[Eager] Fix linspace error in amp (#46088) (#46206)

* fix linspace error in amp

* fix log

* fix amp error

paddle/fluid/eager/auto_code_generator/generator/codegen_utils.py

@@ -174,7 +174,7 @@ def GetDygraphLogName(string):
         arr = filter(None, text.split('_'))
         res = ''
         for i in arr:
-            res = res + i[0].upper() + i[1:]
+            res = res + i.lower()
         return res
 
     string = str2Hump(string)

paddle/fluid/eager/auto_code_generator/generator/eager_gen.py

@@ -166,8 +166,11 @@ paddle::small_vector<std::vector<paddle::experimental::Tensor>, egr::kSlotSmallV
 {}
   // Inplace Strategy
 {}
-  // Call grad_api function
+
   VLOG(5) << \"Running C++ API: \" << \"{}\";
+  // Before log info
+{}
+  // Call grad_api function
 {}
   // Check NaN and Inf id needed
 {}
@@ -195,8 +198,11 @@ FORWARD_FUNCTION_TEMPLATE = \
 {}
   // Get Input AutoGradMeta
 {}
-  // Forward API Call
+
   VLOG(5) << \"Running C++ API: \" << \"{}\";
+ // Before log info
+{}
+ // Forward API Call
 {}
   // Check NaN and Inf if needed
 {}
@@ -220,7 +226,7 @@ FORWARD_FUNCTION_TEMPLATE = \
 }}
 """
 
-LOG_PRINT_TEMPLATE = \
+AFTER_LOG_PRINT_TEMPLATE = \
 """
   if(VLOG_IS_ON(4)){{
       const char* INPUT_PRINT_TEMPLATE = \"{{ Input: [%s],  Output: [%s] }} \";
@@ -229,6 +235,14 @@ LOG_PRINT_TEMPLATE = \
   }}
 """
 
+BEFORE_LOG_PRINT_TEMPLATE = \
+"""
+  if(VLOG_IS_ON(3)){{
+      const char* INPUT_PRINT_TEMPLATE = \"{{ Input: [%s]}} \";
+      {}
+      VLOG(3) << paddle::string::Sprintf(INPUT_PRINT_TEMPLATE, input_str);
+  }}
+"""
 
 FORWARD_ONLY_FUNCTION_TEMPLATE = \
 """
@@ -240,8 +254,10 @@ FORWARD_ONLY_FUNCTION_TEMPLATE = \
 {}
   // Layout autotune
 {}
-  // Forward API Call
   VLOG(5) << \"Running C++ API: \" << \"{}\";
+  // Before log info
+{}
+  // Forward API Call
 {}
   // Get Outputs
 {}
@@ -1286,6 +1302,7 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
             returns_str = f"{returns_type_str}{{{returns_str}}}"
 
         # Node Creation Pre-Processing
+        inputs_names = []
         if not self.is_forward_only:
             # 1. Get Input AutoGradMeta
             inputs_autograd_meta_list = []
@@ -1400,12 +1417,14 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
             var_str += f"\n{indent}  const char* TENSOR_{name.upper()}_TEMPLATE = \"({name}, [%s]), \";"
             var_str += f"\n{indent}  std::string input_{name}_str = paddle::string::Sprintf(TENSOR_{name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({name}));"
             var_str += f"\n{indent}  input_str += input_{name}_str; "
+
+        before_log_str = BEFORE_LOG_PRINT_TEMPLATE.format(var_str)
         for name, (ttype, pos) in forward_outputs_position_map.items():
             var_str += f"\n{indent}  const char* TENSOR_{name.upper()}_TEMPLATE = \"({name}, [%s]), \";"
             var_str += f"\n{indent}  std::string output_{name}_str = paddle::string::Sprintf(TENSOR_{name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({name}));"
             var_str += f"\n{indent}  output_str += output_{name}_str; "
 
-        log_str = LOG_PRINT_TEMPLATE.format(var_str)
+        log_str = AFTER_LOG_PRINT_TEMPLATE.format(var_str)
 
         # Generate forward_definition_str and forward_declaration_str
         if self.is_forward_only:
@@ -1413,23 +1432,21 @@ class DygraphForwardFunctionGenerator(DygraphFunctionGeneratorBase):
                 amp_logic_str = "\n VLOG(7) << \" No AMP for {} because it has no input. \"; ".format(
                     forward_ad_function_name)
             self.forward_definition_str += FORWARD_ONLY_FUNCTION_TEMPLATE.format(
-                returns_type_str,
-                forward_ad_function_name, inputs_args_definition_str,
-                GetDygraphLogName(forward_api_name), dygraph_event_str,
+                returns_type_str, forward_ad_function_name,
+                inputs_args_definition_str, forward_api_name, dygraph_event_str,
                 amp_logic_str, layout_logic_str, forward_api_name,
-                forward_call_str, get_outputs_str, forward_ad_function_name,
-                log_str, returns_str)
+                before_log_str, forward_call_str, get_outputs_str,
+                forward_api_name, log_str, returns_str)
         else:
             self.forward_definition_str += FORWARD_FUNCTION_TEMPLATE.format(
-                returns_type_str,
-                forward_ad_function_name, inputs_args_definition_str,
-                GetDygraphLogName(forward_api_name), dygraph_event_str,
+                returns_type_str, forward_ad_function_name,
+                inputs_args_definition_str, forward_api_name, dygraph_event_str,
                 amp_logic_str, layout_logic_str, inputs_autograd_meta_str,
-                forward_api_name, forward_call_str, check_nan_inf_str,
-                get_outputs_str, outputs_autograd_meta_str,
+                forward_api_name, before_log_str, forward_call_str,
+                check_nan_inf_str, get_outputs_str, outputs_autograd_meta_str,
                 compute_require_grad_args_str, check_inplace_str,
-                bump_inplace_version_str, node_creation_str,
-                forward_ad_function_name, log_str, returns_str)
+                bump_inplace_version_str, node_creation_str, forward_api_name,
+                log_str, returns_str)
 
         self.forward_declaration_str += f"{returns_type_str} {forward_ad_function_name}({inputs_args_declaration_str});\n"
 
@@ -1924,6 +1941,8 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
             var_str += f"\n{indent}  std::string input_{new_name}_str = paddle::string::Sprintf(TENSOR_{new_name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({new_name}));"
             var_str += f"\n{indent}  input_str += input_{new_name}_str; "
 
+        before_log_str = BEFORE_LOG_PRINT_TEMPLATE.format(var_str)
+
         for name, (ttype, fwd_position,
                    grad_api_position) in backward_grad_outputs_map.items():
             new_name = self.TransformToNextGradName(name)
@@ -1931,16 +1950,16 @@ class DygraphNodeGenerator(DygraphFunctionGeneratorBase):
             var_str += f"\n{indent}  std::string output_{new_name}_str = paddle::string::Sprintf(TENSOR_{new_name.upper()}_TEMPLATE, egr::EagerUtils::TensorStr({new_name}));"
             var_str += f"\n{indent}  output_str += output_{new_name}_str; "
 
-        log_str = LOG_PRINT_TEMPLATE.format(var_str)
+        log_str = AFTER_LOG_PRINT_TEMPLATE.format(var_str)
 
         self.node_definition_str = GRAD_FUNCTION_TEMPLATE.format(
-            grad_node_name, GetDygraphLogName(self.backward_api_name),
-            fill_zero_str, get_grad_in_args_str, grad_function_prepare_str,
+            grad_node_name, self.backward_api_name, fill_zero_str,
+            get_grad_in_args_str, grad_function_prepare_str,
             compute_require_next_grad_str, inplace_check_str,
-            inplace_for_grad_outs_str, self.backward_api_name,
+            inplace_for_grad_outs_str, self.backward_api_name, before_log_str,
             grad_function_call_str, check_nan_inf_str,
             outputs_autograd_meta_str, next_grad_node_creation_str,
-            GetDygraphLogName(self.backward_api_name), log_str, returns_str)
+            self.backward_api_name, log_str, returns_str)
 
     def run(self):
         super().run()

paddle/fluid/eager/backward.cc

@@ -71,7 +71,6 @@ std::unordered_map<GradNodeBase*, int> getInDegreeMap(
 
 // Enforce GradNode has TensorWrappers as Input
 void EnforceGradNodeHasInput(GradNodeBase* node) {
-  VLOG(6) << "Running in EnforceGradNodeHasInput";
   PADDLE_ENFORCE_NE(
       node->IsTensorWrappersCleared(),
       true,

paddle/fluid/eager/eager_amp_auto_cast.h

@@ -87,7 +87,7 @@ inline paddle::experimental::Tensor EagerAmpAutoCast(
     const std::string& op_name,
     bool trace_backward = true) {
   VLOG(6) << "AMP AmpAutoCasts:"
-          << " input(" << input_name << ") dst_dtype("
+          << " input(" << egr::EagerUtils::TensorStr(input) << " to dst_dtype("
           << paddle::framework::DataType2String(dst_dtype) << ").";
   if (dst_dtype == paddle::experimental::DataType::FLOAT16) {
     if (op_name == "run_program") {
@@ -107,6 +107,7 @@ inline paddle::experimental::Tensor EagerAmpAutoCast(
     }
   }
   if (NeedCast(input, dst_dtype)) {
+    VLOG(6) << "Input : " << input.impl() << "NeedCast";
     return Cast(input, dst_dtype, trace_backward);
   }
   return input;

paddle/fluid/eager/utils.h

@@ -257,8 +257,7 @@ class EagerUtils {
     } else {
       tensor_name_str = t.name();
     }
-    const char* TENSOR_INFO_TEMPLATE =
-        "{ Type: [ \"%s\" ], Dtype:[ \"%s\" ], Place:[ \"%s\" ] }";
+    const char* TENSOR_INFO_TEMPLATE = "Type: %s, Dtype: %s, Place: %s";
     std::string tensor_info_str = "";
     if (t.defined()) {
       if (t.initialized()) {
@@ -277,13 +276,13 @@ class EagerUtils {
     }
     if (VLOG_IS_ON(6)) {
       const char* TENSOR_PRINT_TEMPLATE =
-          "{ Name:[ \"%s\" ], Initialized: [ \"%d\" ], Ptr: [ \"%d\" ] "
-          "TensorInfo: [ \"%s\" ], ADInfo:[ \"%s\" ] }";
+          "{Name: %s, Initialized: %d, Ptr: %d "
+          "TensorInfo: [ %s ], ADInfo:[ %s ]}";
       auto* ad_meta = nullable_autograd_meta(t);
-      if (!ad_meta && !(ad_meta->WeakGrad().lock().get())) {
+      if (ad_meta && (ad_meta->WeakGrad().lock().get())) {
         std::string ad_info_str = "";
         const char* AD_INFO_TEMPLATE =
-            "{ Grad: [ \"%s\" ],  GradNode: [ %s ], StopGradient: [ %d ] }";
+            "Grad: [ %s ],  GradNode: [ %s ], StopGradient: [ %d ]";
         ad_info_str += paddle::string::Sprintf(AD_INFO_TEMPLATE,
                                                TensorStr(ad_meta->Grad()),
                                                GradNodeStr(t),
@@ -304,8 +303,8 @@ class EagerUtils {
       }
     } else if (VLOG_IS_ON(5)) {
       const char* TENSOR_PRINT_TEMPLATE =
-          "{ Name:[ \"%s\" ], Initialized: [ \"%d\" ], Ptr: [ \"%d\" ] "
-          "TensorInfo: [ \"%s\" ] }";
+          "{Name: %s, Initialized: %d , Ptr: %d "
+          "TensorInfo: [ %s ]}";
       return paddle::string::Sprintf(TENSOR_PRINT_TEMPLATE,
                                      tensor_name_str,
                                      t.initialized(),
@@ -313,7 +312,7 @@ class EagerUtils {
                                      tensor_info_str);
     } else if (VLOG_IS_ON(4)) {
       const char* TENSOR_PRINT_TEMPLATE =
-          "{ Name:[ \"%s\" ], Initialized: [ \"%d\" ], Ptr: [ \"%d\" ] }";
+          "{ Name: %s, Initialized: %d, Ptr: %d }";
       return paddle::string::Sprintf(
           TENSOR_PRINT_TEMPLATE, tensor_name_str, t.initialized(), t.impl());
     } else {
@@ -324,10 +323,10 @@ class EagerUtils {
   static const std::string GradNodeStr(const egr::GradNodeBase& node) {
     if (VLOG_IS_ON(6)) {
       const char* GRAD_NODE_TEMPLATE =
-          " { BackwardOutMeta: [ %s ], BackwardInMeta: [ %s ] }";
+          "BackwardOutMeta: [ %s ], BackwardInMeta: [ %s ]";
       const char* GRAD_SLOT_META_TEMPLATE = " {SlotSize: [%d]: %s} ";
       const char* SLOT_INFO_TEMPLATE =
-          " {SlotID: [\"%s\"], StopGradients: [ %s ], Edges[ %s ] }";
+          "SlotID: %s, StopGradients: %s, Edges[ %s ]";
       auto out_metas = node.OutputMeta();
       auto in_metas = node.InputMeta();
       std::string out_slot_str = "";
@@ -372,8 +371,8 @@ class EagerUtils {
           GRAD_NODE_TEMPLATE, out_meta_str, in_meta_str);
     } else if (VLOG_IS_ON(5)) {
       const char* GRAD_NODE_TEMPLATE =
-          " { BackwardOutMeta: [ %s ], BackwardInMeta: [ %s ] }";
-      const char* GRAD_SLOT_META_TEMPLATE = "SlotSize: [\"%d\"]";
+          "BackwardOutMeta: [ %s ], BackwardInMeta: [ %s ]";
+      const char* GRAD_SLOT_META_TEMPLATE = "SlotSize: %d";
       std::string out_meta_str = paddle::string::Sprintf(
           GRAD_SLOT_META_TEMPLATE, node.OutputMeta().size());
       std::string in_meta_str = paddle::string::Sprintf(
@@ -387,7 +386,7 @@ class EagerUtils {
 
   static const std::string GradNodeStr(const paddle::experimental::Tensor& t) {
     auto* ad_meta = nullable_autograd_meta(t);
-    if (ad_meta && !(ad_meta->GetMutableGradNode().get())) {
+    if (ad_meta && (ad_meta->GetMutableGradNode().get())) {
       return GradNodeStr((*ad_meta->GetMutableGradNode().get()));
     } else {
       return "None";

paddle/phi/api/yaml/legacy_ops.yaml

@@ -1464,13 +1464,16 @@
   backward : linear_interp_grad
 
 - op : linspace
-  args : (Tensor start, Tensor stop, Tensor number, DataType dtype)
+  args : (Tensor start, Tensor stop, Tensor number, DataType dtype, Place place)
   output : Tensor(out)
   infer_meta :
     func : LinspaceInferMeta
+    param: [start, stop, number, dtype]
   kernel :
     func : linspace
+    param: [start, stop, number, dtype]
     data_type : dtype
+    backend : place
 
 - op : log
   args : (Tensor x)

python/paddle/fluid/layers/tensor.py

@@ -1606,7 +1606,8 @@ def linspace(start, stop, num, dtype=None, name=None):
         with device_guard("cpu"):
             tensor_num = fill_constant([1], 'int32', num)
     if in_dygraph_mode():
-        return _C_ops.linspace(tensor_start, tensor_stop, tensor_num, dtype)
+        return _C_ops.linspace(tensor_start, tensor_stop, tensor_num, dtype,
+                               _current_expected_place())
     if _in_legacy_dygraph():
         return _legacy_C_ops.linspace(tensor_start, tensor_stop, tensor_num,
                                       'dtype', dtype)

python/paddle/tensor/creation.py

@@ -100,7 +100,8 @@ def linspace(start, stop, num, dtype=None, name=None):
         with device_guard("cpu"):
             tensor_num = fill_constant([1], 'int32', num, force_cpu=True)
     if in_dygraph_mode():
-        return _C_ops.linspace(tensor_start, tensor_stop, tensor_num, dtype)
+        return _C_ops.linspace(tensor_start, tensor_stop, tensor_num, dtype,
+                               _current_expected_place())
     if _in_legacy_dygraph():
         return _legacy_C_ops.linspace(tensor_start, tensor_stop, tensor_num,
                                       'dtype', dtype)