mirror of https://github.com/hpcaitech/ColossalAI
482 lines
19 KiB
Python
482 lines
19 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from typing import Optional, Tuple
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import torch
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from colossalai.communication import (all_gather, all_reduce, broadcast, reduce, reduce_scatter)
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from colossalai.context.parallel_mode import ParallelMode
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from colossalai.core import global_context as gpc
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from torch import Tensor
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from torch.cuda.amp import custom_bwd, custom_fwd
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from ._utils import get_parallel_mode_from_env
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from colossalai.constants import INPUT_GROUP_3D, WEIGHT_GROUP_3D
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class _Linear3D(torch.autograd.Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float16)
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def forward(ctx,
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input_: Tensor,
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weight: Tensor,
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bias: Optional[Tensor],
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input_parallel_mode: ParallelMode,
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weight_parallel_mode: ParallelMode,
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output_parallel_mode: ParallelMode,
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input_dim: int = 0,
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weight_dim: int = -1,
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output_dim: int = 0) -> Tensor:
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ctx.use_bias = bias is not None
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input_ = all_gather(input_, input_dim, input_parallel_mode)
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weight = all_gather(weight, weight_dim, weight_parallel_mode)
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ctx.save_for_backward(input_, weight)
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output = torch.matmul(input_, weight)
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output = reduce_scatter(output, output_dim, output_parallel_mode)
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if bias is not None:
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output += bias
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ctx.input_parallel_mode = input_parallel_mode
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ctx.weight_parallel_mode = weight_parallel_mode
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ctx.output_parallel_mode = output_parallel_mode
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ctx.input_dim = input_dim
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ctx.weight_dim = weight_dim
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ctx.output_dim = output_dim
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return output
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@staticmethod
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@custom_bwd
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def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
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input_, weight = ctx.saved_tensors
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with torch.no_grad():
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output_grad = all_gather(output_grad, ctx.output_dim, ctx.output_parallel_mode)
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async_ops = list()
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input_grad = torch.matmul(output_grad, weight.transpose(0, 1))
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input_grad, op = reduce_scatter(input_grad, ctx.input_dim, ctx.input_parallel_mode, async_op=True)
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async_ops.append(op)
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weight_grad = torch.matmul(
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input_.reshape(-1, input_.shape[-1]).transpose(0, 1), output_grad.reshape(-1, output_grad.shape[-1]))
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weight_grad, op = reduce_scatter(weight_grad, ctx.weight_dim, ctx.weight_parallel_mode, async_op=True)
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async_ops.append(op)
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if ctx.use_bias:
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bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
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bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
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async_ops.append(op)
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else:
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bias_grad = None
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for op in async_ops:
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if op is not None:
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op.wait()
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return input_grad, weight_grad, bias_grad, None, None, None, None, None, None
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def linear_3d(input_: Tensor,
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weight: Tensor,
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bias: Optional[Tensor],
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input_parallel_mode: ParallelMode,
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weight_parallel_mode: ParallelMode,
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output_parallel_mode: ParallelMode,
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input_dim: int = 0,
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weight_dim: int = -1,
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output_dim: int = 0) -> Tensor:
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"""
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Linear layer for 3D parallelism
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:param input_: matrix of input
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:type input_: torch.tensor
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:param weight: matrix of weight
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:type weight: torch.tensor
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:param bias: matrix of bias
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:type bias: torch.tensor, optional
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:param input_parallel_mode: input parallel mode
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:type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param weight_parallel_mode: weight parallel mode
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:type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param output_parallel_mode: output parallel mode
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:type output_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param input_dim: dimension of input, defaults to 0
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:type input_dim: int, optional
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:param weight_dim: dimension of weight, defaults to -1
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:type weight_dim: int, optional
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:param output_dim: dimension of output, defaults to 0
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:type output_dim: int, optional
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"""
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return _Linear3D.apply(input_, weight, bias, input_parallel_mode, weight_parallel_mode, output_parallel_mode,
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input_dim, weight_dim, output_dim)
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class _Classifier3D(torch.autograd.Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float16)
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def forward(ctx, input_: Tensor, weight: Tensor, bias: Optional[Tensor], input_parallel_mode: ParallelMode,
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weight_parallel_mode: ParallelMode, output_parallel_mode: ParallelMode) -> Tensor:
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ctx.use_bias = bias is not None
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ranks_in_group = gpc.get_ranks_in_group(input_parallel_mode)
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src_rank = ranks_in_group[gpc.get_local_rank(output_parallel_mode)]
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weight = broadcast(weight, src_rank, input_parallel_mode)
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ctx.save_for_backward(input_, weight)
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output = torch.matmul(input_, weight.transpose(0, 1))
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output = all_reduce(output, output_parallel_mode)
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if bias is not None:
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output += bias
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ctx.src_rank = src_rank
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ctx.input_parallel_mode = input_parallel_mode
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ctx.weight_parallel_mode = weight_parallel_mode
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ctx.output_parallel_mode = output_parallel_mode
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return output
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@staticmethod
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@custom_bwd
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def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
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input_, weight = ctx.saved_tensors
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with torch.no_grad():
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async_ops = list()
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weight_grad = torch.matmul(
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output_grad.reshape(-1, output_grad.shape[-1]).transpose(0, 1), input_.reshape(-1, input_.shape[-1]))
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weight_grad = reduce(weight_grad, ctx.src_rank, ctx.input_parallel_mode)
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if gpc.get_local_rank(ctx.input_parallel_mode) == gpc.get_local_rank(ctx.output_parallel_mode):
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weight_grad, op = all_reduce(weight_grad, ctx.weight_parallel_mode, async_op=True)
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async_ops.append(op)
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else:
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weight_grad = None
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if ctx.use_bias:
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bias_grad = torch.sum(output_grad, dim=tuple(range(len(output_grad.shape))[:-1]))
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bias_grad = all_reduce(bias_grad, ctx.input_parallel_mode)
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bias_grad, op = all_reduce(bias_grad, ctx.weight_parallel_mode, async_op=True)
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async_ops.append(op)
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else:
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bias_grad = None
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input_grad = torch.matmul(output_grad, weight)
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for op in async_ops:
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if op is not None:
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op.wait()
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return input_grad, weight_grad, bias_grad, None, None, None, None, None, None
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def classifier_3d(input_: Tensor, weight: Tensor, bias: Optional[Tensor], input_parallel_mode: ParallelMode,
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weight_parallel_mode: ParallelMode, output_parallel_mode: ParallelMode) -> Tensor:
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"""
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3D parallel classifier
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:param input_: matrix of input
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:type input_: torch.tensor
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:param weight: matrix of weight
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:type weight: torch.tensor
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:param bias: matrix of bias
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:type bias: torch.tensor, optional
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:param input_parallel_mode: input parallel mode
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:type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param weight_parallel_mode: weight parallel mode
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:type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param output_parallel_mode: output parallel mode
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:type output_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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"""
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return _Classifier3D.apply(input_, weight, bias, input_parallel_mode, weight_parallel_mode, output_parallel_mode)
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class _Layernorm3D(torch.autograd.Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx, input_: Tensor, weight: Tensor, bias: Tensor, normalized_shape: int, eps: float,
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input_parallel_mode: ParallelMode, weight_parallel_mode: ParallelMode,
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output_parallel_mode: ParallelMode) -> Tensor:
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mean = all_reduce(torch.sum(input_, dim=-1, keepdim=True), output_parallel_mode) / normalized_shape
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mu = input_ - mean
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var = all_reduce(torch.sum(mu**2, dim=-1, keepdim=True), output_parallel_mode) / normalized_shape
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sigma = torch.sqrt(var + eps)
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ctx.save_for_backward(mu, sigma, weight)
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z = mu / sigma
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output = weight * z + bias
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ctx.normalized_shape = normalized_shape
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ctx.input_parallel_mode = input_parallel_mode
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ctx.weight_parallel_mode = weight_parallel_mode
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ctx.output_parallel_mode = output_parallel_mode
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return output
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@staticmethod
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@custom_bwd
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def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
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mu, sigma, weight = ctx.saved_tensors
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with torch.no_grad():
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bias_grad, weight_grad = output_grad, output_grad * mu / sigma
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grads = torch.stack([bias_grad, weight_grad]).contiguous()
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grads = torch.sum(grads, dim=tuple(range(len(grads.shape))[1:-1]))
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grads = all_reduce(grads, ctx.weight_parallel_mode)
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grads = all_reduce(grads, ctx.input_parallel_mode)
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bias_grad, weight_grad = grads[0], grads[1]
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dz = output_grad * weight
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dvar = dz * mu * (-0.5) * sigma**(-3)
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dvar = all_reduce(torch.sum(dvar, dim=-1, keepdim=True), ctx.output_parallel_mode)
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dmean = dz * (-1 / sigma) + dvar * -2 * mu / ctx.normalized_shape
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dmean = all_reduce(torch.sum(dmean, dim=-1, keepdim=True), ctx.output_parallel_mode)
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input_grad = dz / sigma + dvar * 2 * mu / \
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ctx.normalized_shape + dmean / ctx.normalized_shape
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return input_grad, weight_grad, bias_grad, None, None, None, None, None
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def layernorm_3d(input_: Tensor, weight: Tensor, bias: Tensor, normalized_shape: int, eps: float,
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input_parallel_mode: ParallelMode, weight_parallel_mode: ParallelMode,
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output_parallel_mode: ParallelMode) -> Tensor:
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"""
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3D parallel Layernorm
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:param input_: input maxtrix
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:type input_: torch.tensor
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:param weight: matrix of weight
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:type weight: torch.tensor
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:param bias: matrix of bias
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:type bias: torch.tensor
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:param normalized_shape: input shape from an expected input
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of size. :math:`[* \times \text{normalized_shape}[0] \times \text{normalized_shape}[1] \times \ldots \times \text{normalized_shape}[-1]]`
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If a single integer is used, it is treated as a singleton list, and this module will
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normalize over the last dimension which is expected to be of that specific size.
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:type normalized_shape: int
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:param eps: a value added to the denominator for numerical stability
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:type eps: float
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:param input_parallel_mode: input parallel mode
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:type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param weight_parallel_mode: weight parallel mode
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:type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param output_parallel_mode: output parallel mode
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:type output_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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"""
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return _Layernorm3D.apply(input_, weight, bias, normalized_shape, eps, input_parallel_mode, weight_parallel_mode,
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output_parallel_mode)
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def split_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
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"""Splits 3D parallel tensor in specified dimension
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:param tensor: Input tensor
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:param dim: Specified dimension in which to split
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:param parallel_mode: Parallel mode
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:param weight_parallel_mode: Weight parallel mode
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:type tensor: torch.Tensor
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:type dim: int
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:type parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:return output: Splitted tensor
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:rtype output: torch.Tensor
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"""
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if tensor.size(dim) <= 1:
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return tensor
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output = torch.chunk(tensor, gpc.get_world_size(parallel_mode),
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dim=dim)[gpc.get_local_rank(parallel_mode)].contiguous()
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return output
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def split_batch_3d(input_: Tensor,
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dim: int = 0,
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input_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_INPUT,
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weight_parallel_mode: ParallelMode = ParallelMode.PARALLEL_3D_WEIGHT) -> Tensor:
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"""Splits 3D tensor in batch
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:param input_: Input tensor
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:param dim: Specified dimension in which to split
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:param input_parallel_mode: Input parallel mode
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:param weight_parallel_mode: Weight parallel mode
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:type input_: torch.Tensor
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:type dim: int, optional
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:type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode, optional
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:type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode, optional
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:return output: Splitted tensor
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:rtype output: torch.Tensor
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"""
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if input_.size(dim) <= 1:
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return input_
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weight_parallel_mode = get_parallel_mode_from_env(WEIGHT_GROUP_3D)
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input_parallel_mode = get_parallel_mode_from_env(INPUT_GROUP_3D)
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output = torch.chunk(input_, gpc.get_world_size(weight_parallel_mode),
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dim=dim)[gpc.get_local_rank(weight_parallel_mode)].contiguous()
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output = torch.chunk(output, gpc.get_world_size(input_parallel_mode),
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dim=dim)[gpc.get_local_rank(input_parallel_mode)].contiguous()
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return output
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class _ReduceTensor3D(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input_, parallel_mode):
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return all_reduce(input_, parallel_mode)
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@staticmethod
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def backward(ctx, output_grad):
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return output_grad, None
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def reduce_tensor_3d(tensor: Tensor, parallel_mode: ParallelMode) -> Tensor:
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"""
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All-reduce the input.
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:param tensor: Input tensor
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:param parallel_mode: Parallel mode
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"""
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return _ReduceTensor3D.apply(tensor, parallel_mode)
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class _AllGatherTensor3D(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input_, dim, parallel_mode):
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ctx.dim = dim
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ctx.parallel_mode = parallel_mode
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output = all_gather(input_, dim, parallel_mode)
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return output
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@staticmethod
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def backward(ctx, output_grad):
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input_grad = reduce_scatter(output_grad, ctx.dim, ctx.parallel_mode)
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return input_grad, None, None
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def all_gather_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
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"""
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All-reduce the gradient in backward pass.
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:param tensor: Input tensor
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:param parallel_mode: Parallel mode
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"""
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return _AllGatherTensor3D.apply(tensor, dim, parallel_mode)
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class _ReduceScatterTensor3D(torch.autograd.Function):
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@staticmethod
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def forward(ctx, input_, dim, parallel_mode):
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ctx.dim = dim
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ctx.parallel_mode = parallel_mode
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return reduce_scatter(input_, dim, parallel_mode)
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@staticmethod
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def backward(ctx, output_grad):
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input_grad = all_gather(output_grad, ctx.dim, ctx.parallel_mode)
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return input_grad, None, None
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def reduce_scatter_tensor_3d(tensor: Tensor, dim: int, parallel_mode: ParallelMode) -> Tensor:
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"""
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Reduce-scatter the input.
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:param tensor: Input tensor
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:param dim: Dimension to scatter
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:param parallel_mode: Parallel mode
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"""
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return _ReduceScatterTensor3D.apply(tensor, dim, parallel_mode)
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class _ReduceByBatch3D(torch.autograd.Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float32)
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def forward(ctx,
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input_: Tensor,
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input_parallel_mode: ParallelMode,
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weight_parallel_mode: ParallelMode,
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reduce_mean: bool = False) -> Tensor:
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output = all_reduce(input_, input_parallel_mode)
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output = all_reduce(output, weight_parallel_mode)
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ctx.reduce_mean = reduce_mean
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if reduce_mean:
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reduce_size = gpc.get_world_size(input_parallel_mode) * gpc.get_world_size(weight_parallel_mode)
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ctx.reduce_size = reduce_size
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return output.clone() / reduce_size
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return output.clone()
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@staticmethod
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@custom_bwd
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def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
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if ctx.reduce_mean:
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return output_grad / ctx.reduce_size, None, None, None
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else:
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return output_grad, None, None, None
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def reduce_by_batch_3d(tensor: Tensor,
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input_parallel_mode: ParallelMode,
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weight_parallel_mode: ParallelMode,
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reduce_mean: bool = False) -> Tensor:
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"""
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All-reduce the input from the model parallel region.
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:param input_: input maxtrix
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:type input_: torch.tensor
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:param input_parallel_mode: input parallel mode
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:type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param weight_parallel_mode: weight parallel mode
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:type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param reduce_mean: If set to ``True``, it will divide the output by (input parallel size * weight parallel size), default to False
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:type reduce_mean: int, optional
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"""
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return _ReduceByBatch3D.apply(tensor, input_parallel_mode, weight_parallel_mode, reduce_mean)
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class _BroadcastWeight3D_FromDiagonal(torch.autograd.Function):
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"""
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broadcast weight from diagonal
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:param input_: input maxtrix
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:type input_: torch.tensor
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:param input_parallel_mode: input parallel mode
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:type input_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param weight_parallel_mode: weight parallel mode
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:type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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:param weight_parallel_mode: output parallel mode
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:type weight_parallel_mode: colossalai.context.parallel_mode.ParallelMode
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"""
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@staticmethod
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@custom_fwd(cast_inputs=torch.float16)
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def forward(ctx, input_: Tensor, input_parallel_mode: ParallelMode, weight_parallel_mode: ParallelMode,
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output_parallel_mode: ParallelMode) -> Tensor:
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ranks_in_group = gpc.get_ranks_in_group(input_parallel_mode)
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src_rank = ranks_in_group[gpc.get_local_rank(output_parallel_mode)]
|
|
output = broadcast(input_, src_rank, input_parallel_mode)
|
|
ctx.src_rank = src_rank
|
|
ctx.input_parallel_mode = input_parallel_mode
|
|
ctx.weight_parallel_mode = weight_parallel_mode
|
|
ctx.output_parallel_mode = output_parallel_mode
|
|
return output
|
|
|
|
@staticmethod
|
|
@custom_bwd
|
|
def backward(ctx, output_grad: Tensor) -> Tuple[Tensor, ...]:
|
|
input_grad = reduce(output_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):
|
|
input_grad = all_reduce(input_grad, ctx.weight_parallel_mode)
|
|
else:
|
|
input_grad = None
|
|
return input_grad, None, None, None
|
|
|
|
|
|
def broadcast_weight_3d_from_diagonal(tensor: Tensor, input_parallel_mode: ParallelMode,
|
|
weight_parallel_mode: ParallelMode, output_parallel_mode: ParallelMode) -> Tensor:
|
|
return _BroadcastWeight3D_FromDiagonal.apply(tensor, input_parallel_mode, weight_parallel_mode,
|
|
output_parallel_mode)
|