初始化

data_parallel.py

@@ -0,0 +1,100 @@
+from torch.nn.parallel import DataParallel
+import torch
+from torch.nn.parallel._functions import Scatter
+from torch.nn.parallel.parallel_apply import parallel_apply
+
+
+def scatter(inputs, target_gpus, chunk_sizes, dim=0):
+    r"""
+    Slices tensors into approximately equal chunks and
+    distributes them across given GPUs. Duplicates
+    references to objects that are not tensors.
+    """
+    def scatter_map(obj):
+        if isinstance(obj, torch.Tensor):
+            try:
+                return Scatter.apply(target_gpus, chunk_sizes, dim, obj)
+            except:
+                print('obj', obj.size())
+                print('dim', dim)
+                print('chunk_sizes', chunk_sizes)
+                quit()
+        if isinstance(obj, tuple) and len(obj) > 0:
+            return list(zip(*map(scatter_map, obj)))
+        if isinstance(obj, list) and len(obj) > 0:
+            return list(map(list, zip(*map(scatter_map, obj))))
+        if isinstance(obj, dict) and len(obj) > 0:
+            return list(map(type(obj), zip(*map(scatter_map, obj.items()))))
+        return [obj for targets in target_gpus]
+
+    # After scatter_map is called, a scatter_map cell will exist. This cell
+    # has a reference to the actual function scatter_map, which has references
+    # to a closure that has a reference to the scatter_map cell (because the
+    # fn is recursive). To avoid this reference cycle, we set the function to
+    # None, clearing the cell
+    try:
+        return scatter_map(inputs)
+    finally:
+        scatter_map = None
+
+
+def scatter_kwargs(inputs, kwargs, target_gpus, chunk_sizes, dim=0):
+    r"""Scatter with support for kwargs dictionary"""
+    inputs = scatter(inputs, target_gpus, chunk_sizes, dim) if inputs else []
+    kwargs = scatter(kwargs, target_gpus, chunk_sizes, dim) if kwargs else []
+    if len(inputs) < len(kwargs):
+        inputs.extend([() for _ in range(len(kwargs) - len(inputs))])
+    elif len(kwargs) < len(inputs):
+        kwargs.extend([{} for _ in range(len(inputs) - len(kwargs))])
+    inputs = tuple(inputs)
+    kwargs = tuple(kwargs)
+    return inputs, kwargs
+
+
+class BalancedDataParallel(DataParallel):
+    def __init__(self, gpu0_bsz, *args, **kwargs):
+        self.gpu0_bsz = gpu0_bsz
+        super().__init__(*args, **kwargs)
+
+    def forward(self, *inputs, **kwargs):
+        if not self.device_ids:
+            return self.module(*inputs, **kwargs)
+        if self.gpu0_bsz == 0:
+            device_ids = self.device_ids[1:]
+        else:
+            device_ids = self.device_ids
+        inputs, kwargs = self.scatter(inputs, kwargs, device_ids)
+        # print('len(inputs)1: ', str(len(inputs)))
+        # print('self.device_ids[:len(inputs)]', str(self.device_ids[:len(inputs)]))
+        if len(self.device_ids) == 1:
+            return self.module(*inputs[0], **kwargs[0])
+        replicas = self.replicate(self.module, self.device_ids[:len(inputs)])
+        if self.gpu0_bsz == 0:
+            replicas = replicas[1:]
+        outputs = self.parallel_apply(replicas, device_ids, inputs, kwargs)
+        return self.gather(outputs, self.output_device)
+
+    def parallel_apply(self, replicas, device_ids, inputs, kwargs):
+        return parallel_apply(replicas, inputs, kwargs, device_ids[:len(inputs)])
+
+    def scatter(self, inputs, kwargs, device_ids):
+        bsz = inputs[0].size(self.dim)
+        num_dev = len(self.device_ids)
+        gpu0_bsz = self.gpu0_bsz
+        bsz_unit = (bsz - gpu0_bsz) // (num_dev - 1)
+        if gpu0_bsz < bsz_unit:
+            chunk_sizes = [gpu0_bsz] + [bsz_unit] * (num_dev - 1)
+            delta = bsz - sum(chunk_sizes)
+            for i in range(delta):
+                chunk_sizes[i + 1] += 1
+            if gpu0_bsz == 0:
+                chunk_sizes = chunk_sizes[1:]
+        else:
+            return super().scatter(inputs, kwargs, device_ids)
+
+        # print('bsz: ', bsz)
+        # print('num_dev: ', num_dev)
+        # print('gpu0_bsz: ', gpu0_bsz)
+        # print('bsz_unit: ', bsz_unit)
+        # print('chunk_sizes: ', chunk_sizes)
+        return scatter_kwargs(inputs, kwargs, device_ids, chunk_sizes, dim=self.dim)