Add MFU logging support

swift/plugin/callback.py

@@ -1,5 +1,8 @@
 # Copyright (c) Alibaba, Inc. and its affiliates.
+import time
+
 import numpy as np
+import torch
 from transformers import TrainerCallback, TrainerControl, TrainerState, TrainingArguments
 
 from swift.utils import get_logger
@@ -28,6 +31,134 @@ def on_save(self, args: TrainingArguments, state: TrainerState, control: Trainer
             control.should_training_stop = True
 
 
-extra_callbacks = []
+class PerfMetricsLogCallback(TrainerCallback):
+    """An callback for perf metrics (MFU etc) log implementation"""
+
+    def __init__(self):
+        self.device_tflops = None
+        self.elapsed = 0.0
+        self.step_start_time = None
+
+    def on_init_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+        from swift.utils import get_current_device, get_device_count, get_env_args
+
+        # Top priority. Specify by ENV
+        tflops = get_env_args('DEVICE_TFLOPS', int, None)
+        device_count = max(get_device_count(), 1)
+        if tflops is not None:
+            logger.info(f"Specify theoretical max TFLOPS through ENV 'DEVICE_TFLOPS'. [{tflops} TFLOPS]")
+        else:
+            # Run a estimating test.
+            dtype = kwargs.get('model').dtype
+            device = torch.device(get_current_device())
+            logger.info(f'Estimating device TFLOPS baseline. Device: [{device}] dtype: [{dtype}]')
+            tflops = self._estimate_device_tflops_by_dtype(device, dtype)
+            logger.info(f'Estimate test finished. [{tflops} TFLOPS] Device count: [{device_count}]')
+        # TODO Collect comprehensive TFLOPS data. Then provide a fallback strategy based on lookup tables.
+
+        self.device_tflops = tflops * device_count
+
+    def on_step_begin(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+        self.step_start_time = time.time()
+
+    def on_step_end(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, **kwargs):
+        self.elapsed += time.time() - self.step_start_time
+
+    def on_log(self, args: TrainingArguments, state: TrainerState, control: TrainerControl, logs=None, **kwargs):
+        total_flos = getattr(state, 'total_flos', 0)
+        actual_flops = total_flos / self.elapsed
+        theoretical_max_flops = self.device_tflops * 1e12
+        mfu = actual_flops / theoretical_max_flops
+        logger.debug(f'Total_flos[{total_flos}] elapsed_time[{self.elapsed}]sec Average MFU[{mfu}]')
+        logs['MFU'] = round(mfu, 6)
+
+    @staticmethod
+    def _estimate_device_tflops_by_dtype(device: torch.device, dtype: torch.dtype, repeats: int = 60, dim: int = 8192):
+        from swift.utils.torch_utils import empty_cache
+
+        def device_synchronize(sync_device):
+            if backend == 'cuda':
+                torch.cuda.synchronize(sync_device)
+            elif backend == 'npu':
+                torch.npu.synchronize(sync_device)
+            elif backend == 'cpu':
+                torch.cpu.synchronize(sync_device)
+
+        # 默认矩阵规模
+        shape = (dim, dim)
+        backend = device.type
+        if backend == 'npu':
+            import torch_npu
+
+        # 创建矩阵
+        a = torch.randn(*shape, device=device, dtype=dtype)
+        b = torch.randn(*shape, device=device, dtype=dtype)
+
+        # 预热
+        for _ in range(5):
+            c = torch.matmul(a, b)
+        device_synchronize(device)
+
+        # 进行测试
+        start = time.time()
+        for _ in range(repeats):
+            c = torch.matmul(a, b)
+        device_synchronize(device)
+        end = time.time()
+        total_time = end - start
+        avg_time = total_time / repeats
+
+        # 若测试时间过短，调整循环次数并重新测试
+        if total_time < 3:
+            repeats = int(6 / avg_time)
+            start = time.time()
+            for _ in range(repeats):
+                c = torch.matmul(a, b)
+            device_synchronize(device)
+            end = time.time()
+            total_time = end - start
+            avg_time = total_time / repeats
+
+        del a, b, c
+        empty_cache()
+
+        tflops = (2 * dim**3 / avg_time) / 1e12
+        logger.info(f'[Device {device}] Total time: {total_time:.4f}s, dtype: {dtype}, Perf: {tflops:.4f} TFLOPS')
+        return tflops
+
+    @staticmethod
+    def _retrieve_flops_from_map(device):
+        """Retrieve theoretical FLOPS from Map.    """
+
+        device_name = device.get_device_name()
+        flops = None
+        for name, value in device_flops_map.items():
+            if name in device_name:
+                flops = value
+                break
+
+        return flops
+
+
+device_flops_map = {
+    'GB200': 2.5e15,
+    'B200': 2.25e15,
+    'MI300X': 1336e12,
+    'H100': 312e12,
+    'H800': 312e12,
+    'H200': 989e12,
+    'A100': 312e12,
+    'A800': 312e12,
+    'L40S': 362.05e12,
+    'L40': 181.05e12,
+    'A40': 149.7e12,
+    'L20': 119.5e12,
+    'H20': 148e12,
+    '910B': 354e12,
+    'Ascend910': 354e12,
+    'RTX 3070 Ti': 21.75e12
+}
+
+extra_callbacks = [PerfMetricsLogCallback()]
 # This example shows a simple example of EarlyStop Callback, uncomment this to use
-# extra_callbacks = [EarlyStopCallback()]
+# extra_callbacks = [EarlyStopCallback()]