[tensorparallel] fixed tp layers (#1938)

colossalai/nn/layer/parallel_1d/layers.py

@@ -77,12 +77,11 @@ class Linear1D(ColossalaiModule):
                  weight_initializer: Callable = init.kaiming_uniform_(a=math.sqrt(5)),
                  bias_initializer: Callable = init.xavier_uniform_(a=1, scale=1)):
         parallel_input = get_parallel_input()
-        if not parallel_input:
+        if not parallel_input and not gather_output:
             layer = Linear1D_Col(in_features,
                                  out_features,
                                  bias=bias,
                                  dtype=dtype,
-                                 gather_output=gather_output,
                                  skip_bias_add=skip_bias_add,
                                  weight_initializer=weight_initializer,
                                  bias_initializer=bias_initializer)

colossalai/nn/layer/parallel_3d/_operation.py

@@ -4,13 +4,15 @@
 from typing import Optional, Tuple
 
 import torch
-from colossalai.communication import (all_gather, all_reduce, broadcast, reduce, reduce_scatter)
-from colossalai.context.parallel_mode import ParallelMode
-from colossalai.core import global_context as gpc
 from torch import Tensor
 from torch.cuda.amp import custom_bwd, custom_fwd
-from ._utils import get_parallel_mode_from_env, push_async_grad
+
+from colossalai.communication import all_gather, all_reduce, broadcast, reduce, reduce_scatter
 from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
+from colossalai.context.parallel_mode import ParallelMode
+from colossalai.core import global_context as gpc
+
+from ._utils import get_parallel_mode_from_env, push_async_grad
 
 
 class _Linear3D(torch.autograd.Function):
@@ -44,18 +46,17 @@ class _Linear3D(torch.autograd.Function):
     @custom_bwd
     def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
         input_, weight = ctx.saved_tensors
-        with torch.no_grad():
-            output_grad = all_gather(output_grad, 0, ctx.output_parallel_mode)
+        output_grad = all_gather(output_grad, 0, ctx.output_parallel_mode)
 
-            input_grad = torch.matmul(output_grad, weight.transpose(0, 1))
-            input_grad, input_op = reduce_scatter(input_grad, 0, ctx.input_parallel_mode, async_op=True)
+        input_grad = torch.matmul(output_grad, weight.transpose(0, 1))
+        input_grad, input_op = reduce_scatter(input_grad, 0, ctx.input_parallel_mode, async_op=True)
 
-            weight_grad = torch.matmul(
-                input_.reshape(-1, input_.shape[-1]).transpose(0, 1), output_grad.reshape(-1, output_grad.shape[-1]))
-            weight_grad, op = reduce_scatter(weight_grad, -1, ctx.weight_parallel_mode, async_op=True)
-            weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
+        weight_grad = torch.matmul(
+            input_.reshape(-1, input_.shape[-1]).transpose(0, 1), output_grad.reshape(-1, output_grad.shape[-1]))
+        weight_grad, op = reduce_scatter(weight_grad, -1, ctx.weight_parallel_mode, async_op=True)
+        weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
 
-            input_op.wait()
+        input_op.wait()
 
         return input_grad, weight_grad, None, None, None, None
 
@@ -129,25 +130,24 @@ class _Classifier3D(torch.autograd.Function):
     @custom_bwd
     def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
         input_, weight = ctx.saved_tensors
-        with torch.no_grad():
-            weight_grad = torch.matmul(
-                output_grad.reshape(-1, output_grad.shape[-1]).transpose(0, 1), input_.reshape(-1, input_.shape[-1]))
-            weight_grad = reduce(weight_grad, ctx.src_rank, ctx.input_parallel_mode)
-            if gpc.get_local_rank(ctx.input_parallel_mode) == gpc.get_local_rank(ctx.output_parallel_mode):
-                weight_grad, op = all_reduce(weight_grad, ctx.weight_parallel_mode, async_op=True)
-                weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
-            else:
-                weight_grad = None
-
-            if ctx.use_bias:
-                bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
-                bias_grad = all_reduce(bias_grad, ctx.input_parallel_mode)
-                bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
-                bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
-            else:
-                bias_grad = None
-
-            input_grad = torch.matmul(output_grad, weight)
+        weight_grad = torch.matmul(
+            output_grad.reshape(-1, output_grad.shape[-1]).transpose(0, 1), input_.reshape(-1, input_.shape[-1]))
+        weight_grad = reduce(weight_grad, ctx.src_rank, ctx.input_parallel_mode)
+        if gpc.get_local_rank(ctx.input_parallel_mode) == gpc.get_local_rank(ctx.output_parallel_mode):
+            weight_grad, op = all_reduce(weight_grad, ctx.weight_parallel_mode, async_op=True)
+            weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
+        else:
+            weight_grad = None
+
+        if ctx.use_bias:
+            bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
+            bias_grad = all_reduce(bias_grad, ctx.input_parallel_mode)
+            bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
+            bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
+        else:
+            bias_grad = None
+
+        input_grad = torch.matmul(output_grad, weight)
 
         return input_grad, weight_grad, bias_grad, None, None, None, None, None
 
@@ -224,25 +224,24 @@ class _VocabParallelClassifier3D(torch.autograd.Function):
     @custom_bwd
     def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
         input_, weight = ctx.saved_tensors
-        with torch.no_grad():
-            output_grad = all_gather(output_grad, 0, ctx.output_parallel_mode)
+        output_grad = all_gather(output_grad, 0, ctx.output_parallel_mode)
 
-            input_grad = torch.matmul(output_grad, weight.transpose(0, 1))
-            input_grad, input_op = reduce_scatter(input_grad, 0, ctx.input_parallel_mode, async_op=True)
+        input_grad = torch.matmul(output_grad, weight.transpose(0, 1))
+        input_grad, input_op = reduce_scatter(input_grad, 0, ctx.input_parallel_mode, async_op=True)
 
-            weight_grad = torch.matmul(
-                input_.reshape(-1, input_.shape[-1]).transpose(0, 1), output_grad.reshape(-1, output_grad.shape[-1]))
-            weight_grad, op = reduce_scatter(weight_grad.transpose(0, 1), 0, ctx.weight_parallel_mode, async_op=True)
-            weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
+        weight_grad = torch.matmul(
+            input_.reshape(-1, input_.shape[-1]).transpose(0, 1), output_grad.reshape(-1, output_grad.shape[-1]))
+        weight_grad, op = reduce_scatter(weight_grad.transpose(0, 1), 0, ctx.weight_parallel_mode, async_op=True)
+        weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
 
-            if ctx.use_bias:
-                bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
-                bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
-                bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
-            else:
-                bias_grad = None
+        if ctx.use_bias:
+            bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
+            bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
+            bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
+        else:
+            bias_grad = None
 
-            input_op.wait()
+        input_op.wait()
 
         return input_grad, weight_grad, bias_grad, None, None, None, None, None
 
@@ -281,6 +280,30 @@ def vocab_parallel_classifier_3d(
     )
 
 
+@torch.jit.script
+def norm_forward(x, mean, sqr_mean, weight, bias, eps):
+    mu = x - mean
+    var = sqr_mean - mean**2
+    sigma = torch.sqrt(var + eps)
+    z = mu / sigma
+    output = weight * z + bias
+
+    return output, mu, sigma
+
+
+@torch.jit.script
+def norm_backward(grad, mu, sigma, weight):
+    # dbias, dweight = grad, grad * mu / sigma
+    dz = grad * weight
+    dmu = dz / sigma
+    dvar = dz * mu * (-0.5) * sigma**(-3)
+    dmean = -dmu
+    dvar = torch.sum(dvar, -1, keepdim=True)
+    dmean = torch.sum(dmean, -1, keepdim=True)
+
+    return dmu, dmean, dvar
+
+
 class _Layernorm3D(torch.autograd.Function):
 
     @staticmethod
@@ -294,27 +317,21 @@ class _Layernorm3D(torch.autograd.Function):
         bias_id: int,
         normalized_shape: int,
         eps: float,
-        input_parallel_mode: ParallelMode,
-        weight_parallel_mode: ParallelMode,
         output_parallel_mode: ParallelMode,
         input_x_weight_parallel_mode: ParallelMode,
     ) -> Tensor:
         ctx.weight_id = weight_id
         ctx.bias_id = bias_id
 
-        mean = all_reduce(torch.sum(input_, dim=-1, keepdim=True), output_parallel_mode) / normalized_shape
-        mu = input_ - mean
-        var = all_reduce(torch.sum(mu**2, dim=-1, keepdim=True), output_parallel_mode) / normalized_shape
-        sigma = torch.sqrt(var + eps)
+        sum_ = torch.sum(input_, dim=-1, keepdim=True)
+        sqr_sum = torch.sum(input_**2, dim=-1, keepdim=True)
+        mean, sqr_mean = all_reduce(torch.stack((sum_, sqr_sum)), output_parallel_mode) / normalized_shape
 
-        ctx.save_for_backward(mu, sigma, weight)
+        output, mu, sigma = norm_forward(input_, mean, sqr_mean, weight, bias, eps)
 
-        z = mu / sigma
-        output = weight * z + bias
+        ctx.save_for_backward(mu, sigma, weight)
 
         ctx.normalized_shape = normalized_shape
-        ctx.input_parallel_mode = input_parallel_mode
-        ctx.weight_parallel_mode = weight_parallel_mode
         ctx.output_parallel_mode = output_parallel_mode
         ctx.input_x_weight_parallel_mode = input_x_weight_parallel_mode
 
@@ -324,23 +341,18 @@ class _Layernorm3D(torch.autograd.Function):
     @custom_bwd
     def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
         mu, sigma, weight = ctx.saved_tensors
-        with torch.no_grad():
 
-            bias_grad, weight_grad = output_grad, output_grad * mu / sigma
-            bias_grad = torch.sum(bias_grad, dim=tuple(range(len(bias_grad.shape))[:-1]))
-            bias_grad, op = all_reduce(bias_grad, ctx.input_x_weight_parallel_mode, async_op=True)
-            bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
-            weight_grad = torch.sum(weight_grad, dim=tuple(range(len(weight_grad.shape))[:-1]))
-            weight_grad, op = all_reduce(weight_grad, ctx.input_x_weight_parallel_mode, async_op=True)
-            weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
-
-            dz = output_grad * weight
-            dvar = dz * mu * (-0.5) * sigma**(-3)
-            dvar = all_reduce(torch.sum(dvar, dim=-1, keepdim=True), ctx.output_parallel_mode)
-            dmean = dz * (-1 / sigma) + dvar * -2 * mu / ctx.normalized_shape
-            dmean = all_reduce(torch.sum(dmean, dim=-1, keepdim=True), ctx.output_parallel_mode)
+        bias_grad, weight_grad = output_grad, output_grad * mu / sigma
+        bias_grad = torch.sum(bias_grad, dim=tuple(range(len(bias_grad.shape))[:-1]))
+        bias_grad, op = all_reduce(bias_grad, ctx.input_x_weight_parallel_mode, async_op=True)
+        bias_grad = push_async_grad(op, bias_grad, ctx.bias_id)
+        weight_grad = torch.sum(weight_grad, dim=tuple(range(len(weight_grad.shape))[:-1]))
+        weight_grad, op = all_reduce(weight_grad, ctx.input_x_weight_parallel_mode, async_op=True)
+        weight_grad = push_async_grad(op, weight_grad, ctx.weight_id)
 
-            input_grad = dz / sigma + dvar * 2 * mu / ctx.normalized_shape + dmean / ctx.normalized_shape
+        dmu, dmean, dvar = norm_backward(output_grad, mu, sigma, weight)
+        dvar, dmean = all_reduce(torch.stack((dvar, dmean)), ctx.output_parallel_mode)
+        input_grad = dmu + (dmean + 2 * dvar * mu) / ctx.normalized_shape
 
         return input_grad, weight_grad, bias_grad, None, None, None, None, None, None, None, None
 
@@ -351,8 +363,6 @@ def layernorm_3d(
     bias: Tensor,
     normalized_shape: int,
     eps: float,
-    input_parallel_mode: ParallelMode,
-    weight_parallel_mode: ParallelMode,
     output_parallel_mode: ParallelMode,
     input_x_weight_parallel_mode: ParallelMode,
 ) -> Tensor:
@@ -368,9 +378,8 @@ def layernorm_3d(
             If a single integer is used, it is treated as a singleton list, and this module will
             normalize over the last dimension which is expected to be of that specific size.
         eps (float): a value added to the denominator for numerical stability
-        input_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input parallel mode.
-        weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): weight parallel mode.
         output_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): output parallel mode.
+        input_x_weight_parallel_mode (:class:`colossalai.context.parallel_mode.ParallelMode`): input x weight parallel mode.
 
     Note:
         The parallel_mode should be concluded in ``ParallelMode``. More details about ``ParallelMode`` could be found
@@ -384,8 +393,6 @@ def layernorm_3d(
         id(bias),
         normalized_shape,
         eps,
-        input_parallel_mode,
-        weight_parallel_mode,
         output_parallel_mode,
         input_x_weight_parallel_mode,
     )

colossalai/nn/layer/parallel_3d/layers.py

@@ -5,6 +5,9 @@ from typing import Callable
 import torch
 import torch.nn as nn
 import torch.nn.functional as F
+from torch import Tensor
+from torch.nn import Parameter
+
 from colossalai.communication import all_reduce, broadcast
 from colossalai.constants import INPUT_GROUP_3D, INPUT_X_WEIGHT_3D, OUTPUT_GROUP_3D, OUTPUT_X_WEIGHT_3D, WEIGHT_GROUP_3D
 from colossalai.context import ParallelMode, seed
@@ -13,16 +16,25 @@ from colossalai.global_variables import tensor_parallel_env as env
 from colossalai.nn import init as init
 from colossalai.nn.layer.base_layer import ParallelLayer
 from colossalai.registry import LAYERS
-from colossalai.utils.checkpointing import (broadcast_state_dict, gather_tensor_parallel_state_dict,
-                                            partition_tensor_parallel_state_dict)
+from colossalai.utils.checkpointing import (
+    broadcast_state_dict,
+    gather_tensor_parallel_state_dict,
+    partition_tensor_parallel_state_dict,
+)
 from colossalai.utils.cuda import get_current_device
-from torch import Tensor
-from torch.nn import Parameter
 
 from ..utils import divide, set_tensor_parallel_attribute_by_partition, to_2tuple
-from ._operation import (all_gather_tensor_3d, classifier_3d, vocab_parallel_classifier_3d, layernorm_3d, linear_3d,
-                         reduce_scatter_tensor_3d, split_tensor_3d, split_batch_3d)
-from ._utils import get_depth_from_env, get_parallel_mode_from_env, swap_in_out_group, register_async_grad_hook
+from ._operation import (
+    all_gather_tensor_3d,
+    classifier_3d,
+    layernorm_3d,
+    linear_3d,
+    reduce_scatter_tensor_3d,
+    split_batch_3d,
+    split_tensor_3d,
+    vocab_parallel_classifier_3d,
+)
+from ._utils import get_depth_from_env, get_parallel_mode_from_env, register_async_grad_hook, swap_in_out_group
 
 
 @LAYERS.register_module
@@ -144,8 +156,6 @@ class LayerNorm3D(ParallelLayer):
             self.bias,
             self.normalized_shape,
             self.variance_epsilon,
-            self.input_parallel_mode,
-            self.weight_parallel_mode,
             self.output_parallel_mode,
             self.input_x_weight_parallel_mode,
         )
@@ -900,7 +910,7 @@ class PatchEmbedding3D(ParallelLayer):
                                 weight_parallel_mode=self.weight_parallel_mode)
         output = F.conv2d(input_, self.weight, self.bias, stride=self.patch_size)
         if self.flatten:
-            output = output.flatten(2).transpose(1, 2)  # BCHW -> BNC
+            output = output.flatten(2).transpose(1, 2)    # BCHW -> BNC
 
         cls_token = self.cls_token.expand(output.shape[0], -1, -1)
         output = torch.cat((cls_token, output), dim=1)