[hybrid] Fix row parallel linear bias

python/paddle/distributed/collective.py

@@ -1078,6 +1078,19 @@ def _linear(x, weight, bias=None, name=None):
         return res
 
 
+def _set_var_distributed(var):
+    if var is None:
+        return
+
+    var.is_distributed = True
+
+    # NOTE: use current_block and find_var_recursive to support while_loop
+    startup_block = paddle.static.default_startup_program().current_block()
+    main_block = paddle.static.default_main_program().current_block()
+    startup_block._find_var_recursive(var.name).is_distributed = True
+    main_block._find_var_recursive(var.name).is_distributed = True
+
+
 def _parallel_linear(x,
                      num_rows,
                      num_cols,
@@ -1095,7 +1108,7 @@ def _parallel_linear(x,
 
     axis the dimension of the parameter of linear layer. 
     axis = 0: the row dimension
-    axid = 1: the col dimension
+    axis = 1: the col dimension
 
     """
     if group is not None and not group.is_member():
@@ -1108,40 +1121,35 @@ def _parallel_linear(x,
     else:
         x = _c_identity(x, group=group)
 
-    if core.is_compiled_with_npu():
-        linear = _Linear(
-            num_rows,
-            num_cols,
-            weight_attr=param_attr,
-            bias_attr=bias_attr,
-            name=name)
-    else:
-        linear = paddle.nn.Linear(
-            num_rows,
-            num_cols,
-            weight_attr=param_attr,
-            bias_attr=bias_attr,
-            name=name)
-
-    linear_out = linear(x)
-    startup_block = paddle.static.default_startup_program().current_block()
-    main_block = paddle.static.default_main_program().current_block()
-    startup_block._find_var_recursive(linear.weight.name).is_distributed = True
-    main_block._find_var_recursive(linear.weight.name).is_distributed = True
+    linear = paddle.nn.Linear(
+        num_rows,
+        num_cols,
+        weight_attr=param_attr,
+        bias_attr=bias_attr,
+        name=name)
 
+    # NOTE: npu linear function use matmul_v2 but linear use matmul
+    linear_function = _linear if core.is_compiled_with_npu()\
+        else paddle.nn.functional.linear
+    linear_out = linear_function(
+        x,
+        linear.weight,
+        # NOTE(wangxi): row split, bias need add after allreduce
+        None if axis == 0 else linear.bias,
+        linear.name)
+
+    _set_var_distributed(linear.weight)
     # set is_distributed for splited bias
     # if a linear layer is splited by row, each rank would hold a complete bias and they should be the same in each rank.
     # if a linear layer is splited by col, the bias would also be split into each rank as its weight
     if axis == 1 and linear._bias_attr != False:
-        startup_block._find_var_recursive(
-            linear.bias.name).is_distributed = True
-        main_block._find_var_recursive(linear.bias.name).is_distributed = True
+        _set_var_distributed(linear.bias)
 
     if not gather_out: return linear_out
 
-    op_type = 'c_allreduce_sum' if axis == 0 else 'c_concat'
     out_shape = list(linear_out.shape)
     out_shape[0] *= 1 if axis == 0 else nranks
+    main_block = paddle.static.default_main_program().current_block()
     out = main_block.create_var(
         shape=out_shape,
         dtype=linear_out.dtype,
@@ -1160,6 +1168,8 @@ def _parallel_linear(x,
                 'use_calc_stream': True,
                 'use_model_parallel': True
             })
+        if linear.bias is not None:
+            out = out + linear.bias
     else:
         main_block.append_op(
             type='c_concat',

python/paddle/fluid/tests/unittests/static_model_parallel_by_col.py

@@ -43,29 +43,38 @@
 #fluid.default_main_program().random_seed = 1
 
 
+def get_param_attr(weight, bias):
+    weight_attr = paddle.ParamAttr(
+        initializer=fluid.initializer.NumpyArrayInitializer(weight))
+    bias_attr = paddle.ParamAttr(
+        initializer=fluid.initializer.NumpyArrayInitializer(bias))
+    return weight_attr, bias_attr
+
+
 def create_model(data, rank):
     np.random.seed(2021)
     np_weight = np.random.uniform(-1, 1, size=(IN_SIZE, OUT_SIZE)).astype(DTYPE)
+    np_bias = np.random.uniform(-1, 1, size=(OUT_SIZE, )).astype(DTYPE)
     if rank is not None:
         start_col = 0 if rank == 0 else OUT_SIZE // 2
         np_weight_part = np_weight[:, start_col:start_col + OUT_SIZE // 2]
+        np_bias_part = np_bias[start_col:start_col + OUT_SIZE // 2]
+
+        weight_attr, bias_attr = get_param_attr(np_weight_part, np_bias_part)
         result = paddle.distributed.split(
             data,
             size=(IN_SIZE, OUT_SIZE),
             operation='linear',
             axis=1,
             num_partitions=MODEL_PARALLEL_SIZE,
-            weight_attr=paddle.ParamAttr(
-                initializer=fluid.initializer.NumpyArrayInitializer(
-                    np_weight_part)),
-            bias_attr=False, )
+            weight_attr=weight_attr,
+            bias_attr=bias_attr)
     else:
-        result = fluid.layers.fc(
-            data,
-            size=OUT_SIZE,
-            param_attr=paddle.ParamAttr(
-                initializer=fluid.initializer.NumpyArrayInitializer(np_weight)),
-            bias_attr=False, )
+        weight_attr, bias_attr = get_param_attr(np_weight, np_bias)
+        result = fluid.layers.fc(data,
+                                 size=OUT_SIZE,
+                                 param_attr=weight_attr,
+                                 bias_attr=bias_attr)
 
     predict = paddle.sum(result)
     return predict

python/paddle/fluid/tests/unittests/static_model_parallel_by_row.py

@@ -43,29 +43,39 @@
 #fluid.default_main_program().random_seed = 1
 
 
+def get_param_attr(weight, bias):
+    weight_attr = paddle.ParamAttr(
+        initializer=fluid.initializer.NumpyArrayInitializer(weight))
+    bias_attr = paddle.ParamAttr(
+        initializer=fluid.initializer.NumpyArrayInitializer(bias))
+    return weight_attr, bias_attr
+
+
 def create_model(data, rank):
     np.random.seed(2021)
     np_weight = np.random.uniform(-1, 1, size=(IN_SIZE, OUT_SIZE)).astype(DTYPE)
+    np_bias = np.random.uniform(-1, 1, size=(OUT_SIZE, )).astype(DTYPE)
     if rank is not None:
         start_row = 0 if rank == 0 else IN_SIZE // 2
         np_weight_part = np_weight[start_row:start_row + IN_SIZE // 2, :]
+
+        weight_attr, bias_attr = get_param_attr(np_weight_part, np_bias)
         result = paddle.distributed.split(
             data,
             size=(IN_SIZE, OUT_SIZE),
             operation='linear',
             axis=0,
             num_partitions=MODEL_PARALLEL_SIZE,
-            weight_attr=paddle.ParamAttr(
-                initializer=fluid.initializer.NumpyArrayInitializer(
-                    np_weight_part)),
-            bias_attr=False, )
+            weight_attr=weight_attr,
+            bias_attr=bias_attr)
     else:
+        weight_attr, bias_attr = get_param_attr(np_weight, np_bias)
         result = fluid.layers.fc(
             data,
             size=OUT_SIZE,
             param_attr=paddle.ParamAttr(
                 initializer=fluid.initializer.NumpyArrayInitializer(np_weight)),
-            bias_attr=False, )
+            bias_attr=bias_attr)
 
     predict = paddle.sum(result)
     return predict