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Update model.py
Optimized Moe Transformer
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felipjah
GitHub
parent
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commit
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@ -1,804 +1,102 @@
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import math
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from dataclasses import dataclass
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from typing import Tuple, Optional, Literal
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import torch
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from torch import nn
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import torch.nn as nn
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import torch.nn.functional as F
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import torch.distributed as dist
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from kernel import act_quant, weight_dequant, fp8_gemm
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world_size = 1
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rank = 0
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block_size = 128
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gemm_impl: Literal["bf16", "fp8"] = "bf16"
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attn_impl: Literal["naive", "absorb"] = "absorb"
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@dataclass
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class ModelArgs:
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"""
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Data class for defining model arguments and hyperparameters.
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Attributes:
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max_batch_size (int): Maximum batch size.
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max_seq_len (int): Maximum sequence length.
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dtype (Literal["bf16", "fp8"]): Data type for computations.
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vocab_size (int): Vocabulary size.
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dim (int): Model dimension.
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inter_dim (int): Intermediate dimension for MLP layers.
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moe_inter_dim (int): Intermediate dimension for MoE layers.
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n_layers (int): Number of transformer layers.
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n_dense_layers (int): Number of dense layers in the model.
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n_heads (int): Number of attention heads.
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n_routed_experts (int): Number of routed experts for MoE layers.
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n_shared_experts (int): Number of shared experts for MoE layers.
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n_activated_experts (int): Number of activated experts in MoE layers.
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n_expert_groups (int): Number of expert groups.
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n_limited_groups (int): Number of limited groups for MoE routing.
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score_func (Literal["softmax", "sigmoid"]): Scoring function for MoE routing.
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route_scale (float): Scaling factor for routing scores.
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q_lora_rank (int): LoRA rank for query projections.
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kv_lora_rank (int): LoRA rank for key-value projections.
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qk_nope_head_dim (int): Dimension for query-key projections without positional embeddings.
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qk_rope_head_dim (int): Dimension for query-key projections with rotary embeddings.
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v_head_dim (int): Dimension for value projections.
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original_seq_len (int): Original sequence length.
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rope_theta (float): Base for rotary positional encoding.
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rope_factor (float): Scaling factor for extended sequence lengths.
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beta_fast (int): Fast beta correction factor.
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beta_slow (int): Slow beta correction factor.
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mscale (float): Scaling factor for extended attention.
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"""
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max_batch_size: int = 8
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max_seq_len: int = 4096 * 4
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dtype: Literal["bf16", "fp8"] = "bf16"
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vocab_size: int = 102400
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dim: int = 2048
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inter_dim: int = 10944
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moe_inter_dim: int = 1408
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n_layers: int = 27
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n_dense_layers: int = 1
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n_heads: int = 16
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# moe
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n_routed_experts: int = 64
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n_shared_experts: int = 2
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n_activated_experts: int = 6
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n_expert_groups: int = 1
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n_limited_groups: int = 1
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score_func: Literal["softmax", "sigmoid"] = "softmax"
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route_scale: float = 1.
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# mla
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q_lora_rank: int = 0
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kv_lora_rank: int = 512
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qk_nope_head_dim: int = 128
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qk_rope_head_dim: int = 64
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v_head_dim: int = 128
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# yarn
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original_seq_len: int = 4096
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rope_theta: float = 10000.0
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rope_factor: float = 40
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beta_fast: int = 32
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beta_slow: int = 1
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mscale: float = 1.
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class ParallelEmbedding(nn.Module):
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"""
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Embedding layer with parallelism support across distributed processes.
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Args:
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vocab_size (int): Vocabulary size.
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dim (int): Embedding dimension.
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"""
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def __init__(self, vocab_size: int, dim: int):
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super().__init__()
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self.vocab_size = vocab_size
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self.dim = dim
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assert vocab_size % world_size == 0
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self.part_vocab_size = (vocab_size // world_size)
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self.vocab_start_idx = rank * self.part_vocab_size
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self.vocab_end_idx = self.vocab_start_idx + self.part_vocab_size
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self.weight = nn.Parameter(torch.empty(self.part_vocab_size, self.dim))
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass for parallel embedding layer.
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Args:
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x (torch.Tensor): Input tensor containing token indices.
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Returns:
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torch.Tensor: Embedded representations.
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Raises:
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ValueError: If `world_size` is not defined.
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"""
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if world_size > 1:
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mask = (x < self.vocab_start_idx) | (x >= self.vocab_end_idx)
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x = x - self.vocab_start_idx
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x[mask] = 0
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y = F.embedding(x, self.weight)
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if world_size > 1:
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y[mask] = 0
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dist.all_reduce(y)
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return y
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def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
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"""
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Applies a linear transformation to the incoming data: y = xA^T + b.
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This function supports specialized implementations based on quantization
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and tensor formats.
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Args:
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x (torch.Tensor): The input tensor.
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weight (torch.Tensor): The weight tensor. It may be quantized and
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requires dequantization for certain cases.
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bias (Optional[torch.Tensor]): The bias tensor to be added. Default is None.
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Returns:
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torch.Tensor: The result of the linear transformation, which may involve
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quantization-aware computations depending on the input parameters.
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Notes:
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- If `weight` is quantized (e.g., `element_size() > 1`), a dequantized version
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is used for computation.
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- If `gemm_impl == "bf16"`, dequantization and a `bf16` GEMM operation are applied.
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- For other cases, the function applies quantization to `x` and uses `fp8_gemm` for computation.
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"""
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if weight.element_size() > 1:
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return F.linear(x, weight, bias)
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elif gemm_impl == "bf16":
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weight = weight_dequant(weight, weight.scale)
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return F.linear(x, weight, bias)
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else:
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x, scale = act_quant(x, block_size)
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y = fp8_gemm(x, scale, weight, weight.scale)
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if bias is not None:
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y += bias
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return y
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class Linear(nn.Module):
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"""
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Custom linear layer with support for quantized weights and optional bias.
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Args:
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in_features (int): Number of input features.
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out_features (int): Number of output features.
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bias (bool): Whether to include a bias term. Defaults to False.
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dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
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"""
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dtype = torch.bfloat16
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def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
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super().__init__()
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self.in_features = in_features
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self.out_features = out_features
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self.weight = nn.Parameter(torch.empty(out_features, in_features, dtype=dtype or Linear.dtype))
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if self.weight.element_size() == 1:
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scale_out_features = (out_features + block_size - 1) // block_size
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scale_in_features = (in_features + block_size - 1) // block_size
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self.weight.scale = self.scale = nn.Parameter(torch.empty(scale_out_features, scale_in_features, dtype=torch.float32))
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else:
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self.register_parameter("scale", None)
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if bias:
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self.bias = nn.Parameter(torch.empty(self.part_out_features))
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else:
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self.register_parameter("bias", None)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass for the custom linear layer.
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Args:
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x (torch.Tensor): Input tensor.
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Returns:
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torch.Tensor: Transformed tensor after linear computation.
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"""
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return linear(x, self.weight, self.bias)
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class ColumnParallelLinear(Linear):
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"""
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Linear layer with column parallelism, splitting output features across distributed processes.
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Args:
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in_features (int): Number of input features.
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out_features (int): Total number of output features.
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bias (bool): Whether to include a bias term. Defaults to False.
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dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
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"""
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def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
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assert out_features % world_size == 0
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self.part_out_features = out_features // world_size
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super().__init__(in_features, self.part_out_features, bias, dtype)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass for column parallel linear layer.
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Args:
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x (torch.Tensor): Input tensor.
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Returns:
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torch.Tensor: Transformed tensor with column-parallel computation.
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"""
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y = linear(x, self.weight, self.bias)
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return y
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class RowParallelLinear(Linear):
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"""
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Linear layer with row parallelism, splitting input features across distributed processes.
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Args:
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in_features (int): Total number of input features.
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out_features (int): Number of output features.
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bias (bool): Whether to include a bias term. Defaults to False.
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dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
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"""
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def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
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assert in_features % world_size == 0
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self.part_in_features = in_features // world_size
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super().__init__(self.part_in_features, out_features, bias, dtype)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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"""
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Forward pass for row parallel linear layer.
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Args:
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x (torch.Tensor): Input tensor.
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Returns:
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torch.Tensor: Transformed tensor with row-parallel computation.
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"""
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y = linear(x, self.weight)
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if world_size > 1:
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dist.all_reduce(y)
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if self.bias is not None:
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y += self.bias
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return y
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class RMSNorm(nn.Module):
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"""
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Root Mean Square Layer Normalization (RMSNorm).
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Args:
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dim (int): Dimension of the input tensor.
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eps (float): Epsilon value for numerical stability. Defaults to 1e-6.
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"""
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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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self.dim = dim
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self.eps = eps
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self.weight = nn.Parameter(torch.ones(dim))
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def forward(self, x: torch.Tensor):
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"""
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Forward pass for RMSNorm.
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Args:
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x (torch.Tensor): Input tensor.
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Returns:
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torch.Tensor: Normalized tensor with the same shape as input.
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"""
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return F.rms_norm(x, (self.dim,), self.weight, self.eps)
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def precompute_freqs_cis(args: ModelArgs) -> torch.Tensor:
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"""
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Precomputes frequency-based complex exponential values for rotary positional embeddings.
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Args:
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args (ModelArgs): Model arguments containing positional embedding parameters.
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Returns:
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torch.Tensor: Precomputed complex exponential values for positional embeddings.
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"""
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dim = args.qk_rope_head_dim
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seqlen = args.max_seq_len
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beta_fast = args.beta_fast
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beta_slow = args.beta_slow
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base = args.rope_theta
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factor = args.rope_factor
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def find_correction_dim(num_rotations, dim, base, max_seq_len):
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"""
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Computes the correction dimension for a given number of rotations in the rotary positional embedding.
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Args:
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num_rotations (float): Number of rotations to compute the correction for.
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dim (int): Dimensionality of the embedding space.
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base (float): Base value for the exponential computation.
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max_seq_len (int): Maximum sequence length.
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Returns:
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float: The correction dimension based on the input parameters.
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"""
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return dim * math.log(max_seq_len / (num_rotations * 2 * math.pi)) / (2 * math.log(base))
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def find_correction_range(low_rot, high_rot, dim, base, max_seq_len):
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"""
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Computes the range of correction dimensions for rotary positional embeddings.
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Args:
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low_rot (float): Lower bound for the number of rotations.
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high_rot (float): Upper bound for the number of rotations.
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dim (int): Dimensionality of the embedding space.
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base (float): Base value for the exponential computation.
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max_seq_len (int): Maximum sequence length.
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Returns:
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Tuple[int, int]: The range of correction dimensions (low, high), clamped to valid indices.
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"""
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low = math.floor(find_correction_dim(low_rot, dim, base, max_seq_len))
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high = math.ceil(find_correction_dim(high_rot, dim, base, max_seq_len))
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return max(low, 0), min(high, dim-1)
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def linear_ramp_factor(min, max, dim):
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"""
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Computes a linear ramp function used to smooth values between a minimum and maximum range.
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Args:
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min (float): Minimum value for the ramp function.
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max (float): Maximum value for the ramp function.
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dim (int): Dimensionality of the ramp tensor.
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Returns:
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torch.Tensor: A tensor of shape (dim,) with values linearly interpolated between 0 and 1,
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clamped to the range [0, 1].
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"""
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if min == max:
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max += 0.001
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linear_func = (torch.arange(dim, dtype=torch.float32) - min) / (max - min)
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ramp_func = torch.clamp(linear_func, 0, 1)
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return ramp_func
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freqs = 1.0 / (base ** (torch.arange(0, dim, 2, dtype=torch.float32) / dim))
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if seqlen > args.original_seq_len:
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low, high = find_correction_range(beta_fast, beta_slow, dim, base, args.original_seq_len)
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smooth = 1 - linear_ramp_factor(low, high, dim // 2)
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freqs = freqs / factor * (1 - smooth) + freqs * smooth
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t = torch.arange(seqlen)
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freqs = torch.outer(t, freqs)
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freqs_cis = torch.polar(torch.ones_like(freqs), freqs)
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return freqs_cis
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def apply_rotary_emb(x: torch.Tensor, freqs_cis: torch.Tensor) -> torch.Tensor:
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"""
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Applies rotary positional embeddings to the input tensor.
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Args:
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x (torch.Tensor): Input tensor with positional embeddings to be applied.
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freqs_cis (torch.Tensor): Precomputed complex exponential values for positional embeddings.
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Returns:
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torch.Tensor: Tensor with rotary embeddings applied.
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"""
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dtype = x.dtype
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x = torch.view_as_complex(x.float().view(*x.shape[:-1], -1, 2))
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freqs_cis = freqs_cis.view(1, x.size(1), 1, x.size(-1))
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y = torch.view_as_real(x * freqs_cis).flatten(3)
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return y.to(dtype)
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class MLA(nn.Module):
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"""
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Multi-Headed Attention Layer (MLA).
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Attributes:
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dim (int): Dimensionality of the input features.
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n_heads (int): Number of attention heads.
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n_local_heads (int): Number of local attention heads for distributed systems.
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q_lora_rank (int): Rank for low-rank query projection.
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kv_lora_rank (int): Rank for low-rank key/value projection.
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qk_nope_head_dim (int): Dimensionality of non-positional query/key projections.
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qk_rope_head_dim (int): Dimensionality of rotary-positional query/key projections.
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qk_head_dim (int): Total dimensionality of query/key projections.
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v_head_dim (int): Dimensionality of value projections.
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softmax_scale (float): Scaling factor for softmax in attention computation.
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"""
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def __init__(self, args: ModelArgs):
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super().__init__()
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self.dim = args.dim
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self.n_heads = args.n_heads
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self.n_local_heads = args.n_heads // world_size
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self.q_lora_rank = args.q_lora_rank
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self.kv_lora_rank = args.kv_lora_rank
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self.qk_nope_head_dim = args.qk_nope_head_dim
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self.qk_rope_head_dim = args.qk_rope_head_dim
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self.qk_head_dim = args.qk_nope_head_dim + args.qk_rope_head_dim
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self.v_head_dim = args.v_head_dim
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if self.q_lora_rank == 0:
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self.wq = ColumnParallelLinear(self.dim, self.n_heads * self.qk_head_dim)
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else:
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self.wq_a = Linear(self.dim, self.q_lora_rank)
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self.q_norm = RMSNorm(self.q_lora_rank)
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self.wq_b = ColumnParallelLinear(self.q_lora_rank, self.n_heads * self.qk_head_dim)
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self.wkv_a = Linear(self.dim, self.kv_lora_rank + self.qk_rope_head_dim)
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self.kv_norm = RMSNorm(self.kv_lora_rank)
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self.wkv_b = ColumnParallelLinear(self.kv_lora_rank, self.n_heads * (self.qk_nope_head_dim + self.v_head_dim))
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self.wo = RowParallelLinear(self.n_heads * self.v_head_dim, self.dim)
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self.softmax_scale = self.qk_head_dim ** -0.5
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if args.max_seq_len > args.original_seq_len:
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mscale = 0.1 * args.mscale * math.log(args.rope_factor) + 1.0
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self.softmax_scale = self.softmax_scale * mscale * mscale
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||||
if attn_impl == "naive":
|
||||
self.register_buffer("k_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.qk_head_dim), persistent=False)
|
||||
self.register_buffer("v_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.n_local_heads, self.v_head_dim), persistent=False)
|
||||
else:
|
||||
self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.kv_lora_rank), persistent=False)
|
||||
self.register_buffer("pe_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.qk_rope_head_dim), persistent=False)
|
||||
|
||||
def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
|
||||
"""
|
||||
Forward pass for the Multi-Headed Attention Layer (MLA).
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor of shape (batch_size, seq_len, dim).
|
||||
start_pos (int): Starting position in the sequence for caching.
|
||||
freqs_cis (torch.Tensor): Precomputed complex exponential values for rotary embeddings.
|
||||
mask (Optional[torch.Tensor]): Mask tensor to exclude certain positions from attention.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Output tensor with the same shape as the input.
|
||||
"""
|
||||
bsz, seqlen, _ = x.size()
|
||||
end_pos = start_pos + seqlen
|
||||
if self.q_lora_rank == 0:
|
||||
q = self.wq(x)
|
||||
else:
|
||||
q = self.wq_b(self.q_norm(self.wq_a(x)))
|
||||
q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
|
||||
q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
|
||||
q_pe = apply_rotary_emb(q_pe, freqs_cis)
|
||||
kv = self.wkv_a(x)
|
||||
kv, k_pe = torch.split(kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
|
||||
k_pe = apply_rotary_emb(k_pe.unsqueeze(2), freqs_cis)
|
||||
if attn_impl == "naive":
|
||||
q = torch.cat([q_nope, q_pe], dim=-1)
|
||||
kv = self.wkv_b(self.kv_norm(kv))
|
||||
kv = kv.view(bsz, seqlen, self.n_local_heads, self.qk_nope_head_dim + self.v_head_dim)
|
||||
k_nope, v = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
|
||||
k = torch.cat([k_nope, k_pe.expand(-1, -1, self.n_local_heads, -1)], dim=-1)
|
||||
self.k_cache[:bsz, start_pos:end_pos] = k
|
||||
self.v_cache[:bsz, start_pos:end_pos] = v
|
||||
scores = torch.einsum("bshd,bthd->bsht", q, self.k_cache[:bsz, :end_pos]) * self.softmax_scale
|
||||
else:
|
||||
wkv_b = self.wkv_b.weight if self.wkv_b.scale is None else weight_dequant(self.wkv_b.weight, self.wkv_b.scale, block_size)
|
||||
wkv_b = wkv_b.view(self.n_local_heads, -1, self.kv_lora_rank)
|
||||
q_nope = torch.einsum("bshd,hdc->bshc", q_nope, wkv_b[:, :self.qk_nope_head_dim])
|
||||
self.kv_cache[:bsz, start_pos:end_pos] = self.kv_norm(kv)
|
||||
self.pe_cache[:bsz, start_pos:end_pos] = k_pe.squeeze(2)
|
||||
scores = (torch.einsum("bshc,btc->bsht", q_nope, self.kv_cache[:bsz, :end_pos]) +
|
||||
torch.einsum("bshr,btr->bsht", q_pe, self.pe_cache[:bsz, :end_pos])) * self.softmax_scale
|
||||
if mask is not None:
|
||||
scores += mask.unsqueeze(1)
|
||||
scores = scores.softmax(dim=-1, dtype=torch.float32).type_as(x)
|
||||
if attn_impl == "naive":
|
||||
x = torch.einsum("bsht,bthd->bshd", scores, self.v_cache[:bsz, :end_pos])
|
||||
else:
|
||||
x = torch.einsum("bsht,btc->bshc", scores, self.kv_cache[:bsz, :end_pos])
|
||||
x = torch.einsum("bshc,hdc->bshd", x, wkv_b[:, -self.v_head_dim:])
|
||||
x = self.wo(x.flatten(2))
|
||||
return x
|
||||
|
||||
from torch.cuda.amp import autocast
|
||||
from torch.utils.checkpoint import checkpoint
|
||||
|
||||
class MLP(nn.Module):
|
||||
"""
|
||||
Multi-Layer Perceptron (MLP) used as a feed-forward layer.
|
||||
|
||||
Attributes:
|
||||
w1 (nn.Module): Linear layer for input-to-hidden transformation.
|
||||
w2 (nn.Module): Linear layer for hidden-to-output transformation.
|
||||
w3 (nn.Module): Additional linear layer for feature transformation.
|
||||
"""
|
||||
def __init__(self, dim: int, inter_dim: int):
|
||||
"""
|
||||
Initializes the MLP layer.
|
||||
|
||||
Args:
|
||||
dim (int): Input and output dimensionality.
|
||||
inter_dim (int): Hidden layer dimensionality.
|
||||
"""
|
||||
def __init__(self, dim, inter_dim):
|
||||
super().__init__()
|
||||
self.w1 = ColumnParallelLinear(dim, inter_dim)
|
||||
self.w2 = RowParallelLinear(inter_dim, dim)
|
||||
self.w3 = ColumnParallelLinear(dim, inter_dim)
|
||||
self.w1 = nn.Linear(dim, inter_dim, bias=False)
|
||||
self.w2 = nn.Linear(inter_dim, dim, bias=False)
|
||||
self.w3 = nn.Linear(dim, inter_dim, bias=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Forward pass for the MLP layer.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Output tensor after MLP computation.
|
||||
"""
|
||||
def forward(self, x):
|
||||
return self.w2(F.silu(self.w1(x)) * self.w3(x))
|
||||
|
||||
|
||||
class Gate(nn.Module):
|
||||
"""
|
||||
Gating mechanism for routing inputs in a mixture-of-experts (MoE) model.
|
||||
|
||||
Attributes:
|
||||
dim (int): Dimensionality of input features.
|
||||
topk (int): Number of top experts activated for each input.
|
||||
n_groups (int): Number of groups for routing.
|
||||
topk_groups (int): Number of groups to route inputs to.
|
||||
score_func (str): Scoring function ('softmax' or 'sigmoid').
|
||||
route_scale (float): Scaling factor for routing weights.
|
||||
weight (torch.nn.Parameter): Learnable weights for the gate.
|
||||
bias (Optional[torch.nn.Parameter]): Optional bias term for the gate.
|
||||
"""
|
||||
def __init__(self, args: ModelArgs):
|
||||
"""
|
||||
Initializes the Gate module.
|
||||
|
||||
Args:
|
||||
args (ModelArgs): Model arguments containing gating parameters.
|
||||
"""
|
||||
def __init__(self, args):
|
||||
super().__init__()
|
||||
self.dim = args.dim
|
||||
self.topk = args.n_activated_experts
|
||||
self.n_groups = args.n_expert_groups
|
||||
self.topk_groups = args.n_limited_groups
|
||||
self.score_func = args.score_func
|
||||
self.route_scale = args.route_scale
|
||||
self.weight = nn.Parameter(torch.empty(args.n_routed_experts, args.dim))
|
||||
self.bias = nn.Parameter(torch.empty(args.n_routed_experts)) if self.dim == 7168 else None
|
||||
self.n_experts = args.n_routed_experts
|
||||
self.weight = nn.Parameter(torch.empty(self.n_experts, self.dim))
|
||||
nn.init.xavier_uniform_(self.weight)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
"""
|
||||
Forward pass for the gating mechanism.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
Tuple[torch.Tensor, torch.Tensor]: Routing weights and selected expert indices.
|
||||
"""
|
||||
scores = linear(x, self.weight)
|
||||
if self.score_func == "softmax":
|
||||
scores = scores.softmax(dim=-1, dtype=torch.float32)
|
||||
else:
|
||||
scores = scores.sigmoid()
|
||||
original_scores = scores
|
||||
if self.bias is not None:
|
||||
scores = scores + self.bias
|
||||
if self.n_groups > 1:
|
||||
scores = scores.view(x.size(0), self.n_groups, -1)
|
||||
if self.bias is None:
|
||||
group_scores = scores.amax(dim=-1)
|
||||
else:
|
||||
group_scores = scores.topk(2, dim=-1)[0].sum(dim=-1)
|
||||
indices = group_scores.topk(self.topk_groups, dim=-1)[1]
|
||||
mask = torch.zeros_like(scores[..., 0]).scatter_(1, indices, True)
|
||||
scores = (scores * mask.unsqueeze(-1)).flatten(1)
|
||||
def forward(self, x):
|
||||
scores = F.softmax(F.linear(x, self.weight), dim=-1)
|
||||
indices = torch.topk(scores, self.topk, dim=-1)[1]
|
||||
weights = original_scores.gather(1, indices)
|
||||
if self.score_func == "sigmoid":
|
||||
weights /= weights.sum(dim=-1, keepdim=True)
|
||||
weights *= self.route_scale
|
||||
return weights.type_as(x), indices
|
||||
|
||||
weights = torch.gather(scores, 1, indices)
|
||||
return weights, indices
|
||||
|
||||
class Expert(nn.Module):
|
||||
"""
|
||||
Expert layer for Mixture-of-Experts (MoE) models.
|
||||
|
||||
Attributes:
|
||||
w1 (nn.Module): Linear layer for input-to-hidden transformation.
|
||||
w2 (nn.Module): Linear layer for hidden-to-output transformation.
|
||||
w3 (nn.Module): Additional linear layer for feature transformation.
|
||||
"""
|
||||
def __init__(self, dim: int, inter_dim: int):
|
||||
"""
|
||||
Initializes the Expert layer.
|
||||
|
||||
Args:
|
||||
dim (int): Input and output dimensionality.
|
||||
inter_dim (int): Hidden layer dimensionality.
|
||||
"""
|
||||
def __init__(self, dim, inter_dim):
|
||||
super().__init__()
|
||||
self.w1 = Linear(dim, inter_dim)
|
||||
self.w2 = Linear(inter_dim, dim)
|
||||
self.w3 = Linear(dim, inter_dim)
|
||||
self.w1 = nn.Linear(dim, inter_dim, bias=False)
|
||||
self.w2 = nn.Linear(inter_dim, dim, bias=False)
|
||||
self.w3 = nn.Linear(dim, inter_dim, bias=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Forward pass for the Expert layer.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Output tensor after expert computation.
|
||||
"""
|
||||
def forward(self, x):
|
||||
return self.w2(F.silu(self.w1(x)) * self.w3(x))
|
||||
|
||||
|
||||
class MoE(nn.Module):
|
||||
"""
|
||||
Mixture-of-Experts (MoE) module.
|
||||
|
||||
Attributes:
|
||||
dim (int): Dimensionality of input features.
|
||||
n_routed_experts (int): Total number of experts in the model.
|
||||
n_local_experts (int): Number of experts handled locally in distributed systems.
|
||||
n_activated_experts (int): Number of experts activated for each input.
|
||||
gate (nn.Module): Gating mechanism to route inputs to experts.
|
||||
experts (nn.ModuleList): List of expert modules.
|
||||
shared_experts (nn.Module): Shared experts applied to all inputs.
|
||||
"""
|
||||
def __init__(self, args: ModelArgs):
|
||||
"""
|
||||
Initializes the MoE module.
|
||||
|
||||
Args:
|
||||
args (ModelArgs): Model arguments containing MoE parameters.
|
||||
"""
|
||||
def __init__(self, args):
|
||||
super().__init__()
|
||||
self.dim = args.dim
|
||||
assert args.n_routed_experts % world_size == 0
|
||||
self.n_routed_experts = args.n_routed_experts
|
||||
self.n_local_experts = args.n_routed_experts // world_size
|
||||
self.n_activated_experts = args.n_activated_experts
|
||||
self.experts_start_idx = rank * self.n_local_experts
|
||||
self.experts_end_idx = self.experts_start_idx + self.n_local_experts
|
||||
self.n_experts = args.n_routed_experts
|
||||
self.gate = Gate(args)
|
||||
self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim) if self.experts_start_idx <= i < self.experts_end_idx else None
|
||||
for i in range(self.n_routed_experts)])
|
||||
self.shared_experts = MLP(args.dim, args.n_shared_experts * args.moe_inter_dim)
|
||||
self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim) for _ in range(self.n_experts)])
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
"""
|
||||
Forward pass for the MoE module.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Output tensor after expert routing and computation.
|
||||
"""
|
||||
shape = x.size()
|
||||
x = x.view(-1, self.dim)
|
||||
def forward(self, x):
|
||||
weights, indices = self.gate(x)
|
||||
y = torch.zeros_like(x)
|
||||
counts = torch.bincount(indices.flatten(), minlength=self.n_routed_experts).tolist()
|
||||
for i in range(self.experts_start_idx, self.experts_end_idx):
|
||||
if counts[i] == 0:
|
||||
continue
|
||||
expert = self.experts[i]
|
||||
idx, top = torch.where(indices == i)
|
||||
y[idx] += expert(x[idx]) * weights[idx, top, None]
|
||||
z = self.shared_experts(x)
|
||||
if world_size > 1:
|
||||
dist.all_reduce(y)
|
||||
return (y + z).view(shape)
|
||||
for i in range(self.n_experts):
|
||||
mask = (indices == i).float().unsqueeze(-1)
|
||||
if mask.any():
|
||||
y += self.experts[i](x * mask) * weights.unsqueeze(-1)
|
||||
return y
|
||||
|
||||
|
||||
class Block(nn.Module):
|
||||
"""
|
||||
Transformer block combining attention and feed-forward layers.
|
||||
|
||||
Attributes:
|
||||
attn (nn.Module): Attention layer (MLA).
|
||||
ffn (nn.Module): Feed-forward network (MLP or MoE).
|
||||
attn_norm (nn.Module): Layer normalization for attention.
|
||||
ffn_norm (nn.Module): Layer normalization for feed-forward network.
|
||||
"""
|
||||
def __init__(self, layer_id: int, args: ModelArgs):
|
||||
"""
|
||||
Initializes the Transformer block.
|
||||
|
||||
Args:
|
||||
layer_id (int): Layer index in the transformer.
|
||||
args (ModelArgs): Model arguments containing block parameters.
|
||||
"""
|
||||
class TransformerBlock(nn.Module):
|
||||
def __init__(self, args):
|
||||
super().__init__()
|
||||
self.attn = MLA(args)
|
||||
self.ffn = MLP(args.dim, args.inter_dim) if layer_id < args.n_dense_layers else MoE(args)
|
||||
self.attn_norm = RMSNorm(args.dim)
|
||||
self.ffn_norm = RMSNorm(args.dim)
|
||||
self.attn = nn.MultiheadAttention(args.dim, args.num_heads, batch_first=True)
|
||||
self.ffn = MoE(args) if args.use_moe else MLP(args.dim, args.inter_dim)
|
||||
self.norm1 = nn.LayerNorm(args.dim)
|
||||
self.norm2 = nn.LayerNorm(args.dim)
|
||||
|
||||
def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
"""
|
||||
Forward pass for the Transformer block.
|
||||
|
||||
Args:
|
||||
x (torch.Tensor): Input tensor.
|
||||
start_pos (int): Starting position in the sequence.
|
||||
freqs_cis (torch.Tensor): Precomputed complex exponential values for rotary embeddings.
|
||||
mask (Optional[torch.Tensor]): Mask tensor to exclude certain positions from attention.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Output tensor after block computation.
|
||||
"""
|
||||
x = x + self.attn(self.attn_norm(x), start_pos, freqs_cis, mask)
|
||||
x = x + self.ffn(self.ffn_norm(x))
|
||||
def forward(self, x):
|
||||
x = x + self.attn(self.norm1(x), self.norm1(x), self.norm1(x), need_weights=False)[0]
|
||||
x = x + self.ffn(self.norm2(x))
|
||||
return x
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
"""
|
||||
Transformer model with positional embeddings, multiple layers, and output projection.
|
||||
|
||||
Attributes:
|
||||
max_seq_len (int): Maximum sequence length for the transformer.
|
||||
embed (nn.Module): Embedding layer for input tokens.
|
||||
layers (torch.nn.ModuleList): List of transformer blocks.
|
||||
norm (nn.Module): Layer normalization applied after all blocks.
|
||||
head (nn.Module): Output projection layer mapping to vocabulary size.
|
||||
freqs_cis (torch.Tensor): Precomputed complex exponential values for rotary embeddings.
|
||||
"""
|
||||
def __init__(self, args: ModelArgs):
|
||||
"""
|
||||
Initializes the Transformer model.
|
||||
|
||||
Args:
|
||||
args (ModelArgs): Model arguments containing transformer parameters.
|
||||
"""
|
||||
global world_size, rank
|
||||
world_size = dist.get_world_size() if dist.is_initialized() else 1
|
||||
rank = dist.get_rank() if dist.is_initialized() else 0
|
||||
Linear.dtype = torch.float8_e4m3fn if args.dtype == "fp8" else torch.bfloat16
|
||||
def __init__(self, args):
|
||||
super().__init__()
|
||||
self.max_seq_len = args.max_seq_len
|
||||
self.embed = ParallelEmbedding(args.vocab_size, args.dim)
|
||||
self.layers = torch.nn.ModuleList()
|
||||
for layer_id in range(args.n_layers):
|
||||
self.layers.append(Block(layer_id, args))
|
||||
self.norm = RMSNorm(args.dim)
|
||||
self.head = ColumnParallelLinear(args.dim, args.vocab_size, dtype=torch.get_default_dtype())
|
||||
self.register_buffer("freqs_cis", precompute_freqs_cis(args), persistent=False)
|
||||
self.embed = nn.Embedding(args.vocab_size, args.dim)
|
||||
self.layers = nn.ModuleList([TransformerBlock(args) for _ in range(args.n_layers)])
|
||||
self.norm = nn.LayerNorm(args.dim)
|
||||
self.head = nn.Linear(args.dim, args.vocab_size, bias=False)
|
||||
|
||||
@torch.inference_mode()
|
||||
def forward(self, tokens: torch.Tensor, start_pos: int = 0):
|
||||
"""
|
||||
Forward pass for the Transformer model.
|
||||
|
||||
Args:
|
||||
tokens (torch.Tensor): Input tensor of token IDs with shape (batch_size, seq_len).
|
||||
start_pos (int, optional): Starting position in the sequence for rotary embeddings. Defaults to 0.
|
||||
|
||||
Returns:
|
||||
torch.Tensor: Logits tensor of shape (batch_size, vocab_size).
|
||||
"""
|
||||
seqlen = tokens.size(1)
|
||||
h = self.embed(tokens)
|
||||
freqs_cis = self.freqs_cis[start_pos:start_pos+seqlen]
|
||||
mask = None
|
||||
if seqlen > 1:
|
||||
mask = torch.full((seqlen, seqlen), float("-inf"), device=tokens.device).triu_(1)
|
||||
def forward(self, x):
|
||||
x = self.embed(x)
|
||||
for layer in self.layers:
|
||||
h = layer(h, start_pos, freqs_cis, mask)
|
||||
h = self.norm(h)[:, -1]
|
||||
logits = self.head(h)
|
||||
if world_size > 1:
|
||||
all_logits = [torch.empty_like(logits) for _ in range(world_size)]
|
||||
dist.all_gather(all_logits, logits)
|
||||
logits = torch.cat(all_logits, dim=-1)
|
||||
return logits
|
||||
|
||||
x = checkpoint(layer, x)
|
||||
return self.head(self.norm(x))
|
||||
|
||||
if __name__ == "__main__":
|
||||
torch.set_default_dtype(torch.bfloat16)
|
||||
torch.set_default_device("cuda")
|
||||
torch.manual_seed(0)
|
||||
class ModelArgs:
|
||||
vocab_size = 32000
|
||||
dim = 1024
|
||||
inter_dim = 4096
|
||||
n_layers = 12
|
||||
num_heads = 8
|
||||
use_moe = True
|
||||
n_routed_experts = 4
|
||||
n_activated_experts = 2
|
||||
moe_inter_dim = 4096
|
||||
|
||||
args = ModelArgs()
|
||||
x = torch.randint(0, args.vocab_size, (2, 128))
|
||||
model = Transformer(args)
|
||||
model = Transformer(args).cuda()
|
||||
x = torch.randint(0, args.vocab_size, (2, 128), device='cuda')
|
||||
print(model(x).size())
|
||||
|
||||
Loading…
Reference in New Issue
Block a user