[Zero-Dim] fix functool.reduce more safe with intial value, to support empty list (#53182)

python/paddle/distributed/auto_parallel/cost/estimate_cost.py

@@ -252,7 +252,7 @@ class CostEstimator:
 
     def _calculate_bytes(self, sizes, dtype):
         if sizes:
-            total_count = reduce(lambda x, y: x * y, sizes)
+            total_count = reduce(lambda x, y: x * y, sizes, 1)
         else:
             total_count = 0
 

python/paddle/distributed/auto_parallel/cost/tensor_cost.py

@@ -96,7 +96,7 @@ class TensorCost:
             shape = self.shape
             dtype = self.dtype
 
-        total_count = reduce(lambda x, y: x * y, shape)
+        total_count = reduce(lambda x, y: x * y, shape, 1)
 
         if dtype == paddle.float32 or dtype == paddle.int32:
             dtype_factor = 4

python/paddle/distributed/auto_parallel/planner.py

@@ -336,7 +336,7 @@ class PlanSpace:
         ops = program.global_block().ops
         vars = program.global_block().vars
 
-        processes = reduce(lambda x, y: x * y, process_mesh_topology)
+        processes = reduce(lambda x, y: x * y, process_mesh_topology, 1)
         global_group = list(range(processes))
         global_process_mesh = None
         pipeline_process_meshes = None

python/paddle/distributed/auto_parallel/reshard.py

@@ -1120,7 +1120,7 @@ class Resharder:
         """Compute the index of process_shape corresponding to the process."""
         relative_process = process_group.index(process)
         process_index = []
-        product = reduce(lambda x, y: x * y, process_shape)
+        product = reduce(lambda x, y: x * y, process_shape, 1)
 
         for i in range(len(process_shape)):
             idx = relative_process // (product // process_shape[i])

python/paddle/distributed/auto_parallel/tuner/rule_based_tuner.py

@@ -2120,7 +2120,7 @@ class RuleBasedTuner:
             has_used_devices = 0
             self.device_meshes_list.append([])
             for device_mesh in device_meshes:
-                devices = reduce(lambda x, y: x * y, device_mesh)
+                devices = reduce(lambda x, y: x * y, device_mesh, 1)
                 processes = list(
                     range(has_used_devices, has_used_devices + devices)
                 )

python/paddle/distributed/auto_parallel/utils.py

@@ -1684,7 +1684,7 @@ def get_standalone_cost_data(distributed_programs):
                 ].split(",")
                 shape = [int(x.strip()) for x in shape]
                 dtype_factor = 1
-                total_static_input_size += reduce(lambda x, y: x * y, shape)
+                total_static_input_size += reduce(lambda x, y: x * y, shape, 1)
                 if op.type == "c_embedding":
                     arg_name_lower = (
                         "w" if arg_name_lower == "weight" else "ids"
@@ -1838,7 +1838,7 @@ def get_var_numel(var):
     """
     assert isinstance(var, Variable)
     assert -1 not in var.shape
-    return reduce(lambda x, y: x * y, var.shape)
+    return reduce(lambda x, y: x * y, var.shape, 1)
 
 
 def get_lr(optimizer):

python/paddle/distributed/fleet/base/topology.py

@@ -62,7 +62,7 @@ class CommunicateTopology:
         self.coordinate = collections.namedtuple(
             'Coordinate', self._parallel_names
         )
-        self._world_size = reduce(lambda x, y: x * y, self._dims)
+        self._world_size = reduce(lambda x, y: x * y, self._dims, 1)
 
         ranges = [range(d) for d in self._dims]
         all_coordinate = [self.coordinate(*x) for x in product(*ranges)]

python/paddle/distributed/fleet/meta_optimizers/dgc_optimizer.py

@@ -113,7 +113,7 @@ class DGCMomentumOptimizer(Optimizer):
         return regular_type, regular_coeff
 
     def _is_use_dgc(self, param_var, grad_var):
-        var_numel = abs(reduce(lambda x, y: x * y, param_var.shape))
+        var_numel = abs(reduce(lambda x, y: x * y, param_var.shape, 1))
         if (
             var_numel < 16384
             or param_var.type == core.VarDesc.VarType.SELECTED_ROWS

python/paddle/distributed/fleet/meta_optimizers/dygraph_optimizer/dygraph_sharding_optimizer.py

@@ -111,7 +111,7 @@ class DygraphShardingOptimizer:
         for param in self._parameter_list:
             rank = sizes.index(min(sizes))
             mapping[rank].append(param)
-            numel = reduce(lambda x, y: x * y, param.shape)
+            numel = reduce(lambda x, y: x * y, param.shape, 1)
             assert (
                 numel > 0
             ), "param [{}] should larger than 0, but it is [{}]".format(

python/paddle/distributed/fleet/meta_optimizers/sharding/utils.py

@@ -898,7 +898,7 @@ def get_var_size(param):
     """
     assert -1 not in param.shape
     return (
-        reduce(lambda x, y: x * y, param.shape)
+        reduce(lambda x, y: x * y, param.shape, 1)
         * DtypeToSize[param.dtype]
         / 1024.0
         / 1024.0

python/paddle/distributed/passes/auto_parallel_grad_clip.py

@@ -75,8 +75,8 @@ def _get_dpmp_topology(origin_topology, sharding_group):
         sharding_axis = 0
         dp_sharding_topology = dp_sharding_topology[1:]
 
-    product_dp_sharding = reduce(lambda x, y: x * y, dp_sharding_topology)
-    product_topology = reduce(lambda x, y: x * y, origin_topology)
+    product_dp_sharding = reduce(lambda x, y: x * y, dp_sharding_topology, 1)
+    product_topology = reduce(lambda x, y: x * y, origin_topology, 1)
 
     if product_topology == product_dp_sharding:
         dpmp_topology = dp_sharding_topology
@@ -274,7 +274,7 @@ class ClipHelper:
             for param in params:
                 rank = sizes.index(min(sizes))
                 mapping[rank].append(param.name)
-                numel = reduce(lambda x, y: x * y, param.shape)
+                numel = reduce(lambda x, y: x * y, param.shape, 1)
                 assert (
                     numel > 0
                 ), "param [{}] should larger than 0, but it is [{}]".format(

python/paddle/distributed/passes/auto_parallel_sharding.py

@@ -1661,7 +1661,7 @@ def partition_by_greedy_even(params, group_size):
     for param in params:
         rank = sizes.index(min(sizes))
         mapping[rank].append(param)
-        numel = reduce(lambda x, y: x * y, param.shape)
+        numel = reduce(lambda x, y: x * y, param.shape, 1)
         assert (
             numel > 0
         ), "param [{}] should larger than 0, but it is [{}]".format(

python/paddle/distributed/ps/utils/public.py

@@ -386,7 +386,7 @@ def get_dense_send_context(
             grad = merged[1]
             origin_varnames.append(grad.merged_var.name)
             var = program.global_block().vars[grad.merged_var.name]
-            var_numel += reduce(lambda x, y: x * y, var.shape)
+            var_numel += reduce(lambda x, y: x * y, var.shape, 1)
         grad_name = "Dense@GRAD_" + str(idx)
         aggregate = True
         # print("public get_dense_send_context dense_table:", grad_name,
@@ -422,7 +422,7 @@ def get_dense_send_context(
             grad = merged[1]
             origin_varnames.append(grad.merged_var.name)
             var = program.global_block().vars[grad.merged_var.name]
-            var_numel += reduce(lambda x, y: x * y, var.shape)
+            var_numel += reduce(lambda x, y: x * y, var.shape, 1)
         grad_name = "DataNorm@GRAD_" + str(idx)
         aggregate = True
         # print("public get_dense_send_context data_norm table:", grad_name,
@@ -452,7 +452,7 @@ def get_dense_send_context(
             grad = merged[1]
             origin_varname = grad.merged_var.name
             var = program.global_block().vars[origin_varname]
-            var_numel = reduce(lambda x, y: x * y, var.shape)
+            var_numel = reduce(lambda x, y: x * y, var.shape, 1)
             grad_name = origin_varname
             aggregate = True
             from paddle.fluid.core import CommContext
@@ -503,7 +503,7 @@ def get_geo_trainer_send_context(attrs):
                 True if param_name in distibuted_varnames else False
             )
             var = program.global_block().vars[grad.merged_var.name]
-            var_numel = reduce(lambda x, y: x * y, var.shape[1:])
+            var_numel = reduce(lambda x, y: x * y, var.shape[1:], 1)
             from paddle.fluid.core import CommContext
 
             print(
@@ -1167,7 +1167,7 @@ def get_communicate_var_info(
     for name in entrance_var_list:
         var = program.global_block().vars[name]
         shape = var.shape
-        recv_var_dim = -1 * reduce(lambda x, y: x * y, shape)
+        recv_var_dim = -1 * reduce(lambda x, y: x * y, shape, 1)
         input_var_reshape_dim.append(recv_var_dim)
         input_var_reshape_name.append(f"{name}.input_reshape@Heter")
 
@@ -1448,7 +1448,7 @@ dtype_to_size = {
 
 
 def get_var_mem_size(var):
-    m_size = reduce(lambda x, y: x * y, var.shape)
+    m_size = reduce(lambda x, y: x * y, var.shape, 1)
     m_size *= dtype_to_size[var.dtype]
     return m_size
 

python/paddle/distributed/transpiler/distribute_transpiler.py

@@ -117,7 +117,7 @@ def slice_variable(var_list, slice_count, min_block_size):
     blocks = []
     for var in var_list:
         split_count = slice_count
-        var_numel = reduce(lambda x, y: x * y, var.shape)
+        var_numel = reduce(lambda x, y: x * y, var.shape, 1)
         max_pserver_count = int(math.floor(var_numel / float(min_block_size)))
         if max_pserver_count == 0:
             max_pserver_count = 1
@@ -127,7 +127,7 @@ def slice_variable(var_list, slice_count, min_block_size):
 
         if len(var.shape) >= 2:
             # align by dim1(width)
-            dim1 = reduce(lambda x, y: x * y, var.shape[1:])
+            dim1 = reduce(lambda x, y: x * y, var.shape[1:], 1)
             remains = block_size % dim1
             if remains != 0:
                 block_size += dim1 - remains
@@ -2286,7 +2286,9 @@ WIKI: https://github.com/PaddlePaddle/Fleet/blob/develop/markdown_doc/transpiler
             orig_shape = orig_var.shape
             orig_dim1_flatten = 1
             if len(orig_shape) >= 2:
-                orig_dim1_flatten = reduce(lambda x, y: x * y, orig_shape[1:])
+                orig_dim1_flatten = reduce(
+                    lambda x, y: x * y, orig_shape[1:], 1
+                )
 
             for i, block in enumerate(split):
                 size = block[1]

python/paddle/fluid/optimizer.py

@@ -5968,7 +5968,7 @@ class PipelineOptimizer:
         }
         assert -1 not in var.shape
         return (
-            reduce(lambda x, y: x * y, var.shape)
+            reduce(lambda x, y: x * y, var.shape, 1)
             * dtype_to_size[var.dtype]
             / 1024.0
             / 1024.0

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

@@ -46,7 +46,7 @@ class TestDygraphWeightNorm(unittest.TestCase):
     def norm_except_dim(self, w, dim=None):
         shape = w.shape
         ndims = len(shape)
-        shape_numel = reduce(lambda x, y: x * y, shape)
+        shape_numel = reduce(lambda x, y: x * y, shape, 1)
         if dim == -1:
             return np.linalg.norm(w, axis=None, keepdims=True).flatten()
         elif dim == 0:
@@ -68,7 +68,7 @@ class TestDygraphWeightNorm(unittest.TestCase):
     def weight_normalize(self, w, dim=None):
         shape = w.shape
         ndims = len(shape)
-        shape_numel = reduce(lambda x, y: x * y, shape)
+        shape_numel = reduce(lambda x, y: x * y, shape, 1)
         v = w
         g = self.norm_except_dim(w, dim)
         g_mul = g

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

@@ -1427,7 +1427,7 @@ class TestGradientTruncated(unittest.TestCase):
         paddle.enable_static()
 
         to_string = lambda x, i: x + '_' + str(i)
-        numel = lambda input_shape: reduce(lambda x, y: x * y, input_shape)
+        numel = lambda input_shape: reduce(lambda x, y: x * y, input_shape, 1)
 
         def op1(x):
             value = paddle.tensor.fill_constant([1], "float32", 1)

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

@@ -612,7 +612,7 @@ class TestListIndex(unittest.TestCase):
         np.random.seed(2022)
 
     def numel(self, shape):
-        return reduce(lambda x, y: x * y, shape)
+        return reduce(lambda x, y: x * y, shape, 1)
 
     def test_static_graph_list_index(self):
         paddle.enable_static()

python/paddle/fluid/variable_index.py

@@ -117,7 +117,7 @@ class SliceInfo:
         return s
 
     def numel(self, shape):
-        return reduce(lambda x, y: x * y, shape)
+        return reduce(lambda x, y: x * y, shape, 1)
 
     def get_offset_stride(self, tensor_shape):
         for index in self.indexes:

python/paddle/incubate/distributed/fleet/parameter_server/ir/public.py

@@ -652,7 +652,7 @@ class CompileTimeStrategy:
                 var = self.origin_main_program.global_block().vars[
                     grad.merged_var.name
                 ]
-                var_numel = reduce(lambda x, y: x * y, var.shape[1:])
+                var_numel = reduce(lambda x, y: x * y, var.shape[1:], 1)
 
                 sparse_ctx = core.CommContext(
                     grad_name,
@@ -705,7 +705,7 @@ class CompileTimeStrategy:
                 var = self.origin_main_program.global_block().vars[
                     grad.merged_var.name
                 ]
-                var_numel += reduce(lambda x, y: x * y, var.shape)
+                var_numel += reduce(lambda x, y: x * y, var.shape, 1)
             grad_name = "Dense@Grad"
             trainer_id = self.get_role_id()
             aggregate = True
@@ -734,7 +734,7 @@ class CompileTimeStrategy:
                 var = self.origin_main_program.global_block().vars[
                     origin_varname
                 ]
-                var_numel = reduce(lambda x, y: x * y, var.shape)
+                var_numel = reduce(lambda x, y: x * y, var.shape, 1)
                 grad_name = origin_varname
                 aggregate = True
                 dense_ctx = core.CommContext(
@@ -1058,7 +1058,7 @@ class CompileTimeStrategy:
         blocks = []
         for var in var_list:
             if not uniform:
-                var_numel = reduce(lambda x, y: x * y, var.shape)
+                var_numel = reduce(lambda x, y: x * y, var.shape, 1)
 
                 split_count = 1
 
@@ -1077,7 +1077,7 @@ class CompileTimeStrategy:
 
                 if len(var.shape) >= 2:
                     # align by dim1(width)
-                    dim1 = reduce(lambda x, y: x * y, var.shape[1:])
+                    dim1 = reduce(lambda x, y: x * y, var.shape[1:], 1)
                     remains = block_size % dim1
                     if remains != 0:
                         block_size += dim1 - remains
@@ -1102,7 +1102,7 @@ class CompileTimeStrategy:
                 for i in range(remainder):
                     dim0s[i] = dim0s[i] + 1
 
-                dim1 = reduce(lambda x, y: x * y, var.shape[1:])
+                dim1 = reduce(lambda x, y: x * y, var.shape[1:], 1)
 
                 for block_id in range(len(dim0s)):
                     numel = dim0s[block_id] * dim1

python/paddle/incubate/distributed/fleet/parameter_server/ir/trainer_pass.py

@@ -1484,7 +1484,7 @@ def get_communicate_var_info(
         #     raise ValueError(
         #         "Variable {} not support heter training. its shape is {}".
         #         format(name, shape))
-        recv_var_dim = -1 * reduce(lambda x, y: x * y, shape)
+        recv_var_dim = -1 * reduce(lambda x, y: x * y, shape, 1)
         input_var_reshape_dim.append(recv_var_dim)
         input_var_reshape_name.append(f"{name}.input_reshape@Heter")
 
@@ -1497,7 +1497,7 @@ def get_communicate_var_info(
     #    #     raise ValueError(
     #    #         "Variable {} not support heter training. its shape is {}".
     #    #         format(var_name, shape))
-    #    send_reshape_dim = -1 * reduce(lambda x, y: x * y, shape)
+    #    send_reshape_dim = -1 * reduce(lambda x, y: x * y, shape, 1)
     #    output_var_reshape_dim.append(send_reshape_dim)
     #    output_var_reshape_name.append("{}.output_reshape@Heter".format(
     #        var_name))

python/paddle/incubate/distributed/fleet/parameter_server/ir/vars_metatools.py

@@ -65,7 +65,7 @@ class VarStruct:
         self.lod_level = lod_level
         self.persistable = persistable
         self.m_size = 1
-        self.m_size = reduce(lambda x, y: x * y, shape)
+        self.m_size = reduce(lambda x, y: x * y, shape, 1)
         self.m_size *= dtype_to_size[dtype]
 
     def __str__(self):

python/paddle/nn/functional/norm.py

@@ -546,7 +546,7 @@ def local_response_norm(
 
     from functools import reduce
 
-    sum_sizes = reduce(lambda x, y: x * y, sizes[1:])
+    sum_sizes = reduce(lambda x, y: x * y, sizes[1:], 1)
 
     div = paddle.unsqueeze(paddle.multiply(x, x), axis=1)
     if not channel_last:

python/paddle/nn/utils/transform_parameters.py

@@ -155,7 +155,7 @@ def vector_to_parameters(vec, parameters, name=None):
     for param in parameters:
         shape = param.shape
         origin_shapes.append(shape)
-        numel = reduce(lambda x, y: x * y, shape)
+        numel = reduce(lambda x, y: x * y, shape, 1)
         sections.append(numel)
 
     if len(sections) == 1:

python/paddle/static/nn/common.py

@@ -3601,7 +3601,7 @@ def layer_norm(
     # create intput and parameters
     inputs = {'X': input}
     input_shape = input.shape
-    param_shape = [reduce(lambda x, y: x * y, input_shape[begin_norm_axis:])]
+    param_shape = [reduce(lambda x, y: x * y, input_shape[begin_norm_axis:], 1)]
     if scale:
         assert (
             param_attr is not False

test/ir/inference/test_onednn_reshape_transpose_matmul_fuse_pass.py

@@ -46,13 +46,13 @@ class TestOneDNNReshapeTransposeMatmulFusePass(PassAutoScanTest):
 
         def generate_input2(attrs):
             shape_x = [attrs[3]['batch_size'], attrs[3]['channel'], self.num]
-            input_volume = reduce(lambda x, y: x * y, shape_x)
+            input_volume = reduce(lambda x, y: x * y, shape_x, 1)
             matmul_shape = list(attrs[0]['shape'])
             if 0 in matmul_shape:
                 for i in range(len(matmul_shape)):
                     if matmul_shape[i] == 0:
                         matmul_shape[i] = shape_x[i]
-            shape_volume = reduce(lambda x, y: x * y, matmul_shape)
+            shape_volume = reduce(lambda x, y: x * y, matmul_shape, 1)
 
             if -1 in matmul_shape:
                 for i in range(len(matmul_shape)):

test/xpu/test_set_value_op_xpu.py

@@ -1252,7 +1252,9 @@ class XPUTestSetValueOp(XPUOpTestWrapper):
             paddle.enable_static()
 
             to_string = lambda x, i: x + '_' + str(i)
-            numel = lambda input_shape: reduce(lambda x, y: x * y, input_shape)
+            numel = lambda input_shape: reduce(
+                lambda x, y: x * y, input_shape, 1
+            )
 
             def op1(x):
                 value = paddle.tensor.fill_constant([1], "float32", 1)