[gemini] param_trace_hook (#2020)

colossalai/gemini/ophooks/param_trace_hook.py

+from contextlib import contextmanager
+from enum import Enum
+from functools import partial
+from typing import List
+
+import torch
+
+from colossalai.gemini.memory_tracer import SyncCudaMemoryMonitor
+from colossalai.tensor.param_op_hook import ParamOpHook
+
+
+class TrainingPhase(Enum):
+    FORWARD = 0
+    BACKWARD = 1
+
+
+class ParamMemHook(ParamOpHook):
+
+    def __init__(self) -> None:
+        super().__init__()
+        self._training_phase = TrainingPhase.FORWARD
+        self.mem_monitor = SyncCudaMemoryMonitor()
+        self._non_model_data_list = []
+        self._model_data_list = []
+
+    def _move_params_to_dev(self, params, dev: str) -> int:
+        assert isinstance(dev, str), f"device should be a str not torch.device"
+        comm_volume = 0
+        for p in params:
+            if p.data.device.type != dev:
+                p.data = p.data.to(dev)
+                comm_volume += p.data.numel() * p.data.element_size()
+            if p.grad is not None:
+                if p.grad.device.type != dev:
+                    p.grad = p.grad.to(dev)
+                    comm_volume += p.grad.numel() * p.grad.element_size()
+        return comm_volume
+
+    def sample_model_data(self, params):
+        data_volume = 0
+        for p in params:
+            data_volume += p.data.numel() * p.data.element_size()
+        if self._training_phase == TrainingPhase.BACKWARD:
+            # add param.grad, actually param.grad is None in this time
+            data_volume *= 2
+        self._model_data_list.append(data_volume)
+
+    def pre_op(self, params):
+        cuda_volume = self.mem_monitor.finish()
+        if len(self._model_data_list):
+            self._non_model_data_list.append(cuda_volume - self._model_data_list[-1])
+        self._move_params_to_dev(params, 'cuda')
+        self.sample_model_data(params)
+        self.mem_monitor.start()
+
+    def post_op(self, params):
+        self._move_params_to_dev(params, 'cpu')
+
+    def pre_forward(self, params: List[torch.Tensor]) -> None:
+        self.pre_op(params)
+
+    def post_forward(self, params: List[torch.Tensor]) -> None:
+        self.post_op(params)
+
+    def pre_backward(self, params: List[torch.Tensor]) -> None:
+        self.pre_op(params)
+
+    def post_backward(self, params: List[torch.Tensor]) -> None:
+        self.post_op(params)
+
+    @contextmanager
+    def switch_training_phase(self, training_phase: TrainingPhase = TrainingPhase.BACKWARD):
+        old_training_phase = self._training_phase
+        try:
+            self._training_phase = training_phase
+            yield
+        finally:
+            self._training_phase = old_training_phase
+
+    switch_to_backward = switch_training_phase
+    switch_to_forward = partial(switch_to_backward, training_phase=TrainingPhase.FORWARD)
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