[hybrid performance] Early grad fusion. (#54403)

python/paddle/distributed/fleet/meta_parallel/pp_utils/utils.py

@@ -18,10 +18,21 @@ import numpy as np
 
 import paddle
 from paddle import _legacy_C_ops
-from paddle.distributed.parallel import _split_tensors
+from paddle.distributed.fleet.meta_parallel.sharding.group_sharded_storage import (
+    GradStorage,
+)
 from paddle.fluid import core
 from paddle.framework import base as imperative_base
 
+alignment = {
+    "gpu": 256,
+}
+align = {
+    paddle.float16.value: 2,
+    paddle.bfloat16.value: 2,
+    paddle.float32.value: 4,
+}
+
 __all__ = []
 
 
@@ -120,26 +131,47 @@ def _all_gather(tensor, group=None, use_calc_stream=True):
     )
 
 
+def flatten_dense_tensors(parameters, use_main_grad=False):
+    _buffer_size = 0
+    _param2align = {}
+    dtype = paddle.float32 if use_main_grad else parameters[0].dtype
+
+    for param in parameters:
+        assert param.trainable, "param must be trainable..."
+        size = np.prod(param.shape) * align[dtype]
+        remaining = size % alignment["gpu"]
+        ali = 0 if remaining == 0 else alignment["gpu"] - remaining
+        align_ = ali // align[dtype]
+        _buffer_size += np.prod(param.shape) + align_
+        _param2align[param.name] = align_
+
+    # process gradient
+    grad_storage = GradStorage(
+        size=_buffer_size,
+        dtype=dtype,
+        device="gpu",
+        destination="0",
+        parm2align=_param2align,
+    )
+
+    for param in parameters:
+        grad_storage.add_grad(param, _param2align[param.name])
+
+    return grad_storage.buffer
+
+
 class FusedCommBuffer:
-    def __init__(
-        self,
-        id,
-        params,
-        comm_group,
-        acc_steps=1,
-        act=None,
-        dst=-1,
-    ):
+    def __init__(self, id, params, comm_group, acc_steps=1, act=None, dst=-1):
         self._id = id
         self._params = params
         self._acc_steps = acc_steps
         self._comm_group = comm_group
 
-        self._tasks = []
-        self._grads = []
+        use_main_grad = hasattr(self._params[0], "main_grad")
+
+        self._task = None
         self._params_step_dict = {}
         self._params_checked_in = 0
-        self._coalesced_grads_and_grad_vars = []
 
         self._act = act
         if self._act == HOOK_ACTION.ALL_REDUCE:
@@ -154,16 +186,16 @@ class FusedCommBuffer:
 
         self._init_step_dict()
 
+        self.grad_storage = flatten_dense_tensors(self._params, use_main_grad)
+
     def _init_step_dict(self):
         for p in self._params:
             self._params_step_dict[p.name] = 0
 
     def _reset_params_checked_in(self):
-        self._tasks.clear()
-        self._grads.clear()
+        self._task = None
         self._init_step_dict()
         self._params_checked_in = 0
-        self._coalesced_grads_and_grad_vars.clear()
 
     @property
     def _all_params_checked_in(self):
@@ -175,13 +207,6 @@ class FusedCommBuffer:
     def add_grad(self, param):
         assert param.name in self._params_step_dict
 
-        if self._params_step_dict[param.name] == 0:
-            if getattr(param, "main_grad", None) is not None:
-                assert param.grad is None
-                self._grads.append(param.main_grad)
-            else:
-                self._grads.append(param.grad)
-
         self._params_step_dict[param.name] += 1
 
         if self._params_step_dict[param.name] == self._acc_steps:
@@ -189,49 +214,33 @@ class FusedCommBuffer:
             self._params_step_dict.pop(param.name)
 
         if self._all_params_checked_in:
-            self._fused_comm_grads()
+            self._comm_grads()
 
     @imperative_base.no_grad
-    def _fused_comm_grads(self):
+    def _comm_grads(self):
         assert self._all_params_checked_in
-        flattened_vars = []
-        g_var_shapes = []
-
-        for g_var in self._grads:
-            g_var_shapes.append(g_var.shape)
-            flattened_vars.append(
-                paddle.reshape(x=g_var, shape=[np.prod(g_var.shape)])
-            )
-
-        coalesced_grad = paddle.concat(flattened_vars)
-        self._coalesced_grads_and_grad_vars.append(
-            [coalesced_grad, self._grads, g_var_shapes]
-        )
 
-        for coalesced_grad, _, _ in self._coalesced_grads_and_grad_vars:
         if self._act == HOOK_ACTION.ALL_REDUCE:
             task = paddle.distributed.all_reduce(
-                    coalesced_grad, group=self._comm_group, sync_op=False
+                self.grad_storage, group=self._comm_group, sync_op=False
             )
         elif self._act == HOOK_ACTION.REDUCE:
             task = paddle.distributed.reduce(
-                    coalesced_grad,
+                self.grad_storage,
                 dst=self._dst,
                 group=self._comm_group,
                 sync_op=False,
             )
-            self._tasks.append(task)
+        self._task = task
 
     @imperative_base.no_grad
     def scale_and_split_grads(self):
-        for task in self._tasks:
-            task.wait()
+        assert self._task is not None
+        self._task.wait()
 
         scale_factor = 1.0 / self._comm_group.nranks
-        for coalesced_grad, _, _ in self._coalesced_grads_and_grad_vars:
-            coalesced_grad.scale_(scale_factor)
+        self.grad_storage.scale_(scale_factor)
 
-        _split_tensors(self._coalesced_grads_and_grad_vars)
         self._reset_params_checked_in()
 
 

python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_storage.py

@@ -315,6 +315,11 @@ class GradStorage(InternalStorage):
         assert (
             param._numel() > 0
         ), "Cannot add a gradient to a released InternalStorage, please rebuild"
+
+        use_main_grad = hasattr(param, "main_grad")
+        if use_main_grad:
+            assert self.buffer.dtype == paddle.float32
+        else:
             assert param.dtype == self.buffer.dtype
 
         grad_end = self._fill + param._numel()
@@ -325,7 +330,10 @@ class GradStorage(InternalStorage):
         with device_guard(self.dev_id, self._device):
             tmp_var = self.buffer._slice(self._fill, grad_end)
             tmp_var.get_tensor()._set_dims(param.shape)
+            if not use_main_grad:
                 param._copy_gradient_from(tmp_var)
+            else:
+                param.main_grad = tmp_var
             del tmp_var
 
         self._fill = offset