Merge remote-tracking branch 'upstream/develop' into Support_HF_ops

docs/inference_model_convertor/op_list.md

@@ -114,7 +114,7 @@ Aten:
 | 117 | aten::bitwise\_not | 118 | aten::bitwise\_xor | 119 | aten::bitwise\_and | 120 | aten::silu |
 | 121 | aten::repeat\_interleave | 122 | aten::maxpool1d | 123 | aten::frobenius\_norm | 124 | aten::format |
 | 125 | aten::complex | 126 | aten::real | 127 | aten::imag | 128 | aten::fft\_rfftn |
-| 129 | aten::fft\_irfftn | 130 | aten::hardsigmoid | 131 | aten::hardswish |  |  |
+| 129 | aten::fft\_irfftn | 130 | aten::hardsigmoid | 131 | aten::hardswish | 132 | aten::linear |
 
 
 Prim:

x2paddle/op_mapper/pytorch2paddle/aten.py

@@ -3238,6 +3238,55 @@ def aten_len(mapper, graph, node):
     return current_inputs, current_outputs
 
 
+def aten_linear(mapper, graph, node):
+    """
+    TorchScript Code:
+        %x.6 : Float(1, 128, strides=[128, 1]) = aten::linear(%input.305, %weight.629, %bias.317)
+        Parameter meaning:
+        %x.6 (Tensor): output
+        %input.305 (Tensor): input tensor
+        %weight.629 (Tensor): weight tensor
+        %bias.317 (Tensor): bias tensor
+    """
+    scope_name = mapper.normalize_scope_name(node)
+    output_name = mapper._get_outputs_name(node)[0]
+    layer_outputs = [output_name]
+    layer_inputs = {}
+    layer_attrs = {}
+    inputs_name, inputs_node = mapper._get_inputs_name(node)
+    # outputs list
+    current_outputs = [output_name]
+    # inputs list
+    mapper._check_input(graph, inputs_node[0], inputs_name[0], current_outputs,
+                        scope_name)
+    layer_inputs["x"] = inputs_name[0]
+    # transpose weight
+    mapper._check_input(graph, inputs_node[1], inputs_name[1], current_outputs,
+                        scope_name)
+    layer_attrs_transpose = {}
+    layer_attrs_transpose["perm"] = [1, 0]
+    graph.add_layer(
+        "paddle.transpose",
+        inputs={"x": inputs_name[1]},
+        outputs=[inputs_name[1] + "_transpose"],
+        scope_name=scope_name,
+        **layer_attrs_transpose)
+    layer_inputs["weight"] = inputs_name[1] + "_transpose"
+    if len(inputs_name) == 3:
+        mapper._check_input(graph, inputs_node[2], inputs_name[2],
+                            current_outputs, scope_name)
+        layer_inputs["bias"] = inputs_name[2]
+    current_inputs = list(layer_inputs.values())
+
+    graph.add_layer(
+        "paddle.nn.functional.linear",
+        inputs=layer_inputs,
+        outputs=layer_outputs,
+        scope_name=scope_name,
+        **layer_attrs)
+    return current_inputs, current_outputs
+
+
 def aten_log(mapper, graph, node):
     """ 构构造log的PaddleLayer。
     TorchScript示例:

x2paddle/optimizer/fusion/trace_fc_fuser.py

@@ -113,10 +113,13 @@ class TraceFcFuser(FuseBase):
         attrs["out_features"] = parameters[weight_name].shape[0]
         linear_name = "linear{}".format(self.linear_index)
         self.linear_index += 1
-        parameters["{}.weight".format(linear_name)] = parameters[
-            weight_name].transpose((1, 0))
-        parameters["{}.bias".format(linear_name)] = np.squeeze(parameters[
-            bias_name])
+        weight_numpy = parameters[weight_name]
+        parameters["{}.weight".format(linear_name)] = weight_numpy.transpose(
+            (1, 0))
+        self.rm_params.add(weight_name)
+        bias_numpy = parameters[bias_name]
+        parameters["{}.bias".format(linear_name)] = np.squeeze(bias_numpy)
+        self.rm_params.add(bias_name)
         new_layer = PaddleLayer(
             layers_id[0],
             "paddle.nn.Linear",

x2paddle/optimizer/pattern_matcher.py

@@ -325,6 +325,7 @@ class FuseBase(object):
     def __init__(self):
         self.pattern = PaddleGraph()
         self.patterns = list()
+        self.rm_params = set()
 
     def operate(self, graph, match_kind="topo"):
         parameters = graph.parameters
@@ -335,6 +336,8 @@ class FuseBase(object):
             subgraph = get_subgraph("", first_layer_id, graph)
             self.insert_new_layer(subgraph, parameters, match)
         self.delete_match(graph)
+        for param_name in self.rm_params:
+            parameters.pop(param_name)
         graph.build()
 
     def perform_pattern_matcher(self, graph, match_kind="topo"):