mirror of
https://github.com/firestar5683/StarPilot.git
synced 2026-11-08 05:23:41 +08:00
Daniel Koepping
568 lines
35 KiB
Python
568 lines
35 KiB
Python
from __future__ import annotations
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import enum, functools, itertools, math, pathlib, re
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from dataclasses import dataclass, replace
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from tinygrad import Tensor, nn, UOp, TinyJit, getenv, function, dtypes, Device
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from tinygrad.llm.kernels.amd import Linear, gated_delta_prefill, flash_attention, amd_custom_kernels_supported
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from tinygrad.llm.gguf import gguf_parse, gguf_shard
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from tinygrad.uop.ops import resolve
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class ExpertGating(enum.IntEnum):
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SOFTMAX = 1
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SIGMOID = 2
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SOFTMAX_WEIGHT = 3 # softmax over the top-k selected logits
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SQRT_SOFTPLUS = 4
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@dataclass(frozen=True)
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class YaRNConfig:
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factor: float
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orig_ctx_len: int
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beta_fast: float
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beta_slow: float
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@functools.cache
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def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, device:str|tuple[str, ...]|None=None, yarn:YaRNConfig|None=None) -> Tensor:
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freqs = 1.0 / (theta ** (Tensor.arange(0, dim, 2) / dim))
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concentration = 1.0
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if yarn is not None and yarn.factor > 1.0:
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concentration = 0.1 * math.log(yarn.factor) + 1.0
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d_half = dim // 2
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low = max(0, math.floor(d_half * math.log(yarn.orig_ctx_len / (yarn.beta_fast * 2 * math.pi)) / math.log(theta)))
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high = min(dim-1, math.ceil(d_half * math.log(yarn.orig_ctx_len / (yarn.beta_slow * 2 * math.pi)) / math.log(theta)))
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interp = 1 - ((Tensor.arange(d_half).float() - low) / max(0.001, high-low)).clamp(0, 1)
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freqs = freqs * interp + (freqs / yarn.factor) * (1 - interp)
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freqs = Tensor.arange(end).unsqueeze(dim=1) * freqs.unsqueeze(dim=0)
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return (freqs.cos() * concentration).cat(freqs.sin() * concentration, dim=-1).clone(device)
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class ExpertWeights:
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"""Like Linear but with num_experts dimension. Weight shape: (num_experts, out_features, in_features)."""
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def __init__(self, num_experts:int, in_features:int, out_features:int, bias:bool=False):
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self.weight = Tensor.zeros(num_experts, out_features, in_features)
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if bias: self.bias = Tensor.zeros(num_experts, out_features)
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def __call__(self, sel:Tensor, x:Tensor) -> Tensor:
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# sel: (B, T, k), x: (B, T, 1, in) or (B, T, k, in) -> output: (B, T, k, out)
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ret = (x.unsqueeze(-2) @ self.weight[sel].transpose(-1, -2)).contiguous().squeeze(-2)
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return ret + self.bias[sel] if hasattr(self, 'bias') else ret
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def gated_activation(gate:Tensor, up:Tensor, *, alpha:float=1.0, limit:float|None=None, up_bias:float=0.0) -> Tensor:
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if limit is not None and limit > 0: gate, up = gate.clamp(max_=limit), up.clamp(-limit, limit)
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return gate * (gate * alpha).sigmoid() * (up + up_bias)
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def apply_rope(x:Tensor, freqs_cis:Tensor) -> Tensor:
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assert x.shape[-1] % 2 == 0
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cos, sin = freqs_cis.reshape(1, 1, x.shape[2], -1).chunk(2, dim=-1)
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x1, x2 = x.chunk(2, dim=-1)
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return (x1 * cos - x2 * sin).cat(x2 * cos + x1 * sin, dim=-1)
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def pairwise_topk(x: Tensor, k: int) -> tuple[Tensor, Tensor]:
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n = x.shape[-1]
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vals = Tensor.arange(n).reshape(1,1,n).cast(x.dtype).expand(x.shape)
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cmp = (x.unsqueeze(-1) > x.unsqueeze(-2)) | ((x.unsqueeze(-1) == x.unsqueeze(-2)) & \
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(Tensor.arange(n).reshape(1,1,n,1) < Tensor.arange(n).reshape(1,1,1,n)))
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sel = x.const_like(0).scatter(-1, cmp.sum(axis=-1).cast('int32'), vals)[:,:,n-k:].cast('int32')
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return x.gather(-1, sel), sel
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@dataclass(frozen=True)
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class SSMConfig:
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conv_kernel: int
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state_size: int
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group_count: int
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time_step_rank: int
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inner_size: int
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kda: bool = False
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@dataclass(frozen=True)
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class TransformerConfig:
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num_blocks: int
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dim: int
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hidden_dim: int
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n_heads: int
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n_kv_heads: int
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norm_eps: float
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vocab_size: int
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head_dim: int
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rope_theta: float
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rope_dim: int
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v_head_dim: int
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yarn: YaRNConfig|None = None
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max_context: int = 0
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qk_norm: int = 0
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num_experts: int = 0
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num_experts_per_tok: int = 0
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norm_topk_prob: bool = False
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expert_gating_func: ExpertGating = ExpertGating.SOFTMAX
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q_lora_rank: int = 0
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kv_lora_rank: int = 0
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shared_expert_dim: int = 0
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ssm_layers: tuple[bool, ...] = ()
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attn_output_gate: bool = False
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attn_output_bias: bool = False
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attn_sinks: bool = False
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ssm: SSMConfig|None = None
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shared_expert_gate: bool = True
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leading_dense_blocks: int = 0
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dense_hidden_dim: int = 0
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routed_scaling_factor: float = 1.0
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qkv_bias: bool = False
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expert_bias: bool = False
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expert_proj_bias: bool = False
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swiglu_alpha: float = 1.0
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swiglu_limit: float|None = None
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swiglu_up_bias: float = 0.0
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sliding_window: int = 0
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sliding_window_pattern: int = 0
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class FFNBlock:
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def __init__(self, config:TransformerConfig):
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self.config = config
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# --- RMSNorms --------------------------------------------------------
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self.attn_norm = nn.RMSNorm(config.dim, config.norm_eps)
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self.ffn_norm = nn.RMSNorm(config.dim, config.norm_eps)
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# --- feed-forward (MoE or dense) -------------------------------------
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if config.num_experts > 0:
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self.ffn_gate_inp = Linear(config.dim, config.num_experts, bias=config.expert_proj_bias) # router
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if config.expert_bias: self.exp_probs_b = {"bias": Tensor.zeros(config.num_experts)}
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self.ffn_gate_exps = ExpertWeights(config.num_experts, config.dim, config.hidden_dim, bias=config.expert_proj_bias)
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self.ffn_up_exps = ExpertWeights(config.num_experts, config.dim, config.hidden_dim, bias=config.expert_proj_bias)
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self.ffn_down_exps = ExpertWeights(config.num_experts, config.hidden_dim, config.dim, bias=config.expert_proj_bias)
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if config.shared_expert_dim > 0:
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self.ffn_gate_shexp = Linear(config.dim, config.shared_expert_dim, bias=False)
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self.ffn_up_shexp = Linear(config.dim, config.shared_expert_dim, bias=False)
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self.ffn_down_shexp = Linear(config.shared_expert_dim, config.dim, bias=False)
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if config.shared_expert_gate: self.ffn_gate_inp_shexp = {"weight": Tensor.zeros(config.dim)}
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else:
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self.ffn_gate = Linear(config.dim, config.hidden_dim, bias=False)
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self.ffn_up = Linear(config.dim, config.hidden_dim, bias=False)
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self.ffn_down = Linear(config.hidden_dim, config.dim, bias=False)
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def _feed_forward(self, x:Tensor) -> Tensor:
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if hasattr(self, 'ffn_gate_exps'):
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h = x.unsqueeze(2) # (B, T, 1, D) - add expert dim for broadcasting
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logits = self.ffn_gate_inp(x)
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bias = self.exp_probs_b["bias"] if hasattr(self, 'exp_probs_b') else None
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gating, normalize_topk = self.config.expert_gating_func, self.config.norm_topk_prob
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# fast path: without selection bias, normalized SOFTMAX is equivalent to SOFTMAX_WEIGHT
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if gating == ExpertGating.SOFTMAX and bias is None and normalize_topk:
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gating, normalize_topk = ExpertGating.SOFTMAX_WEIGHT, False
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if gating == ExpertGating.SOFTMAX_WEIGHT: scores = logits
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elif gating == ExpertGating.SOFTMAX: scores = logits.softmax(-1)
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elif gating == ExpertGating.SIGMOID: scores = logits.sigmoid()
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elif gating == ExpertGating.SQRT_SOFTPLUS: scores = logits.softplus().sqrt()
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_, sel = pairwise_topk(scores if bias is None else scores + bias, self.config.num_experts_per_tok)
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probs = scores.gather(-1, sel)
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# SOFTMAX_WEIGHT applies softmax after top-k selection
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if gating == ExpertGating.SOFTMAX_WEIGHT: probs = probs.softmax(-1)
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if normalize_topk: probs = probs / probs.sum(axis=-1, keepdim=True)
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probs = probs * self.config.routed_scaling_factor
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gate, up = self.ffn_gate_exps(sel, h), self.ffn_up_exps(sel, h)
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act = gated_activation(gate, up, alpha=self.config.swiglu_alpha, limit=self.config.swiglu_limit, up_bias=self.config.swiglu_up_bias)
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x_down = self.ffn_down_exps(sel, act.contiguous()) # (B, T, k, D)
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out = (x_down * probs.unsqueeze(-1)).sum(axis=2) # (B, T, D)
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if hasattr(self, 'ffn_gate_shexp'):
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shexp = self.ffn_down_shexp(gated_activation(self.ffn_gate_shexp(x), self.ffn_up_shexp(x), limit=self.config.swiglu_limit).contiguous())
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if hasattr(self, 'ffn_gate_inp_shexp'): shexp = shexp * (x * self.ffn_gate_inp_shexp["weight"]).sum(axis=-1, keepdim=True).sigmoid()
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out = out + shexp
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return out
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# TODO: remove the need for this contiguous
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return self.ffn_down(gated_activation(self.ffn_gate(x), self.ffn_up(x), limit=self.config.swiglu_limit).contiguous())
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# given the token-prefix match, return how much cached state this block can still reuse
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def _reusable_prefix_len(self, prefix_len:int, cached_len:int) -> int: return prefix_len
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def _init_state(self, x:Tensor): raise NotImplementedError
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def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor: raise NotImplementedError
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def __call__(self, x: Tensor, start_pos: int|UOp):
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self._init_state(x)
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# we pass in the weights implicitly so we unpack the GGUF on the fly
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@function(precompile=True, allow_implicit=True)
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def _run(x:Tensor, start_pos:int|UOp):
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h = x + self._attention(self.attn_norm(x), start_pos)
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return (h + self._feed_forward(self.ffn_norm(h))).contiguous()
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return _run(x, start_pos)
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class TransformerBlock(FFNBlock):
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def __init__(self, config:TransformerConfig):
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super().__init__(config)
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assert config.v_head_dim == config.head_dim, "TransformerBlock requires v_head_dim == head_dim"
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# --- attention projections (all linear, bias-free) ------------------
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q_proj_out = config.head_dim * config.n_heads * (2 if config.attn_output_gate else 1)
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kv_proj_out = config.head_dim * config.n_kv_heads
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self.attn_q = Linear(config.dim, q_proj_out, bias=config.qkv_bias)
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self.attn_k = Linear(config.dim, kv_proj_out, bias=config.qkv_bias)
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self.attn_v = Linear(config.dim, kv_proj_out, bias=config.qkv_bias)
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self.attn_output = Linear(config.head_dim * config.n_heads, config.dim, bias=config.attn_output_bias)
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if config.qk_norm: self.attn_q_norm, self.attn_k_norm = nn.RMSNorm(config.qk_norm, config.norm_eps), nn.RMSNorm(config.qk_norm, config.norm_eps)
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if config.attn_sinks: self.attn_sinks = {"weight": Tensor.zeros(config.n_heads)}
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def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor:
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q, k, v = self.attn_q(x), self.attn_k(x), self.attn_v(x)
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if self.config.qk_norm and self.config.qk_norm != self.config.head_dim: q, k = self.attn_q_norm(q), self.attn_k_norm(k)
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B, T, _ = x.shape
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if self.config.attn_output_gate:
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qg = q.reshape(B, T, self.config.n_heads, 2, self.config.head_dim)
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q, gate = qg[:, :, :, 0, :], qg[:, :, :, 1, :].reshape(B, T, self.config.n_heads * self.config.head_dim)
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q = q.reshape(B, T, self.config.n_heads, self.config.head_dim).transpose(1, 2) # (B,H,T,Hd)
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k = k.reshape(B, T, self.config.n_kv_heads, self.config.head_dim).transpose(1, 2) # (B,KvH,T,Hd)
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v = v.reshape(B, T, self.config.n_kv_heads, self.config.head_dim).transpose(1, 2) # (B,KvH,T,Hd)
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if self.config.qk_norm == self.config.head_dim: q, k = self.attn_q_norm(q), self.attn_k_norm(k)
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q = apply_rope(q[..., :self.config.rope_dim], self.freqs_cis[start_pos:start_pos+T]).cat(q[..., self.config.rope_dim:], dim=-1)
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k = apply_rope(k[..., :self.config.rope_dim], self.freqs_cis[start_pos:start_pos+T]).cat(k[..., self.config.rope_dim:], dim=-1)
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# NOTE: we don't want to change self.cache_kv, the function API doesn't support this well
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store = self.cache_kv[:, :, :, start_pos:start_pos+T, :].uop.store(Tensor.stack(k, v).cast(self.cache_kv.dtype).uop)
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assigned_kv = Tensor(self.cache_kv.uop.after(store))
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# on RDNA3/4, hybrid models use custom flash attention kernels on the KV cache
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if amd_custom_kernels_supported(x.device) and self.config.ssm is not None:
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attn = flash_attention(q, assigned_kv, start_pos+T)
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attn = attn.transpose(1, 2).reshape(B, T, -1) # back to (B,T,D)
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return self.attn_output(attn if not self.config.attn_output_gate else (attn * gate.sigmoid()))
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k = assigned_kv[0, :, :, 0:start_pos+T, :]
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v = assigned_kv[1, :, :, 0:start_pos+T, :]
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#self.cache_kv[:, :, :, start_pos:start_pos+T, :].assign(Tensor.stack(k, v))
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#k = self.cache_kv[0, :, :, 0:start_pos+T, :]
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#v = self.cache_kv[1, :, :, 0:start_pos+T, :]
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# NOTE: this mask is causal_lower_right, not the causal_upper_left generated by is_casual = True
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# TODO: this if statement should be removed and it shouldn't generate extra kernels
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mask, window = None, self.config.sliding_window
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if resolve(T != 1) or window:
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mask = Tensor.full((1, 1, T, start_pos+T), float("-inf"), dtype=x.dtype, buffer=False)
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mask = mask.triu(start_pos+1) + mask.tril(start_pos-window) if window else mask.triu(start_pos+1)
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if hasattr(self, 'attn_sinks'):
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k, v = k.cat(k[..., :1, :].const_like(0), dim=-2), v.cat(v[..., :1, :].const_like(0), dim=-2)
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sink_col = self.attn_sinks["weight"].reshape(1, -1, 1, 1).expand(1, self.config.n_heads, T, 1)
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if mask is None: mask = Tensor.zeros(1, 1, T, start_pos+T, dtype=x.dtype, buffer=False)
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mask = mask.expand(1, self.config.n_heads, T, start_pos+T).cat(sink_col, dim=-1)
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attn = q.scaled_dot_product_attention(k, v, attn_mask=mask, enable_gqa=True) # (B,H,T,Hd)
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attn = attn.transpose(1, 2).reshape(B, T, -1) # back to (B,T,D)
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return self.attn_output(attn if not self.config.attn_output_gate else (attn * gate.sigmoid()))
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def _init_state(self, x:Tensor):
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if not hasattr(self, "cache_kv"):
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# zeroed so the flash kernels can safely read whole tiles past the valid region (masked lanes multiply by 0)
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self.cache_kv = Tensor.zeros(2, x.shape[0], self.config.n_kv_heads, self.config.max_context, self.config.head_dim,
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dtype=dtypes.half, device=x.device if isinstance(x.device, str) else None)
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if isinstance(x.device, tuple): self.cache_kv = self.cache_kv.shard(x.device, 2).realize()
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self.freqs_cis = precompute_freqs_cis(self.config.rope_dim, self.config.max_context, self.config.rope_theta,
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device=x.device, yarn=self.config.yarn)
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class MLATransformerBlock(FFNBlock):
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def __init__(self, config:TransformerConfig):
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super().__init__(config)
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qk_nope_head_dim = config.head_dim - config.rope_dim
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if config.q_lora_rank > 0:
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self.attn_q_a = Linear(config.dim, config.q_lora_rank, bias=False)
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self.attn_q_a_norm = nn.RMSNorm(config.q_lora_rank, config.norm_eps)
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self.attn_q_b = Linear(config.q_lora_rank, config.n_heads * config.head_dim, bias=False)
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else:
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self.attn_q = Linear(config.dim, config.n_heads * config.head_dim, bias=False)
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self.attn_kv_a_mqa = Linear(config.dim, config.kv_lora_rank + config.rope_dim, bias=False)
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self.attn_kv_a_norm = nn.RMSNorm(config.kv_lora_rank, config.norm_eps)
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self.attn_k_b = {"weight": Tensor.zeros(config.n_heads, config.kv_lora_rank, qk_nope_head_dim)}
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self.attn_v_b = {"weight": Tensor.zeros(config.n_heads, config.v_head_dim, config.kv_lora_rank)}
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self.attn_output = Linear(config.n_heads * config.v_head_dim, config.dim, bias=False)
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def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor:
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B, T, _ = x.shape
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q_nope_head_dim = self.config.head_dim - self.config.rope_dim
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q_proj = self.attn_q_b(self.attn_q_a_norm(self.attn_q_a(x))) if self.config.q_lora_rank > 0 else self.attn_q(x)
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q = q_proj.reshape(B, T, self.config.n_heads, self.config.head_dim).transpose(1, 2)
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q_nope, q_rope = q[..., :q_nope_head_dim], q[..., q_nope_head_dim:]
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if not self.config.ssm or not self.config.ssm.kda: q_rope = apply_rope(q_rope, self.freqs_cis[start_pos:start_pos+T])
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q = (q_nope @ self.attn_k_b["weight"].transpose(-1, -2)).cat(q_rope, dim=-1)
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kv_a = self.attn_kv_a_mqa(x)
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c_kv = self.attn_kv_a_norm(kv_a[..., :self.config.kv_lora_rank])
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k_rope = kv_a[..., self.config.kv_lora_rank:].reshape(B, T, 1, self.config.rope_dim).transpose(1, 2)
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if not self.config.ssm or not self.config.ssm.kda: k_rope = apply_rope(k_rope, self.freqs_cis[start_pos:start_pos+T])
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k_store = c_kv.reshape(B, 1, T, self.config.kv_lora_rank).cat(k_rope.reshape(B, 1, T, self.config.rope_dim), dim=-1)
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k = Tensor(self.cache_k.uop.after(self.cache_k[:, :, start_pos:start_pos+T, :].uop.store(k_store.uop)))[:, :, 0:start_pos+T, :]
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v = k[..., :self.config.kv_lora_rank]
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attn = q @ k.transpose(-1, -2) * (1.0 / self.config.head_dim ** 0.5)
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if (mask:=self._attn_mask(x, start_pos)) is not None: attn = attn + mask
|
|
attn = attn.softmax(-1)
|
|
attn = ((attn @ v) @ self.attn_v_b["weight"].transpose(-1, -2)).transpose(1, 2).reshape(B, T, -1)
|
|
return self.attn_output(attn)
|
|
|
|
def _attn_mask(self, x:Tensor, start_pos:int|UOp) -> Tensor|None: # (1, 1, T, start_pos+T), None: everything is visible
|
|
T = x.shape[1]
|
|
return Tensor.full((1, 1, T, start_pos+T), float("-inf"), dtype=x.dtype, buffer=False).triu(start_pos+1) if resolve(T != 1) else None
|
|
|
|
def _init_state(self, x:Tensor):
|
|
if not hasattr(self, "cache_k"):
|
|
self.cache_k = Tensor.empty(x.shape[0], 1, self.config.max_context, self.config.kv_lora_rank + self.config.rope_dim, device=x.device)
|
|
self.freqs_cis = precompute_freqs_cis(self.config.rope_dim, self.config.max_context, self.config.rope_theta,
|
|
device=x.device, yarn=self.config.yarn)
|
|
|
|
class GatedDeltaNetBlock(FFNBlock):
|
|
def __init__(self, config:TransformerConfig, ssm:SSMConfig):
|
|
super().__init__(config)
|
|
self.head_k_dim, self.num_k_heads, self.num_v_heads = ssm.state_size, ssm.group_count, ssm.time_step_rank
|
|
assert self.num_v_heads % self.num_k_heads == 0
|
|
self.head_v_dim, self.ssm_conv_kernel = ssm.inner_size // ssm.time_step_rank, ssm.conv_kernel
|
|
self.conv_channels, self.q_dim = ssm.inner_size + 2*ssm.group_count*ssm.state_size, ssm.state_size*ssm.group_count
|
|
self.attn_qkv = Linear(config.dim, self.conv_channels, bias=False)
|
|
if ssm.kda:
|
|
self.ssm_g_a, self.ssm_g_b = Linear(config.dim, self.head_v_dim, bias=False), Linear(self.head_v_dim, ssm.inner_size, bias=False)
|
|
self.ssm_f_a, self.ssm_f_b = Linear(config.dim, self.head_k_dim, bias=False), Linear(self.head_k_dim, ssm.inner_size, bias=False)
|
|
else:
|
|
self.attn_gate = Linear(config.dim, ssm.inner_size, bias=False)
|
|
self.ssm_alpha = Linear(config.dim, self.num_v_heads, bias=False)
|
|
self.ssm_beta = Linear(config.dim, self.num_v_heads, bias=False)
|
|
self.ssm_conv1d = {"weight": Tensor.zeros(self.conv_channels, self.ssm_conv_kernel)}
|
|
self.ssm_dt = {"bias": Tensor.zeros(ssm.inner_size if ssm.kda else self.num_v_heads)}
|
|
self.ssm_a = Tensor.zeros(self.num_v_heads, 1) if ssm.kda else Tensor.zeros(self.num_v_heads)
|
|
self.ssm_norm, self.ssm_out = nn.RMSNorm(self.head_v_dim, config.norm_eps), Linear(ssm.inner_size, config.dim, bias=False)
|
|
|
|
def _attention(self, x:Tensor, start_pos:int|UOp) -> Tensor:
|
|
B, T, _ = x.shape
|
|
# bind ints to a variable so the reset flag stays a runtime value (it toggles when generation restarts at position 0)
|
|
start_pos = start_pos if isinstance(start_pos, UOp) else UOp.variable("start_pos", 0, self.config.max_context-1).bind(start_pos)
|
|
initial = Tensor(start_pos).eq(0)
|
|
is_kda = hasattr(self, "ssm_g_a")
|
|
symbolic = isinstance(T, UOp)
|
|
T_pad = x.max_shape[1] # symbolic chunks are padded to their max size: one graph serves every size
|
|
|
|
# input processing
|
|
x = x.half()
|
|
out_gate = self.ssm_g_b(self.ssm_g_a(x)) if is_kda else self.attn_gate(x)
|
|
out_gate = out_gate.reshape(B, T, self.num_v_heads, self.head_v_dim)
|
|
beta = self.ssm_beta(x).sigmoid().reshape(B, T, self.num_v_heads)
|
|
alpha = self.ssm_f_b(self.ssm_f_a(x)) if is_kda else self.ssm_alpha(x)
|
|
g, a = (alpha.float() + self.ssm_dt["bias"]).reshape(B, T, self.num_v_heads, -1), self.ssm_a.reshape(self.num_v_heads, -1)
|
|
log_alpha = g.softplus() * a
|
|
|
|
# qkv conv, conv_state is reset when starting from position 0
|
|
conv_state = initial.where(0, self.conv_state)
|
|
# assemble the conv window in a static-size buffer: [conv_state | qkv rows | zero-pad].
|
|
# padded steps are exact no-ops: beta=0 (delta rule off), log_alpha=0 (decay 1 after exp)
|
|
conv_window = conv_state.cat(self.attn_qkv(x).cast(conv_state.dtype), dim=1)
|
|
conv_window = conv_window.pad_to((B, self.ssm_conv_kernel-1 + T_pad, self.conv_channels)).contiguous()
|
|
# the last conv_kernel-1 columns of the window become the next conv state
|
|
conv_state_store = self.conv_state.uop.store(conv_window[:, T:T+self.ssm_conv_kernel-1].cast(self.conv_state.dtype).uop)
|
|
|
|
conv_out = functools.reduce(lambda a,b: a+b,
|
|
(conv_window[:, i:i+T_pad] * self.ssm_conv1d["weight"][:, i] for i in range(self.ssm_conv_kernel))).silu()
|
|
if symbolic:
|
|
out_gate = out_gate.pad_to((B, T_pad, self.num_v_heads, self.head_v_dim))
|
|
beta, log_alpha = beta.pad_to((B, T_pad, self.num_v_heads)), log_alpha.pad_to((B, T_pad, *log_alpha.shape[2:]))
|
|
q, k, v = conv_out.split([self.q_dim, self.q_dim, self.conv_channels - 2*self.q_dim], dim=-1)
|
|
q, k = (z.reshape(B, T_pad, self.num_k_heads, self.head_k_dim) for z in (q, k))
|
|
# l2 norm x/sqrt(sum(x²)+1e-6) as in the FLA kernels, then the v heads share the k heads
|
|
q, k = ((z.rmsnorm(eps=1e-6/self.head_k_dim) / self.head_k_dim**0.5).repeat(1, 1, self.num_v_heads//self.num_k_heads, 1) for z in (q, k))
|
|
v = v.reshape(B, T_pad, self.num_v_heads, self.head_v_dim)
|
|
# layout the per-step operands to broadcast against the (B, H, V, K) state
|
|
q, k, v, beta = (z.transpose(1, 2).float() for z in (q, k, v, beta))
|
|
q = q * self.head_k_dim**-0.5
|
|
alpha = log_alpha.transpose(1, 2).exp() # per-channel decay for kda, per-head otherwise (B, H, T, K|1)
|
|
|
|
# recurrent: scan over the (padded) tokens, updating the recurrent state. collect the per-step outputs
|
|
state = Tensor(self.recurrent_state.uop.after(conv_state_store)) # carry the conv write into this graph
|
|
if self.head_k_dim % 32 == 0 and self.head_v_dim % 4 == 0 and amd_custom_kernels_supported(x.device):
|
|
# one fused kernel for the whole scan; it resets and updates the recurrent state in place (RDNA3/4)
|
|
core = gated_delta_prefill(q, k, v, beta, alpha, state, Tensor(start_pos)).transpose(1, 2)
|
|
else:
|
|
q, k, v, beta = q.unsqueeze(-2), k.unsqueeze(-2), v.unsqueeze(-1), beta.unsqueeze(-1).unsqueeze(-1)
|
|
alpha = alpha.unsqueeze(-2)
|
|
state = initial.where(0, state.float())
|
|
outs = []
|
|
for t in range(T_pad):
|
|
s1 = state * alpha[:, :, t] # decay the state
|
|
delta = (v[:, :, t] - (s1*k[:, :, t]).sum(-1, keepdim=True)) * beta[:, :, t] # the delta rule update
|
|
state = s1 + delta * k[:, :, t]
|
|
outs.append((state * q[:, :, t]).sum(-1))
|
|
|
|
# store the updated recurrent state in place, then read the stacked outputs after the write
|
|
state_store = self.recurrent_state.uop.store(state.cast(self.recurrent_state.dtype).uop)
|
|
core = Tensor(outs[0].stack(*outs[1:], dim=1).contiguous().uop.after(state_store))
|
|
|
|
# output; undo the padding before the output projection
|
|
z = (self.ssm_norm(core) * (out_gate.sigmoid() if is_kda else out_gate.silu())).cast(x.dtype).contiguous()
|
|
if symbolic: z = z[:, :T]
|
|
return self.ssm_out(z.reshape(B, T, -1))
|
|
|
|
def _init_state(self, x):
|
|
if not hasattr(self, "conv_state"):
|
|
self.conv_state = Tensor.zeros(x.shape[0], self.ssm_conv_kernel-1, self.conv_channels, device=x.device).clone()
|
|
self.recurrent_state = Tensor.zeros(x.shape[0], self.num_v_heads, self.head_v_dim, self.head_k_dim, device=x.device).clone()
|
|
|
|
class Transformer:
|
|
def __init__(self, config:TransformerConfig):
|
|
dense_config = replace(config, num_experts=0, num_experts_per_tok=0, shared_expert_dim=0, hidden_dim=config.dense_hidden_dim or config.hidden_dim)
|
|
if config.ssm: config = replace(config, qk_norm=config.head_dim)
|
|
block_cls = MLATransformerBlock if config.kv_lora_rank > 0 else TransformerBlock
|
|
self.blk:list[FFNBlock] = []
|
|
for i in range(config.num_blocks):
|
|
c = dense_config if i < config.leading_dense_blocks else config
|
|
if config.sliding_window_pattern != 0 and (i+1) % config.sliding_window_pattern == 0: c = replace(c, sliding_window=0)
|
|
self.blk.append(GatedDeltaNetBlock(c, config.ssm) if config.ssm and config.ssm_layers[i] else block_cls(c))
|
|
self.token_embd = nn.Embedding(config.vocab_size, config.dim)
|
|
self.output_norm = nn.RMSNorm(config.dim, config.norm_eps)
|
|
self.output = Linear(config.dim, config.vocab_size, bias=False)
|
|
self.max_context = config.max_context
|
|
self.has_recurrent_block = any(isinstance(b, GatedDeltaNetBlock) for b in self.blk)
|
|
self._cached_tokens: list[int] = []
|
|
# we specialize the JIT for prefill and rollout
|
|
self.prefill_jit = TinyJit(self.forward)
|
|
self.rollout_jit = TinyJit(self.forward)
|
|
|
|
def forward(self, tokens:Tensor, start_pos:int|UOp, temperature:Tensor) -> Tensor:
|
|
x = self.token_embd(tokens.to(self.token_embd.weight.device)).float() # (B, T, D)
|
|
for block in self.blk: x = block(x, start_pos)
|
|
# only run the output projection on the last token
|
|
logits = self.output(self.output_norm(x[:, -1:]))[:, -1, :].to(tokens.device)
|
|
# Gumbel-max trick: argmax(logits/temp - log(-log(uniform))) is equivalent to sampling from softmax(logits/temp)
|
|
return (logits / temperature.maximum(1e-12) - (Tensor.rand_like(logits).maximum(1e-12).log().neg()).log()).argmax(-1, keepdim=True)
|
|
|
|
def __call__(self, tokens:Tensor, start_pos:int|UOp, temperature:Tensor) -> Tensor:
|
|
return (self.prefill_jit if resolve(tokens.shape[1] != 1) else self.rollout_jit)(tokens.contiguous(), start_pos, temperature)
|
|
|
|
@staticmethod
|
|
def from_gguf(gguf:Tensor|str|pathlib.Path, max_context:int|None=None,
|
|
realize=bool(getenv("REALIZE", 0)), shard:int=1) -> tuple[Transformer, dict]:
|
|
# TODO: remove the need for copy to default device
|
|
kv, entries = gguf_parse(gguf.to(None).realize() if isinstance(gguf, Tensor) else gguf)
|
|
arch = kv['general.architecture']
|
|
n_heads, n_kv_heads = kv[f'{arch}.attention.head_count'], kv[f'{arch}.attention.head_count_kv']
|
|
assert shard >= 1, f"shard must be at least 1, got {shard}"
|
|
shard_map:dict[str, int] = {}
|
|
if shard > 1:
|
|
heads = n_heads if kv.get(f'{arch}.attention.kv_lora_rank') else n_kv_heads
|
|
assert heads % shard == 0, f"tensor parallel needs the attention heads to split over {shard} devices"
|
|
# shard MLA heads and routed experts while replicating latent projections, KV cache and shared experts
|
|
rules = {**{w: 0 for w in ('token_embd.weight', 'output.weight', 'attn_q.weight', 'attn_k.weight', 'attn_v.weight', 'ffn_gate.weight',
|
|
'ffn_up.weight', 'attn_q_b.weight', 'attn_k_b.weight', 'attn_v_b.weight')},
|
|
**{w: 1 for w in ('attn_output.weight', 'ffn_down.weight', 'ffn_gate_exps.weight', 'ffn_up_exps.weight')}, 'ffn_down_exps.weight':2}
|
|
shard_map = {name: rules[k] for name in entries if (k:=re.sub(r"^blk\.\d+\.", "", name)) in rules}
|
|
devices = tuple(Device.canonicalize(f'{Device.DEFAULT}:{i}') for i in range(shard))
|
|
state_dict = gguf_shard(entries, devices, shard_map)
|
|
|
|
# all state items should be float16, not float32
|
|
state_dict = {k:v.cast('float16') if getenv("HALF", 1) else v for k,v in state_dict.items()}
|
|
|
|
# some models like Llama 3.2 don't have an output.weight, they just tie to the token_embd.weight
|
|
if 'output.weight' not in state_dict: state_dict['output.weight'] = state_dict['token_embd.weight']
|
|
|
|
max_context = min(max_context, kv[f'{arch}.context_length']) if max_context is not None else kv[f'{arch}.context_length']
|
|
|
|
num_blocks = kv[f'{arch}.block_count'] - kv.get(f'{arch}.nextn_predict_layers', 0)
|
|
ssm = None
|
|
ssm_layers: tuple[bool, ...] = ()
|
|
if arch in ('qwen35', 'qwen35moe'):
|
|
ssm = SSMConfig(**{k: kv[f'{arch}.ssm.{k}'] for k in ('conv_kernel','state_size','group_count','time_step_rank','inner_size')})
|
|
ssm_layers = tuple((i+1) % kv[f'{arch}.full_attention_interval'] != 0 for i in range(num_blocks))
|
|
elif arch == 'kimi-linear':
|
|
ssm_layers = tuple(x == 0 for x in n_kv_heads)[:num_blocks]
|
|
n_kv_heads = max(n_kv_heads)
|
|
ssm = SSMConfig(kv[f'{arch}.ssm.conv_kernel'], kv[f'{arch}.kda.head_dim'], n_heads, n_heads, n_heads*kv[f'{arch}.kda.head_dim'], kda=True)
|
|
for i, is_ssm in enumerate(ssm_layers):
|
|
if not is_ssm: continue
|
|
state_dict[f"blk.{i}.ssm_a"] = state_dict[f"blk.{i}.ssm_a"].reshape(n_heads, 1)
|
|
state_dict[f"blk.{i}.attn_qkv.weight"] = state_dict.pop(f"blk.{i}.attn_q.weight").cat(
|
|
state_dict.pop(f"blk.{i}.attn_k.weight"), state_dict.pop(f"blk.{i}.attn_v.weight"), dim=0).contiguous()
|
|
state_dict[f"blk.{i}.ssm_conv1d.weight"] = state_dict.pop(f"blk.{i}.ssm_conv1d_q.weight").cat(
|
|
state_dict.pop(f"blk.{i}.ssm_conv1d_k.weight"), state_dict.pop(f"blk.{i}.ssm_conv1d_v.weight"), dim=0).squeeze(1).contiguous()
|
|
state_dict[f"blk.{i}.ssm_out.weight"] = state_dict.pop(f"blk.{i}.attn_output.weight")
|
|
if arch in ('qwen35', 'qwen35moe', 'glm4moe', 'gpt-oss'):
|
|
state_dict = {k.replace('post_attention_norm', 'ffn_norm'):v for k,v in state_dict.items()}
|
|
|
|
kv_lora_rank = kv.get(f'{arch}.attention.kv_lora_rank', 0)
|
|
head_dim = kv.get(f'{arch}.attention.key_length_mla', kv.get(f'{arch}.attention.key_length', kv[f'{arch}.embedding_length'] // n_heads))
|
|
rope_dim = kv.get(f'{arch}.rope.dimension_count', head_dim)
|
|
yarn = YaRNConfig(factor=kv[f'{arch}.rope.scaling.factor'],
|
|
orig_ctx_len=kv.get(f'{arch}.rope.scaling.original_context_length', kv[f'{arch}.context_length']),
|
|
beta_fast=kv.get(f'{arch}.rope.scaling.yarn_beta_fast', 32.0),
|
|
beta_slow=kv.get(f'{arch}.rope.scaling.yarn_beta_slow', 1.0)) if kv.get(f'{arch}.rope.scaling.type') == 'yarn' else None
|
|
|
|
# Permute RoPE weights from interleaved to half-split layout.
|
|
for name in state_dict:
|
|
if arch == 'kimi-linear': continue
|
|
if ('attn_q.weight' in name or 'attn_q_b.weight' in name) and (arch == 'llama' or kv_lora_rank):
|
|
w = state_dict[name].reshape(n_heads, state_dict[name].shape[0]//n_heads, -1)
|
|
prefix = head_dim-rope_dim
|
|
state_dict[name] = w[:, :prefix].cat(w[:, prefix:].rearrange("n (h two) d -> n (two h) d", two=2), dim=1).reshape(-1, w.shape[-1])
|
|
elif arch == 'llama' and 'attn_k.weight' in name:
|
|
w = state_dict[name].reshape(n_kv_heads, state_dict[name].shape[0]//n_kv_heads, -1)
|
|
state_dict[name] = w.rearrange("n (h two) d -> n (two h) d", two=2).reshape(-1, w.shape[-1])
|
|
elif kv_lora_rank and 'attn_kv_a_mqa.weight' in name:
|
|
state_dict[name] = state_dict[name][:kv_lora_rank].cat(state_dict[name][kv_lora_rank:].rearrange("(h two) d -> (two h) d", two=2), dim=0)
|
|
config = TransformerConfig(
|
|
num_blocks=num_blocks, dim=kv[f'{arch}.embedding_length'],
|
|
hidden_dim=kv.get(f'{arch}.expert_feed_forward_length', kv.get(f'{arch}.feed_forward_length', 0)),
|
|
n_heads=n_heads, n_kv_heads=n_kv_heads, norm_eps=kv[f'{arch}.attention.layer_norm_rms_epsilon'],
|
|
vocab_size=len(kv['tokenizer.ggml.tokens']),
|
|
head_dim=head_dim,
|
|
rope_theta=kv.get(f'{arch}.rope.freq_base', 10000.0), rope_dim=rope_dim, yarn=yarn,
|
|
v_head_dim=kv.get(f'{arch}.attention.value_length_mla', kv.get(f'{arch}.attention.value_length', head_dim)),
|
|
max_context=max_context,
|
|
qk_norm=int(state_dict['blk.0.attn_q_norm.weight'].shape[0]) if 'blk.0.attn_q_norm.weight' in state_dict else 0,
|
|
num_experts=kv.get(f'{arch}.expert_count', 0), num_experts_per_tok=kv.get(f'{arch}.expert_used_count', 0),
|
|
norm_topk_prob=kv.get(f'{arch}.expert_weights_norm', arch in ('qwen3moe', 'qwen35moe', 'kimi-linear')),
|
|
expert_gating_func=ExpertGating(kv.get(f'{arch}.expert_gating_func',
|
|
ExpertGating.SOFTMAX_WEIGHT if arch == 'gpt-oss' else ExpertGating.SOFTMAX)),
|
|
kv_lora_rank=kv_lora_rank, q_lora_rank=kv.get(f'{arch}.attention.q_lora_rank', 0),
|
|
leading_dense_blocks=kv.get(f'{arch}.leading_dense_block_count', 0),
|
|
shared_expert_dim=kv.get(
|
|
f'{arch}.expert_shared_feed_forward_length',
|
|
kv.get(f'{arch}.expert_shared_count', 0) * kv.get(f'{arch}.expert_feed_forward_length', 0)),
|
|
shared_expert_gate=f"blk.{kv.get(f'{arch}.leading_dense_block_count', 0)}.ffn_gate_inp_shexp.weight" in state_dict,
|
|
dense_hidden_dim=kv.get(f'{arch}.feed_forward_length', 0) if kv.get(f'{arch}.leading_dense_block_count', 0) else 0,
|
|
routed_scaling_factor=kv.get(f'{arch}.expert_weights_scale', 1.0), attn_output_gate=arch in ('qwen35', 'qwen35moe'), ssm=ssm,
|
|
ssm_layers=ssm_layers,
|
|
qkv_bias='blk.0.attn_q.bias' in state_dict,
|
|
expert_bias=f"blk.{kv.get(f'{arch}.leading_dense_block_count', 0)}.exp_probs_b.bias" in state_dict,
|
|
expert_proj_bias='blk.0.ffn_gate_exps.bias' in state_dict, attn_output_bias='blk.0.attn_output.bias' in state_dict,
|
|
swiglu_alpha=1.702 if arch == 'gpt-oss' else 1.0, swiglu_limit=7.0 if arch == 'gpt-oss' else kv.get(f'{arch}.swiglu_clamp_exp', [None])[0],
|
|
swiglu_up_bias=1.0 if arch == 'gpt-oss' else 0.0, attn_sinks='blk.0.attn_sinks.weight' in state_dict,
|
|
sliding_window=kv.get(f'{arch}.attention.sliding_window', 0),
|
|
sliding_window_pattern=kv.get(f'{arch}.attention.sliding_window_pattern', 2 if arch == 'gpt-oss' else 0))
|
|
model = Transformer(config)
|
|
for p in (nn.state.get_parameters(model) if shard > 1 else []): p.to_(devices)
|
|
nn.state.load_state_dict(model, state_dict, verbose=False, consume=True, realize=False) # NOTE: rope_freqs.weight (32,) is unused
|
|
# NOTE: without this contiguous, it unpacks the weights from the model every time. we shouldn't need this, but for now it's faster
|
|
if realize:
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for s in (params:=nn.state.get_parameters(model)): s.replace(s.contiguous())
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Tensor.realize(*params)
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return model, kv
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|
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def warmup(self):
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for _ in range(2): list(zip(range(2), self.generate([0])))
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|
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def get_start_pos(self, tokens:list[int]) -> int:
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# recurrent state can't be partially reused after divergence: reuse it only when tokens extend the cached prefix
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if self.has_recurrent_block:
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return len(self._cached_tokens) if self._cached_tokens and len(self._cached_tokens) < len(tokens) \
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and tokens[:len(self._cached_tokens)] == self._cached_tokens else 0
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prefix_len = sum(1 for _ in itertools.takewhile(lambda ab: ab[0] == ab[1], zip(tokens[:-1], self._cached_tokens)))
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return min(block._reusable_prefix_len(prefix_len, len(self._cached_tokens)) for block in self.blk)
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|
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|
def generate(self, tokens:list[int], chunk_size:int=32, temperature:float=0.0):
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|
if self.has_recurrent_block and not amd_custom_kernels_supported(self.token_embd.weight.device): chunk_size = 1
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v_start_pos = UOp.variable("start_pos", 0, self.max_context-1)
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v_toks = UOp.variable("toks", 1, chunk_size)
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|
# TODO: use UOp.variable for temperature once float variables are supported
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|
temp = Tensor([temperature])
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|
# assign all input tokens once, then slice from start_pos for the model call
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|
t = Tensor(tokens + [0] * (self.max_context - len(tokens)), dtype="int32").reshape(1, self.max_context)
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|
# recompute start_pos from what's currently valid in the caches
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|
start_pos = self.get_start_pos(tokens)
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|
out, prompt_len = None, len(tokens)
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|
while len(tokens) < self.max_context:
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|
n_toks = min(chunk_size, len(tokens) - start_pos)
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|
sp, nt = v_start_pos.bind(start_pos), v_toks.bind(n_toks)
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out = self(t[:, sp:sp+nt] if start_pos < prompt_len or out is None else out, sp, temp).realize()
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|
start_pos += n_toks
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|
# chunked prefill: keep processing until all prompt tokens are consumed
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|
if start_pos < len(tokens): continue
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|
tokens.append(int(out.item()))
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|
self._cached_tokens = tokens[:-1]
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|
yield tokens[-1]
|