mirror of
https://github.com/deepseek-ai/DeepSeek-V3
synced 2025-06-26 18:17:55 +00:00
Release DeepSeek-V3
This commit is contained in:
stack-heap-overflow
421
inference/model.py
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421
inference/model.py
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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.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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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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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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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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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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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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return linear(x, self.weight, self.bias)
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class ColumnParallelLinear(Linear):
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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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y = linear(x, self.weight, self.bias)
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return y
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class RowParallelLinear(Linear):
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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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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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def __init__(self, dim: int, eps: float = 1e-6):
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super().__init__()
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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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x = x.float()
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y = x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)
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return y.type_as(self.weight) * self.weight
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def precompute_freqs_cis(args: ModelArgs) -> torch.Tensor:
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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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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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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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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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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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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":
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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)
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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)
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else:
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self.register_buffer("kv_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.kv_lora_rank), persistent=False)
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self.register_buffer("pe_cache", torch.zeros(args.max_batch_size, args.max_seq_len, self.qk_rope_head_dim), persistent=False)
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def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]):
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bsz, seqlen, _ = x.size()
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end_pos = start_pos + seqlen
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if self.q_lora_rank == 0:
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q = self.wq(x)
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else:
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q = self.wq_b(self.q_norm(self.wq_a(x)))
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q = q.view(bsz, seqlen, self.n_local_heads, self.qk_head_dim)
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q_nope, q_pe = torch.split(q, [self.qk_nope_head_dim, self.qk_rope_head_dim], dim=-1)
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q_pe = apply_rotary_emb(q_pe, freqs_cis)
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kv = self.wkv_a(x)
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kv, k_pe = torch.split(kv, [self.kv_lora_rank, self.qk_rope_head_dim], dim=-1)
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k_pe = apply_rotary_emb(k_pe.unsqueeze(2), freqs_cis)
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if attn_impl == "naive":
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q = torch.cat([q_nope, q_pe], dim=-1)
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kv = self.wkv_b(self.kv_norm(kv))
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kv = kv.view(bsz, seqlen, self.n_local_heads, self.qk_nope_head_dim + self.v_head_dim)
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k_nope, v = torch.split(kv, [self.qk_nope_head_dim, self.v_head_dim], dim=-1)
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k = torch.cat([k_nope, k_pe.expand(-1, -1, self.n_local_heads, -1)], dim=-1)
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self.k_cache[:bsz, start_pos:end_pos] = k
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self.v_cache[:bsz, start_pos:end_pos] = v
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scores = torch.einsum("bshd,bthd->bsht", q, self.k_cache[:bsz, :end_pos]) * self.softmax_scale
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else:
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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)
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wkv_b = wkv_b.view(self.n_local_heads, -1, self.kv_lora_rank)
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q_nope = torch.einsum("bshd,hdc->bshc", q_nope, wkv_b[:, :self.qk_nope_head_dim])
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self.kv_cache[:bsz, start_pos:end_pos] = self.kv_norm(kv)
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self.pe_cache[:bsz, start_pos:end_pos] = k_pe.squeeze(2)
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scores = (torch.einsum("bshc,btc->bsht", q_nope, self.kv_cache[:bsz, :end_pos]) +
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torch.einsum("bshr,btr->bsht", q_pe, self.pe_cache[:bsz, :end_pos])) * self.softmax_scale
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if mask is not None:
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scores += mask.unsqueeze(1)
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scores = scores.softmax(dim=-1, dtype=torch.float32).type_as(x)
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if attn_impl == "naive":
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x = torch.einsum("bsht,bthd->bshd", scores, self.v_cache[:bsz, :end_pos])
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else:
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x = torch.einsum("bsht,btc->bshc", scores, self.kv_cache[:bsz, :end_pos])
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x = torch.einsum("bshc,hdc->bshd", x, wkv_b[:, -self.v_head_dim:])
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x = self.wo(x.flatten(2))
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return x
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class MLP(nn.Module):
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def __init__(self, dim: int, inter_dim: int):
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super().__init__()
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self.w1 = ColumnParallelLinear(dim, inter_dim)
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self.w2 = RowParallelLinear(inter_dim, dim)
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self.w3 = ColumnParallelLinear(dim, inter_dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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class Gate(nn.Module):
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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.topk = args.n_activated_experts
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self.n_groups = args.n_expert_groups
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self.topk_groups = args.n_limited_groups
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self.score_func = args.score_func
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self.route_scale = args.route_scale
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self.weight = nn.Parameter(torch.empty(args.n_routed_experts, args.dim))
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self.bias = nn.Parameter(torch.empty(args.n_routed_experts)) if self.dim == 7168 else None
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def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
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scores = linear(x, self.weight)
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if self.score_func == "softmax":
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scores = scores.softmax(dim=-1, dtype=torch.float32)
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else:
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scores = scores.sigmoid()
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original_scores = scores
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if self.bias is not None:
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scores = scores + self.bias
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if self.n_groups > 1:
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scores = scores.view(x.size(0), self.n_groups, -1)
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if self.bias is None:
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group_scores = scores.amax(dim=-1)
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else:
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group_scores = scores.topk(2, dim=-1)[0].sum(dim=-1)
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indices = group_scores.topk(self.topk_groups, dim=-1)[1]
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mask = torch.zeros_like(scores[..., 0]).scatter_(1, indices, True)
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scores = (scores * mask.unsqueeze(-1)).flatten(1)
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indices = torch.topk(scores, self.topk, dim=-1)[1]
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weights = original_scores.gather(1, indices)
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if self.score_func == "sigmoid":
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weights /= weights.sum(dim=-1, keepdim=True)
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weights *= self.route_scale
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return weights.type_as(x), indices
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class Expert(nn.Module):
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def __init__(self, dim: int, inter_dim: int):
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super().__init__()
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self.w1 = Linear(dim, inter_dim)
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self.w2 = Linear(inter_dim, dim)
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self.w3 = Linear(dim, inter_dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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return self.w2(F.silu(self.w1(x)) * self.w3(x))
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class MoE(nn.Module):
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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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assert args.n_routed_experts % world_size == 0
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self.n_routed_experts = args.n_routed_experts
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self.n_local_experts = args.n_routed_experts // world_size
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self.n_activated_experts = args.n_activated_experts
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self.experts_start_idx = rank * self.n_local_experts
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self.experts_end_idx = self.experts_start_idx + self.n_local_experts
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self.gate = Gate(args)
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self.experts = nn.ModuleList([Expert(args.dim, args.moe_inter_dim) if self.experts_start_idx <= i < self.experts_end_idx else None
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for i in range(self.n_routed_experts)])
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self.shared_experts = MLP(args.dim, args.n_shared_experts * args.moe_inter_dim)
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def forward(self, x: torch.Tensor) -> torch.Tensor:
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shape = x.size()
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x = x.view(-1, self.dim)
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weights, indices = self.gate(x)
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y = torch.zeros_like(x)
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counts = torch.bincount(indices.flatten(), minlength=self.n_routed_experts).tolist()
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for i in range(self.experts_start_idx, self.experts_end_idx):
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if counts[i] == 0:
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continue
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expert = self.experts[i]
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idx, top = torch.where(indices == i)
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y[idx] += expert(x[idx]) * weights[idx, top, None]
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z = self.shared_experts(x)
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if world_size > 1:
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dist.all_reduce(y)
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return (y + z).view(shape)
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||||
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class Block(nn.Module):
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def __init__(self, layer_id: int, args: ModelArgs):
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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)
|
||||
|
||||
def forward(self, x: torch.Tensor, start_pos: int, freqs_cis: torch.Tensor, mask: Optional[torch.Tensor]) -> torch.Tensor:
|
||||
x = x + self.attn(self.attn_norm(x), start_pos, freqs_cis, mask)
|
||||
x = x + self.ffn(self.ffn_norm(x))
|
||||
return x
|
||||
|
||||
|
||||
class Transformer(nn.Module):
|
||||
def __init__(self, args: ModelArgs):
|
||||
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
|
||||
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)
|
||||
|
||||
@torch.inference_mode()
|
||||
def forward(self, tokens: torch.Tensor, start_pos: int = 0):
|
||||
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)
|
||||
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
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
torch.set_default_dtype(torch.bfloat16)
|
||||
torch.set_default_device("cuda")
|
||||
torch.manual_seed(0)
|
||||
args = ModelArgs()
|
||||
x = torch.randint(0, args.vocab_size, (2, 128))
|
||||
model = Transformer(args)
|
||||
print(model(x).size())
|
||||
Reference in New Issue
Block a user