Python fluid.global_scope方法代码示例

# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import global_scope [as 别名]
def load(self, param_state_pairs, optim_state):
        if self._executor is None:
            executor = fluid.Executor(fluid.CPUPlace())._default_executor
        else:
            executor = self._executor._default_executor

        # restore parameter states
        fluid.core._create_loaded_parameter(
            [param for param, state in param_state_pairs],
            global_scope(), executor)
        for param, state in param_state_pairs:
            self._set_var(param, state)

        # restore optimizer states
        # FIXME what if a different optimizer is used?
        if not self.model._optimizer or not optim_state:
            return
        self._load_optimizer(optim_state, executor) 

# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import global_scope [as 别名]
def cast_fp32_to_fp16(exe, main_program):
    """ cast_fp32_to_fp16 """
    logging.info("Cast parameters to float16 data format.")
    for param in main_program.global_block().all_parameters():
        if not param.name.endswith(".master"):
            #load fp16
            param_t = fluid.global_scope().find_var(param.name).get_tensor()
            data = np.array(param_t)
            if param.name.startswith("encoder_layer") \
                    and "layer_norm" not in param.name:
                logging.info(param.name)
                param_t.set(np.float16(data).view(np.uint16), exe.place)

            #load fp32
            master_param_var = fluid.global_scope().find_var(param.name + 
                    ".master")
            if master_param_var is not None:
                master_param_var.get_tensor().set(data, exe.place) 

# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import global_scope [as 别名]
def extract_weights(args):
    # add ERNIR to environment
    print('extract weights start'.center(60, '='))
    startup_prog = fluid.Program()
    test_prog = fluid.Program()
    place = fluid.CPUPlace()
    exe = fluid.Executor(place)
    exe.run(startup_prog)
    args.max_seq_len = 512
    args.use_fp16 = False
    args.num_labels = 2
    args.loss_scaling = 1.0
    ernie_config = ErnieConfig(args.ernie_config_path)
    ernie_config.print_config()
    with fluid.program_guard(test_prog, startup_prog):
        with fluid.unique_name.guard():
            create_model(
                args,
                pyreader_name='train',
                ernie_config=ernie_config)
    fluid.io.load_vars(exe, args.init_pretraining_params, main_program=test_prog, predicate=if_exist)
    state_dict = collections.OrderedDict()
    weight_map = build_weight_map()
    for ernie_name, pytorch_name in weight_map.items():
        fluid_tensor = fluid.global_scope().find_var(ernie_name).get_tensor()
        fluid_array = np.array(fluid_tensor, dtype=np.float32)
        if 'w_0' in ernie_name:
            fluid_array = fluid_array.transpose()
        state_dict[pytorch_name] = fluid_array
        print(f'{ernie_name} -> {pytorch_name} {fluid_array.shape}')
    print('extract weights done!'.center(60, '='))
    return state_dict 

# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import global_scope [as 别名]
def _set_var(self, var, ndarray):
        t = global_scope().find_var(var.name).get_tensor()
        p = t._place()
        if p.is_cpu_place():
            place = fluid.CPUPlace()
        elif p.is_cuda_pinned_place():
            place = fluid.CUDAPinnedPlace()
        else:
            p = fluid.core.Place()
            p.set_place(t._place())
            place = fluid.CUDAPlace(p.gpu_device_id())

        t.set(ndarray, place) 

# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import global_scope [as 别名]
def _compile_and_initialize(self, prog, mode):
        compiled_prog = self._compiled_progs.get(mode, None)
        if compiled_prog is not None:
            return compiled_prog

        assert self.model._place is not None, \
            "device is not set, please call `model.prepare()` first"

        place = self.model._place

        # XXX *ALL WEIGHTS* should be initialized upon model construction
        # even if `forward()` may run different code path for different mode
        # therefore startup program only needs to run once
        if self._executor is None:
            self._executor = fluid.Executor(place)
            # XXX incremental initialization
            uninitialized = []
            for var_py in self._startup_prog.list_vars():
                var = fluid.global_scope().find_var(var_py.name)
                if not var_py.name.startswith('nccl_id') and var and \
                        var.get_tensor()._is_initialized():
                    continue

                uninitialized.append(var_py)
            if uninitialized:
                startup_prog = self._startup_prog._prune(uninitialized)
                self._executor.run(startup_prog)

        if self._nranks < 2:
            compiled_prog = fluid.CompiledProgram(prog)
        else:
            compiled_prog = prog

        self._compiled_progs[mode] = compiled_prog 

# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import global_scope [as 别名]
def cast_fp32_to_fp16(exe, main_program):
    print("Cast parameters to float16 data format.")
    for param in main_program.global_block().all_parameters():
        if not param.name.endswith(".master"):
            param_t = fluid.global_scope().find_var(param.name).get_tensor()
            data = np.array(param_t)
            if param.name.find("layer_norm") == -1:
                param_t.set(np.float16(data).view(np.uint16), exe.place)
            master_param_var = fluid.global_scope().find_var(param.name +
                                                             ".master")
            if master_param_var is not None:
                master_param_var.get_tensor().set(data, exe.place) 

# 需要导入模块: from paddle import fluid [as 别名]
# 或者: from paddle.fluid import global_scope [as 别名]
def set_zero(var_name):
    param = fluid.global_scope().var(var_name).get_tensor()
    param_array = np.zeros(param._get_dims()).astype("int64")
    param.set(param_array, fluid.CPUPlace())