Merge branch 'develop_v2.7' of...

Merge branch 'develop_v2.7' of http://10.16.6.30/dcutoolkit/deeplearing/TransformerEngine into release_v2.7

transformer_engine/pytorch/cpp_extensions/gemm.py

@@ -488,31 +488,6 @@ def general_grouped_gemm(
         else:
             raise ValueError(f"Unsupported layout {layout} in int8 simulation fp8")
     
-    if int8_simulation_fp8 and (isinstance(A[0], Float8TensorBase) or isinstance(B[0], Float8TensorBase)) and int8_simulation_fp8_tensorwise:
-        assert not gelu, "GELU not supported with int8 simulation groupgemm."
-        assert TE_DType_To_Torch[out_dtype] is torch.bfloat16 or TE_DType_To_Torch[out_dtype] is torch.float32, "Out_dtype must be bfloat16 or float32 for int8 simulation"
-        bias = tex.te_general_grouped_gemm(
-            A,
-            transa,
-            B,
-            transb,
-            out,
-            out_dtype,
-            m_splits,
-            grad_bias if grad else bias,
-            bias_dtype,
-            single_output,
-            gelu_input,  # this is pre_gelu_out
-            grad,  # grad
-            workspaces,
-            workspaces[0].shape[0],
-            accumulate,
-            use_split_accumulator,
-            sm_count - int(os.getenv("NVTE_EXT_MARGIN_SM", str(sm_count))),
-        )
-    
-        return out, bias, gelu_input
-
     
     if int8_simulation_fp8 and (isinstance(A[0], Float8TensorBase) or isinstance(B[0], Float8TensorBase)) and int8_simulation_fp8_tensorwise and int8_simulation_fp8_tensorwise_batched:
         assert len(set(m_splits)) == 1, "Need token pad as same as batchgemm for NVTE_INT8_SIM_FP8_TENSORWISE_BATCHED."
@@ -626,6 +601,32 @@ def general_grouped_gemm(
             for i in range(num_gemms):
                 out[i].copy_(dw[i])
             return out, bias, gelu_input
+
+
+    if int8_simulation_fp8 and (isinstance(A[0], Float8TensorBase) or isinstance(B[0], Float8TensorBase)) and int8_simulation_fp8_tensorwise:
+        assert not gelu, "GELU not supported with int8 simulation groupgemm."
+        assert TE_DType_To_Torch[out_dtype] is torch.bfloat16 or TE_DType_To_Torch[out_dtype] is torch.float32, "Out_dtype must be bfloat16 or float32 for int8 simulation"
+        bias = tex.te_general_grouped_gemm(
+            A,
+            transa,
+            B,
+            transb,
+            out,
+            out_dtype,
+            m_splits,
+            grad_bias if grad else bias,
+            bias_dtype,
+            single_output,
+            gelu_input,  # this is pre_gelu_out
+            grad,  # grad
+            workspaces,
+            workspaces[0].shape[0],
+            accumulate,
+            use_split_accumulator,
+            sm_count - int(os.getenv("NVTE_EXT_MARGIN_SM", str(sm_count))),
+        )
+    
+        return out, bias, gelu_input
             
 
     if int8_simulation_fp8 and (isinstance(A[0], Float8TensorBase) or isinstance(B[0], Float8TensorBase)):

transformer_engine/pytorch/module/layernorm_linear.py

@@ -949,7 +949,7 @@ class _LayerNormLinear(torch.autograd.Function):
                 )
                 dgrad = dgrad.reshape(inputmat.size())
             elif ctx.normalization == "RMSNorm":
-                if enable_lightop and (rsigma is torch.bfloat16 or rsigma is torch.float16):
+                if enable_lightop and (rsigma.dtype is torch.bfloat16 or rsigma.dtype is torch.float16):
                     dgrad, dgamma =rmsnorm_backward(dgrad,inputmat,rsigma,ln_weight)
                 else: