[DCU] fix tensorwise int8 moe bugs

transformer_engine/pytorch/cpp_extensions/gemm.py

@@ -473,13 +473,13 @@ def general_grouped_gemm(
         else:
             raise ValueError(f"Unsupported layout {layout} in int8 simulation fp8")
     
-    if int8_simulation_fp8 and (isinstance(A, Float8TensorBase) or isinstance(B, Float8TensorBase)) and int8_simulation_fp8_tensorwise:
+    if int8_simulation_fp8 and (isinstance(A[0], Float8TensorBase) or isinstance(B[0], Float8TensorBase)) and int8_simulation_fp8_tensorwise:
         assert len(set(m_splits)) == 1, "Int8 simulation groupgemm just surpport token pad as same as batchgemm for now."
         assert not gelu, "GELU not supported with int8 simulation groupgemm."
         assert not use_bias, "Bias not supported with int8 simulation groupgemm."
-        assert out_dtype is torch.bfloat16 or out_dtype is torch.float32, "Out_dtype must be bfloat16 or float32 for int8 simulation"
+        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"
         if layout == "TN":
-            assert out_dtype is torch.bfloat16
+            assert TE_DType_To_Torch[out_dtype] is torch.bfloat16
             qx_data_list, scales_x_list = [b._data.view(dtype=TE_DType_To_Torch[b._fp8_dtype]) for b in B], [b._scale_inv for b in B]
             w_data_list, scales_w_list = [a._data.view(dtype=TE_DType_To_Torch[a._fp8_dtype]) for a in A], [a._scale_inv for a in A]
             
@@ -501,7 +501,7 @@ def general_grouped_gemm(
                 num_gemms,
                 None,
                 TE_DType[out_dtype],
-                bias,
+                bias[0],
                 bias_dtype,
                 gelu,
                 gelu_input[0],
@@ -514,7 +514,7 @@ def general_grouped_gemm(
             return out, bias, gelu_input
         
         if layout == "NN":
-            assert out_dtype is torch.bfloat16
+            assert TE_DType_To_Torch[out_dtype] is torch.bfloat16
             qdout_data_list, scales_dout_list = [b._data.view(dtype=TE_DType_To_Torch[b._fp8_dtype]) for b in B], [b._scale_inv for b in B]
             w_data_list, scales_w_list = [a._data.view(dtype=TE_DType_To_Torch[a._fp8_dtype]) for a in A], [a._scale_inv for a in A]
             
@@ -536,7 +536,7 @@ def general_grouped_gemm(
                 num_gemms,
                 None,
                 TE_DType[out_dtype],
-                bias,
+                bias[0],
                 bias_dtype,
                 gelu,
                 gelu_input[0],
@@ -549,7 +549,7 @@ def general_grouped_gemm(
             return out, bias, gelu_input
             
         elif layout == "NT":
-            assert out_dtype is torch.bfloat16 or out_dtype is torch.float32
+            assert TE_DType_To_Torch[out_dtype] is torch.bfloat16 or TE_DType_To_Torch[out_dtype] is torch.float32
             qdout_data_list, scales_dout_list = [b._data.view(dtype=TE_DType_To_Torch[b._fp8_dtype]) for b in B], [b._scale_inv for b in B]
             qx_data_list, scales_x_list = [a._data.view(dtype=TE_DType_To_Torch[a._fp8_dtype]) for a in A], [a._scale_inv for a in A]
             
@@ -572,7 +572,7 @@ def general_grouped_gemm(
                 num_gemms,
                 None,
                 TE_DType[out_dtype],
-                bias,
+                bias[0],
                 bias_dtype,
                 gelu,
                 gelu_input[0],
@@ -591,10 +591,10 @@ def general_grouped_gemm(
         assert len(set(m_splits)) == 1, "Int8 simulation groupgemm just surpport token pad as same as batchgemm for now."
         assert not gelu, "GELU not supported with int8 simulation groupgemm."
         assert not use_bias, "Bias not supported with int8 simulation groupgemm."
-        assert out_dtype is torch.bfloat16 or out_dtype is torch.float32, "Out_dtype must be bfloat16 or float32 for int8 simulation"
+        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"
 
         if layout == "TN":
-            assert out_dtype is torch.bfloat16
+            assert TE_DType_To_Torch[out_dtype] is torch.bfloat16
             qx_data_list = []
             w_data_list = []
             scales_x_list = []
@@ -642,7 +642,7 @@ def general_grouped_gemm(
             return out, bias, gelu_input
         
         elif layout == "NN":
-            assert out_dtype is torch.bfloat16
+            assert TE_DType_To_Torch[out_dtype] is torch.bfloat16
             qdout_data_list = []
             w_data_list = []
             scales_dout_list = []
@@ -690,7 +690,7 @@ def general_grouped_gemm(
             return out, bias, gelu_input
 
         elif layout == "NT":
-            assert out_dtype is torch.bfloat16 or out_dtype is torch.float32
+            assert TE_DType_To_Torch[out_dtype] is torch.bfloat16 or TE_DType_To_Torch[out_dtype] is torch.float32
             qdout_data_list = []
             qx_data_list = []
             scales_dout_list = []
@@ -730,7 +730,7 @@ def general_grouped_gemm(
                 use_split_accumulator,
             )[0]        
 
-            if out_dtype is torch.bfloat16:
+            if TE_DType_To_Torch[out_dtype] is torch.bfloat16:
                 if accumulate:
                     for i in num_gemms:
                         channelwise_dequantize_transA_add(scales_dout_list[i], scales_x_list[i], dw_int32[i], out[i])

transformer_engine/pytorch/csrc/extensions/gemm.cpp

@@ -49,8 +49,10 @@ std::vector<size_t> getGemmOutputShape(const NVTEShape& A_shape, const bool tran
   const size_t B1 = B_shape.data[B_shape.ndim - 1];
 
   // Check matrix dims
+  if (transa || transb) {
     NVTE_CHECK((transa ? A1 : A0) == (transb ? B0 : B1), "Invalid matrix dimensions for GEMM (A=(",
                A0, ",", A1, "), transa=", transa, ", B=(", B0, ",", B1, "), transb=", transb, ")");
+  }
 
   // Construct output dims
   std::vector<size_t> ret;