refine and add seqconv elementwiseadd relu op test

paddle/fluid/operators/fusion_seqconv_eltadd_relu_op.cc

@@ -40,6 +40,7 @@ void FusionSeqConvEltAddReluOp::InferShape(
 
   auto x_dims = ctx->GetInputDim("X");
   auto w_dims = ctx->GetInputDim("Filter");
+  int context_length = ctx->Attrs().Get<int>("contextLength");
   PADDLE_ENFORCE(
       ctx->Attrs().Get<int>("contextStride") == 1,
       "Currently, FusionSeqConvEltAddReluOp only supports contextStride=1.");
@@ -47,10 +48,11 @@ void FusionSeqConvEltAddReluOp::InferShape(
                  "Input(X, Filter) should be 2-D tensor.");
   PADDLE_ENFORCE(x_dims.size() == 2 && w_dims.size() == 2,
                  "Input(X, Filter) should be 2-D tensor.");
-  PADDLE_ENFORCE(
-      w_dims[0] == ctx->Attrs().Get<int>("contextLength") * x_dims[1],
-      "Filter's height should be context_length * "
-      "input_hidden_size .");
+  PADDLE_ENFORCE(w_dims[0] == context_length * x_dims[1],
+                 "Filter's height should be context_length * "
+                 "input_hidden_size .");
+  PADDLE_ENFORCE_GT(context_length + ctx->Attrs().Get<int>("contextStart"), 0,
+                    "contextStart size should be smaller than contextLength.");
 
   ctx->SetOutputDim("Out", {x_dims[0], w_dims[1]});
   ctx->SetOutputDim("ColMat", {x_dims[0], w_dims[0]});
@@ -156,9 +158,8 @@ class FusionSeqConvEltAddReluKernel : public framework::OpKernel<T> {
       T* dst_data = col_data + st * col_mat_w;
       int seq_len = ed - st;
       if (seq_len > up_pad + down_pad) {
-        // zero all up_pad
+        // zero all up_pad and fill data
         std::memset(dst_data, 0, up_pad * col_mat_w_sz);
-        // fill up_pad data
         dst_data = dst_data + up_pad * src_mat_w;
         int copy_size = col_mat_w_sz - up_pad * src_mat_w_sz;
         for (int j = 0; j < up_pad; ++j) {
@@ -173,9 +174,8 @@ class FusionSeqConvEltAddReluKernel : public framework::OpKernel<T> {
           dst_data += col_mat_w;
           src_data += src_mat_w;
         }
-        // zero all down_pad
+        // zero all down_pad and fill data
         std::memset(dst_data, 0, down_pad * col_mat_w_sz);
-        // fill down_pad data
         copy_size -= src_mat_w_sz;
         for (int j = 0; j < down_pad; ++j) {
           std::memcpy(dst_data, src_data, copy_size);
@@ -186,27 +186,29 @@ class FusionSeqConvEltAddReluKernel : public framework::OpKernel<T> {
       } else {
         PADDLE_ENFORCE_GE(context_length, up_pad + down_pad + 1);
         std::memset(dst_data, 0, seq_len * col_mat_w_sz);
+        dst_data = dst_data + up_pad * src_mat_w;
         int zero_sz = up_pad * src_mat_w_sz;
-        int seq_len_size = seq_len * src_mat_w_sz;
+        int cur_src_sz = seq_len * src_mat_w_sz;
         for (int j = 0; j < std::min(up_pad, seq_len); ++j) {
-          int copy_size = std::min(seq_len_size, col_mat_w_sz - zero_sz);
-          std::memcpy(dst_data + zero_sz / sizeof(T), src_data, copy_size);
-          dst_data += col_mat_w;
+          int copy_size = std::min(cur_src_sz, col_mat_w_sz - zero_sz);
+          std::memcpy(dst_data, src_data, copy_size);
+          dst_data += (col_mat_w - src_mat_w);
           zero_sz -= src_mat_w_sz;
         }
+        // from bottom
+        dst_data = col_data + ed * col_mat_w;
+        src_data = x_data + st * src_mat_w;
         zero_sz = down_pad * src_mat_w_sz;
-        dst_data = col_data + (ed - 1) * col_mat_w;
-        src_data = x_data + (ed - up_pad - 1) * src_mat_w;
-        for (int j = 0; j < std::min(0, seq_len - up_pad); ++j) {
-          int copy_size = std::min(seq_len_size, col_mat_w_sz - zero_sz);
-          std::memcpy(dst_data, src_data, copy_size);
+        for (int j = 1; j <= std::min(down_pad, seq_len); ++j) {
+          int copy_size = std::min(cur_src_sz, col_mat_w_sz - zero_sz);
+          std::memcpy(dst_data - (zero_sz + copy_size) / sizeof(T),
+                      src_data + std::max(seq_len - j - up_pad, 0) * src_mat_w,
+                      copy_size);
           dst_data -= col_mat_w;
-          src_data += src_mat_w;
           zero_sz -= src_mat_w_sz;
         }
       }
     }
-
     auto& dev_ctx = ctx.template device_context<DeviceContext>();
     auto blas = math::GetBlas<DeviceContext, T>(dev_ctx);
     math::FCCompute<DeviceContext, T>(blas, x_dims[0], w_dims[1], w_dims[0],

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

+#   Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from __future__ import print_function
+
+import unittest
+import numpy as np
+import random
+from op_test import OpTest
+from test_seq_conv import seqconv
+
+
+class TestSeqConvEltAddRelu(OpTest):
+    def set_conf(self):
+        pass
+
+    def setUp(self):
+        self.op_type = 'fusion_seqconv_eltadd_relu'
+        self.lod = [[6, 4]]
+        self.in_fea_size = 16
+        self.out_fea_size = 8
+        self.context_length = 4
+        self.context_stride = 1
+        self.context_start = 0
+        self.set_conf()
+
+        assert self.context_stride == 1
+
+        T = sum(self.lod[0])
+        x = np.random.uniform(-1, 1, [T, self.in_fea_size]).astype('float32')
+        w = np.random.uniform(
+            -1, 1, [self.in_fea_size * self.context_length,
+                    self.out_fea_size]).astype('float32')
+        b = np.random.uniform(-2, 1, [1, self.out_fea_size]).astype('float32')
+        out = seqconv(x, self.lod, w, self.context_length, self.context_start)
+        out = np.maximum(out + b, 0)
+
+        self.inputs = {'X': (x, self.lod), 'Filter': w, 'Bias': b}
+        self.attrs = {
+            'contextStart': self.context_start,
+            'contextLength': self.context_length,
+            'contextStride': self.context_stride
+        }
+        self.outputs = {'Out': out}
+
+    def test_check_output(self):
+        self.check_output()
+
+
+class TestSeqConvEltAddReluBS1(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[10]]
+
+
+class TestSeqConvEltAddReluBS1Case2(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[2]]
+
+
+class TestSeqConvEltAddReluCase1(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[3, 5, 1, 6]]
+        self.context_length = 3
+        self.context_start = -2
+
+
+class TestSeqConvEltAddReluCase2(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[10, 1, 2, 4, 1, 5, 6]]
+        self.in_fea_size = 2
+        self.context_length = 4
+        self.context_start = -1
+
+
+class TestSeqConvEltAddReluCase3(TestSeqConvEltAddRelu):
+    def set_conf(self):
+        self.lod = [[10, 1, 2, 4, 1, 5, 6]]
+        self.context_length = 5
+        self.context_start = -4
+
+
+if __name__ == '__main__':
+    unittest.main()

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

@@ -20,6 +20,53 @@ import random
 from op_test import OpTest
 
 
+def seqconv(x,
+            lod,
+            filter,
+            context_length,
+            context_start,
+            padding_trainable=False,
+            padding_data=None):
+    [T, M] = x.shape
+    col = np.zeros((T, context_length * M)).astype('float32')
+    offset = [0]
+    for seq_len in lod[0]:
+        offset.append(offset[-1] + seq_len)
+    begin_pad = np.max([0, -context_start])
+    for i in range(len(offset) - 1):
+        for j in range(context_length):
+            in_begin = offset[i] + context_start + j
+            in_end = offset[i + 1] + context_start + j
+            out_begin = offset[i]
+            out_end = offset[i + 1]
+            if in_begin < offset[i]:
+                pad_size = np.min(
+                    [offset[i] - in_begin, offset[i + 1] - offset[i]])
+                if padding_trainable:
+                    sub_w = padding_data[j:j + pad_size, :]
+                    col[offset[i]:offset[i] + pad_size, j * M:(j + 1) *
+                        M] = sub_w
+                out_begin = offset[i] + pad_size
+                in_begin = offset[i]
+
+            if in_end > offset[i + 1]:
+                pad_size = np.min(
+                    [in_end - offset[i + 1], offset[i + 1] - offset[i]])
+                if padding_trainable:
+                    sub_w = padding_data[begin_pad + context_start + j -
+                                         pad_size:begin_pad + context_start +
+                                         j, :]
+                    col[offset[i + 1] - pad_size:offset[i + 1], j * M:(j + 1) *
+                        M] = sub_w
+                in_end = offset[i + 1]
+                out_end = offset[i + 1] - pad_size
+            if in_end <= in_begin:
+                continue
+            in_sub = x[in_begin:in_end, :]
+            col[out_begin:out_end, j * M:(j + 1) * M] += in_sub
+    return np.dot(col, filter)
+
+
 class TestSeqProject(OpTest):
     def setUp(self):
         self.init_test_case()
@@ -66,57 +113,9 @@ class TestSeqProject(OpTest):
             'paddingTrainable': self.padding_trainable,
             'contextStride': self.context_stride
         }
-        out = np.zeros(
-            (self.input_size[0], self.output_represention)).astype('float32')
+        out = seqconv(x, self.lod, w, self.context_length, self.context_start,
+                      self.padding_trainable, self.pad_data)
         self.outputs = {'Out': out}
-        self.compute()
-
-    def compute(self):
-        x, lod = self.inputs['X']
-        filter = self.inputs['Filter']
-        pading_data = self.pad_data
-        out = np.zeros((self.input_size[0], self.context_length *
-                        self.input_size[1])).astype('float32')
-        offset = [0]
-        for seq_len in lod[0]:
-            offset.append(offset[-1] + seq_len)
-        begin_pad = np.max([0, -self.context_start])
-
-        for i in range(len(offset) - 1):
-            for j in range(self.context_length):
-                in_begin = offset[i] + self.context_start + j
-                in_end = offset[i + 1] + self.context_start + j
-                out_begin = offset[i]
-                out_end = offset[i + 1]
-                if in_begin < offset[i]:
-                    pad_size = np.min(
-                        [offset[i] - in_begin, offset[i + 1] - offset[i]])
-                    if self.padding_trainable:
-                        sub_w = pading_data[j:j + pad_size, :]
-                        out[offset[i]:offset[i] + pad_size, j * self.input_size[
-                            1]:(j + 1) * self.input_size[1]] = sub_w
-                    out_begin = offset[i] + pad_size
-                    in_begin = offset[i]
-
-                if in_end > offset[i + 1]:
-                    pad_size = np.min(
-                        [in_end - offset[i + 1], offset[i + 1] - offset[i]])
-                    if self.padding_trainable:
-                        sub_w = pading_data[begin_pad + self.context_start + j -
-                                            pad_size:begin_pad +
-                                            self.context_start + j, :]
-                        out[offset[i + 1] - pad_size:offset[i + 1], j * self.
-                            input_size[1]:(j + 1) * self.input_size[1]] = sub_w
-                    in_end = offset[i + 1]
-                    out_end = offset[i + 1] - pad_size
-                if in_end <= in_begin:
-                    continue
-
-                in_sub = x[in_begin:in_end, :]
-                out[out_begin:out_end, j * self.input_size[1]:(j + 1) *
-                    self.input_size[1]] += in_sub
-
-        np.dot(out, filter, out=self.outputs['Out'])
 
     def test_check_output(self):
         self.check_output()