{i}")
                 weight_i.main_grad = torch.rand_like(weight_i, dtype=torch.float32)
                 sequential_linear[i].weight.main_grad = weight_i.main_grad.clone()
-
+    if IS_HIP_EXTENSION:
+        os.environ["NVTE_FORCE_ROCM_GEMM"] = "1"
     outputs_ref = _test_grouped_linear_accuracy(
         sequential_linear,
         num_gemms,
@@ -1886,7 +1912,8 @@ def test_grouped_linear_accuracy(
         fuse_wgrad_accumulation,
         delay_wgrad_compute,
     )
-
+    if IS_HIP_EXTENSION:
+        os.environ["NVTE_FORCE_ROCM_GEMM"] = "0"
     for o, o_ref in zip(outputs, outputs_ref):
         if use_cutlass:
             torch.testing.assert_close(o, o_ref, rtol=1e-3, atol=1e-3)
@@ -1956,6 +1983,12 @@ def test_grouped_linear_accuracy_save_original_input(
         pytest.skip("FP8 parameters are not supported in debug mode.")
     if fp8 and recipe.delayed():
         pytest.skip("DelayedScaling recipe is not supported with save_original_input")
+    if fp8 and not fp8_available:
+        pytest.skip(reason_for_no_fp8)
+    if fp8 and recipe.mxfp8() and not mxfp8_available:
+        pytest.skip(reason_for_no_mxfp8)
+    if fp8 and recipe.float8_block_scaling() and not fp8_block_scaling_available:
+        pytest.skip(reason_for_no_fp8_block_scaling)
 
     config = model_configs[model]
     if config.max_seqlen_q % 16 != 0 and fp8:
@@ -2162,8 +2195,14 @@ def test_padding_grouped_linear_accuracy(
     fp8_model_params,
     parallel_mode=None,
 ):
+    if fp8 and not fp8_available:
+        pytest.skip(reason_for_no_fp8)
+    if recipe.mxfp8() and not mxfp8_available:
+        pytest.skip(reason_for_no_mxfp8)
     if fp8_model_params and NVTE_TEST_NVINSPECT_ENABLED:
         pytest.skip("FP8 parameters are not supported in debug mode.")
+    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
+        pytest.skip(reason_for_no_fp8_block_scaling)
 
     config = model_configs[model]
     if config.max_seqlen_q % 16 != 0 and fp8:
@@ -2235,6 +2274,12 @@ def test_padding_grouped_linear_accuracy_save_original_input(
         pytest.skip("FP8 parameters are not supported in debug mode.")
     if fp8 and recipe.delayed():
         pytest.skip("DelayedScaling recipe is not supported with save_original_input")
+    if fp8 and not fp8_available:
+        pytest.skip(reason_for_no_fp8)
+    if recipe.mxfp8() and not mxfp8_available:
+        pytest.skip(reason_for_no_mxfp8)
+    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
+        pytest.skip(reason_for_no_fp8_block_scaling)
 
     config = model_configs[model]
     if config.max_seqlen_q % 16 != 0 and fp8:
@@ -2446,8 +2491,14 @@ def _test_gpt_fp8_parameters(bs, dtype, config, fp8_model_params, recipe):
 @pytest.mark.parametrize("model", ["126m"])
 @pytest.mark.parametrize("recipe", fp8_recipes)
 def test_gpt_fp8_parameters(dtype, bs, model, recipe):
+    if not fp8_available:
+        pytest.skip(reason_for_no_fp8)
+    if recipe.mxfp8() and not mxfp8_available:
+        pytest.skip(reason_for_no_mxfp8)
     if NVTE_TEST_NVINSPECT_ENABLED:
         pytest.skip("FP8 parameters are not supported in debug mode.")
+    if recipe.float8_block_scaling() and not fp8_block_scaling_available:
+        pytest.skip(reason_for_no_fp8_block_scaling)
 
     config = model_configs[model]
 

transformer_engine/pytorch/module/_common.py

@@ -53,7 +53,7 @@ def apply_normalization(
     normalization_func = _get_normalization_func(normalization, True)
 
     inputs = (inputmat, ln_weight) if ln_bias is None else (inputmat, ln_weight, ln_bias)
-    if enable_lightop and (ln_bias is None) and normalization == "RMSNorm" and output_quantizer is None and (output_dtype is torch.bfloat16 or output_dtype is torch.float16 or output_dtype is torch.float32):
+    if enable_lightop and (ln_bias is None) and normalization == "RMSNorm" and output_quantizer is None and (output_dtype is torch.bfloat16 or output_dtype is torch.float16 or output_dtype is torch.float32) and not zero_centered_gamma:
         out, rsigma = rmsnorm_forward(inputmat, ln_weight,ln_out,eps,True)
         return out, None, rsigma
     else: