mirror of https://github.com/InternLM/InternLM
208 lines
6.9 KiB
Python
208 lines
6.9 KiB
Python
#!/usr/bin/env python
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# -*- encoding: utf-8 -*-
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from typing import Optional
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import torch
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import torch.nn.functional as F
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from flash_attn.ops.fused_dense import FusedDenseFunc
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from flash_attn.utils.distributed import (
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all_gather_raw,
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all_reduce_raw,
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reduce_scatter_raw,
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)
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from torch import Tensor
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from torch.cuda.amp import custom_bwd
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from torch.distributed import ProcessGroup
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from internlm.core.context import global_context as gpc
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from internlm.utils.logger import get_logger
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logger = get_logger(__file__)
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def _split(input_, parallel_mode, dim=-1):
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# skip if only one rank involved
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world_size = gpc.get_world_size(parallel_mode)
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if world_size == 1:
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return input_
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# Split along last dimension.
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dim_size = input_.size(dim)
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assert dim_size % world_size == 0, (
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f"The dimension to split ({dim_size}) is not a multiple of world size ({world_size}), "
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f"cannot split tensor evenly"
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)
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tensor_list = torch.split(input_, dim_size // world_size, dim=dim)
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rank = gpc.get_local_rank(parallel_mode)
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output = tensor_list[rank].contiguous()
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return output
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def _gather(input_, parallel_mode, dim=-1):
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# skip if only one rank involved
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world_size = gpc.get_world_size(parallel_mode)
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if world_size == 1:
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return input_
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# all gather
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rank = gpc.get_local_rank(parallel_mode)
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tensor_list = [torch.empty_like(input_) for _ in range(world_size)]
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tensor_list[rank] = input_
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group = gpc.get_cpu_group(parallel_mode) if input_.device.type == "cpu" else gpc.get_group(parallel_mode)
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torch.distributed.all_gather(tensor_list, input_, group=group)
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# concat
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output = torch.cat(tensor_list, dim=dim).contiguous()
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return output
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class _GatherForwardSplitBackward(torch.autograd.Function):
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"""Gather the input from model parallel region and concatenate.
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Args:
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input_: input matrix.
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parallel_mode: parallel mode.
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dim: dimension
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"""
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@staticmethod
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def symbolic(input_):
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return _gather(input_, parallel_mode=None)
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@staticmethod
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def forward(ctx, input_, parallel_mode, dim):
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ctx.mode = parallel_mode
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ctx.dim = dim
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return _gather(input_, parallel_mode, dim)
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@staticmethod
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def backward(ctx, grad_output):
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return _split(grad_output, ctx.mode, ctx.dim), None, None
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def gather_forward_split_backward(input_, parallel_mode, dim):
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return _GatherForwardSplitBackward.apply(input_, parallel_mode, dim)
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def linear_bias_wgrad_torch(input, grad_output, has_d_bias):
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assert input.dtype == grad_output.dtype
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grad_weight = torch.matmul(grad_output.t(), input)
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grad_bias = grad_output.sum(dim=0) if has_d_bias else None
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return grad_weight, grad_bias
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# adpated from https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/ops/fused_dense.py
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class FusedDenseFuncTorch(FusedDenseFunc):
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@staticmethod
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@custom_bwd
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def backward(ctx, grad_output, *args):
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grad_output = grad_output.contiguous()
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if ctx.return_residual:
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(grad_input,) = args
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grad_input = grad_input.contiguous()
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process_group = ctx.process_group
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sequence_parallel = ctx.sequence_parallel
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if ctx.compute_weight_gradient:
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x, weight = ctx.saved_tensors
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if process_group is not None and sequence_parallel:
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total_x, handle_x = all_gather_raw(x, process_group, async_op=True)
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else:
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total_x = x
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else:
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(weight,) = ctx.saved_tensors
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total_x = None
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batch_shape = grad_output.shape[:-1]
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batch_dim = batch_shape.numel()
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grad_output = grad_output.reshape(batch_dim, grad_output.shape[-1])
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if ctx.needs_input_grad[0]:
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if not ctx.return_residual:
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grad_input = F.linear(grad_output, weight.t())
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else:
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grad_input = torch.addmm(grad_input.reshape(batch_dim, grad_input.shape[-1]), grad_output, weight)
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grad_input = grad_input.reshape(*batch_shape, grad_input.shape[-1])
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if process_group is not None:
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reduce_fn = reduce_scatter_raw if sequence_parallel else all_reduce_raw
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grad_input, handle_grad_input = reduce_fn(grad_input, process_group, async_op=True)
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else:
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grad_input = None
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if ctx.needs_input_grad[1]:
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assert ctx.compute_weight_gradient
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if process_group is not None and sequence_parallel:
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handle_x.wait()
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# we remove the cuda independence, which is different from flash_attn.
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grad_weight, grad_bias = linear_bias_wgrad_torch(
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total_x.reshape(batch_dim, total_x.shape[-1]), grad_output, ctx.needs_input_grad[2]
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)
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else:
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grad_weight = None
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grad_bias = grad_output if ctx.needs_input_grad[2] else None
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if process_group is not None and ctx.needs_input_grad[0]:
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handle_grad_input.wait()
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return grad_input, grad_weight, grad_bias, None, None, None
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def fused_dense_func_torch(
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x: Tensor,
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weight: Tensor,
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bias: Optional[Tensor] = None,
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return_residual: bool = False,
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process_group: Optional[ProcessGroup] = None,
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sequence_parallel: bool = True,
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):
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dtype_eligible = x.dtype in [torch.float16, torch.bfloat16] or (
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x.dtype == torch.float32 and torch.is_autocast_enabled()
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)
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if x.is_cuda and weight.is_cuda and (bias is None or bias.is_cuda) and dtype_eligible:
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return FusedDenseFunc.apply(x, weight, bias, return_residual, process_group, sequence_parallel)
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else:
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return FusedDenseFuncTorch.apply(x, weight, bias, return_residual, process_group, sequence_parallel)
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class _SplitForwardGatherBackward(torch.autograd.Function):
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"""
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Split the input and keep only the corresponding chuck to the rank.
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Args:
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input_: input matrix.
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parallel_mode: parallel mode.
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dim: dimension
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"""
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@staticmethod
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def symbolic(graph, input_):
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return _split(input_)
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@staticmethod
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def forward(ctx, input_, parallel_mode, dim):
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ctx.mode = parallel_mode
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ctx.dim = dim
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return _split(input_, parallel_mode, dim)
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@staticmethod
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def backward(ctx, grad_output):
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return _gather(grad_output, ctx.mode, ctx.dim), None, None
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def split_forward_gather_backward(input_, parallel_mode, dim):
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return _SplitForwardGatherBackward.apply(input_, parallel_mode, dim)
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def try_import_RMSNorm():
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"""
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Try import MixFusedRMSNorm from apex, if failed, return our RMSNorm
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"""
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try:
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from apex.normalization.fused_layer_norm import MixedFusedRMSNorm as RMSNorm
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return RMSNorm
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except ModuleNotFoundError:
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logger.warn("The torch implementation for MixFusedRMSNorm is slower than apex. Please note this!")
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from internlm.model.norm import RMSNormTorch as RMSNorm
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return RMSNorm
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