fix macunittest (#13434)

paddle/fluid/operators/math/cpu_lstm_compute.cc

 /* 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.
@@ -13,76 +10,9 @@ See the License for the specific language governing permissions and
 limitations under the License. */
 
 #include "paddle/fluid/operators/math/cpu_lstm_compute.h"
-#include "paddle/fluid/operators/math/cpu_vec.h"
-#include "paddle/fluid/platform/cpu_info.h"
-#ifdef __AVX__
-#include <immintrin.h>
-#endif
 
 namespace paddle {
 namespace operators {
-namespace math {
-
-// TODO(TJ): ugly workaround, clean me
-template <typename T>
-void lstm_compute_ctht(T* gates, const T* ct_1, T* ct, T* ht) {
-  // gates: W_ch, W_ih, W_fh, W_oh
-  vec_sigmoid<T, platform::jit::avx>(24, gates + 8, gates + 8);
-  vec_tanh<T, platform::jit::avx>(8, gates, gates);
-  const T *i = gates + 8, *f = gates + 16, *o = gates + 24;
-  const T min = SIGMOID_THRESHOLD_MIN;
-  const T max = SIGMOID_THRESHOLD_MAX;
-  for (int d = 0; d < 8; ++d) {
-    // C_t = C_t-1 * fgated + cand_gated * igated
-    ct[d] = ct_1[d] * f[d] + gates[d] * i[d];
-    // H_t = act_cell(C_t) * ogated
-    T tmp = ct[d] * 2;
-    tmp = static_cast<T>(0) - ((tmp < min) ? min : ((tmp > max) ? max : tmp));
-    vec_exp<T>(1, &tmp, &tmp);
-    tmp = static_cast<T>(2) / (static_cast<T>(1) + tmp) - static_cast<T>(1);
-    ht[d] = tmp * o[d];
-  }
-}
-
-#ifdef __AVX__
-namespace detail {
-namespace forward {
-namespace avx {
-__m256 Sigmoid(const __m256 a);
-__m256 Tanh(const __m256 a);
-}  // namespace avx
-}  // namespace forward
-}  // namespace detail
-
-template <>
-void lstm_compute_ctht<float>(float* gates, const float* ct_1, float* ct,
-                              float* ht) {
-  namespace act = detail::forward::avx;
-  // gates: W_ch, W_ih, W_fh, W_oh
-  __m256 c, i, f, o;
-  c = _mm256_loadu_ps(gates);
-  i = _mm256_loadu_ps(gates + 8);
-  f = _mm256_loadu_ps(gates + 16);
-  o = _mm256_loadu_ps(gates + 24);
-
-  /* C_t = C_t-1 * fgated + cand_gated * igated*/
-  c = _mm256_mul_ps(act::Tanh(c), act::Sigmoid(i));
-  i = _mm256_loadu_ps(ct_1);
-  f = _mm256_mul_ps(i, act::Sigmoid(f));
-  f = _mm256_add_ps(c, f);
-  _mm256_storeu_ps(ct, f);
-
-  /* H_t = act_cell(C_t) * ogated */
-  o = _mm256_mul_ps(act::Tanh(f), act::Sigmoid(o));
-  _mm256_storeu_ps(ht, o);
-}
-#endif
-
-template void lstm_compute_ctht<float>(float* gates, const float* ct_1,
-                                       float* ct, float* ht);
-template void lstm_compute_ctht<double>(double* gates, const double* ct_1,
-                                        double* ct, double* ht);
-
-}  // namespace math
+namespace math {}  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/math/cpu_lstm_compute.h

 /* 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.
@@ -14,6 +11,11 @@ limitations under the License. */
 
 #pragma once
 #include <string>
+#include "paddle/fluid/operators/math/cpu_vec.h"
+#include "paddle/fluid/platform/cpu_info.h"
+#ifdef __AVX__
+#include <immintrin.h>
+#endif
 
 namespace paddle {
 namespace operators {
@@ -21,7 +23,58 @@ namespace math {
 
 // TODO(TJ): ugly workaround, clean me
 template <typename T>
-void lstm_compute_ctht(T* gates, const T* ct_1, T* ct, T* ht);
+void lstm_compute_ctht(T* gates, const T* ct_1, T* ct, T* ht) {
+  // gates: W_ch, W_ih, W_fh, W_oh
+  vec_sigmoid<T, platform::jit::avx>(24, gates + 8, gates + 8);
+  vec_tanh<T, platform::jit::avx>(8, gates, gates);
+  const T *i = gates + 8, *f = gates + 16, *o = gates + 24;
+  const T min = SIGMOID_THRESHOLD_MIN;
+  const T max = SIGMOID_THRESHOLD_MAX;
+  for (int d = 0; d < 8; ++d) {
+    // C_t = C_t-1 * fgated + cand_gated * igated
+    ct[d] = ct_1[d] * f[d] + gates[d] * i[d];
+    // H_t = act_cell(C_t) * ogated
+    T tmp = ct[d] * 2;
+    tmp = static_cast<T>(0) - ((tmp < min) ? min : ((tmp > max) ? max : tmp));
+    vec_exp<T>(1, &tmp, &tmp);
+    tmp = static_cast<T>(2) / (static_cast<T>(1) + tmp) - static_cast<T>(1);
+    ht[d] = tmp * o[d];
+  }
+}
+
+#ifdef __AVX__
+namespace detail {
+namespace forward {
+namespace avx {
+__m256 Sigmoid(const __m256 a);
+__m256 Tanh(const __m256 a);
+}  // namespace avx
+}  // namespace forward
+}  // namespace detail
+
+template <>
+void lstm_compute_ctht<float>(float* gates, const float* ct_1, float* ct,
+                              float* ht) {
+  namespace act = detail::forward::avx;
+  // gates: W_ch, W_ih, W_fh, W_oh
+  __m256 c, i, f, o;
+  c = _mm256_loadu_ps(gates);
+  i = _mm256_loadu_ps(gates + 8);
+  f = _mm256_loadu_ps(gates + 16);
+  o = _mm256_loadu_ps(gates + 24);
+
+  /* C_t = C_t-1 * fgated + cand_gated * igated*/
+  c = _mm256_mul_ps(act::Tanh(c), act::Sigmoid(i));
+  i = _mm256_loadu_ps(ct_1);
+  f = _mm256_mul_ps(i, act::Sigmoid(f));
+  f = _mm256_add_ps(c, f);
+  _mm256_storeu_ps(ct, f);
+
+  /* H_t = act_cell(C_t) * ogated */
+  o = _mm256_mul_ps(act::Tanh(f), act::Sigmoid(o));
+  _mm256_storeu_ps(ht, o);
+}
+#endif
 
 }  // namespace math
 }  // namespace operators

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

@@ -109,15 +109,20 @@ def get_transpiler(trainer_id, main_program, pserver_endpoints, trainers):
     return t
 
 
+from paddle.fluid.transpiler.details import op_to_code
+
+
 def operator_equal(a, b):
+    if op_to_code(a) != op_to_code(b):
+        raise ValueError("In operator_equal not equal\n")
+
     for k, v in six.iteritems(a.__dict__):
         if isinstance(v, fluid.framework.Program) or \
                 isinstance(v, fluid.framework.Block):
             continue
 
         elif isinstance(v, core.OpDesc):
-            if v.serialize_to_string() != b.__dict__[k].serialize_to_string():
-                raise ValueError("In operator_equal not equal:{0}\n".format(k))
+            continue
 
         elif isinstance(v, collections.OrderedDict):
             v0 = sorted(list(six.iteritems(v)), key=lambda x: x[0])

python/paddle/fluid/transpiler/details/program_utils.py

@@ -113,27 +113,32 @@ def op_to_code(op):
             inputs_str += ", "
     inputs_str += "}"
 
+    attr_names = sorted(op.attr_names)
     attrs_str = ""
-    for i in range(0, len(op.attr_names)):
-        name = op.attr_names[i]
+    for i in range(0, len(attr_names)):
+        name = attr_names[i]
 
         attr_type = op.desc.attr_type(name)
         if attr_type == core.AttrType.BLOCK:
             a = "{name} = block[{value}]".format(
                 name=name, type=attr_type, value=op.block_attr_id(name))
             attrs_str += a
+            if i != len(attr_names) - 1:
+                attrs_str += ", "
             continue
 
         if attr_type == core.AttrType.BLOCKS:
             a = "{name} = blocks{value}".format(
                 name=name, type=attr_type, value=op.blocks_attr_ids(name))
             attrs_str += a
+            if i != len(attr_names) - 1:
+                attrs_str += ", "
             continue
 
         a = "{name} = {value}".format(
             name=name, type=attr_type, value=op.desc.attr(name))
         attrs_str += a
-        if i != len(op.attr_names) - 1:
+        if i != len(attr_names) - 1:
             attrs_str += ", "
 
     if outputs_str != "{}":