|
|
@@ -1,257 +0,0 @@
|
|
|
-from typing import Tuple, Union, Optional, Dict, Any
|
|
|
-from tinygrad import Tensor, Variable, TinyJit, dtypes, nn, Device
|
|
|
-from tinygrad.helpers import getenv
|
|
|
-
|
|
|
-
|
|
|
-# https://github.com/facebookresearch/llama/blob/1076b9c51c77ad06e9d7ba8a4c6df775741732bd/llama/model.py#L47
|
|
|
-def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, dtype=dtypes.half) -> Tensor:
|
|
|
- freqs = 1.0/(theta**(Tensor.arange(0, dim, 2)[:(dim // 2)]/dim))
|
|
|
- freqs = Tensor.arange(end).unsqueeze(dim=1)*freqs.unsqueeze(dim=0)
|
|
|
- # TODO: move dtype outside this
|
|
|
- return Tensor.stack(freqs.cos().cast(dtype), freqs.sin().cast(dtype), dim=-1).reshape(1, end, 1, dim // 2, 2)
|
|
|
-
|
|
|
-
|
|
|
-# (a+i*b) * (c+i*d) = (ac-bd) + i*(ad+bc)
|
|
|
-def complex_mult(A, c, d):
|
|
|
- a, b = A[..., 0:1], A[..., 1:2]
|
|
|
- ro = a*c - b*d
|
|
|
- co = a*d + b*c
|
|
|
- return ro.cat(co, dim=-1)
|
|
|
-
|
|
|
-
|
|
|
-def apply_rotary_emb(xq: Tensor, xk: Tensor, freqs_cis: Tensor) -> Tuple[Tensor, Tensor]:
|
|
|
- assert freqs_cis.shape[1] == xq.shape[1] == xk.shape[1], f"freqs_cis shape mismatch {freqs_cis.shape} xq:{xq.shape} xk:{xk.shape}"
|
|
|
- xq = xq.reshape(*xq.shape[0:-1], -1, 2)
|
|
|
- xk = xk.reshape(*xk.shape[0:-1], -1, 2)
|
|
|
- assert len(xq.shape) == len(xk.shape) == len(freqs_cis.shape) == 5
|
|
|
- c, d = freqs_cis[..., 0:1], freqs_cis[..., 1:2]
|
|
|
- xq_out = complex_mult(xq, c, d)
|
|
|
- xk_out = complex_mult(xk, c, d)
|
|
|
- return xq_out.flatten(3), xk_out.flatten(3)
|
|
|
-
|
|
|
-
|
|
|
-def repeat_kv(x: Tensor, n_rep: int) -> Tensor:
|
|
|
- bs, seqlen, n_kv_heads, head_dim = x.shape
|
|
|
- if n_rep == 1: return x
|
|
|
- # NOTE: this is different from x.repeat((1, 1, n_rep, 1))
|
|
|
- return x.repeat((1, 1, 1, n_rep)).reshape(bs, seqlen, n_kv_heads*n_rep, head_dim)
|
|
|
-
|
|
|
-
|
|
|
-class Attention:
|
|
|
- def __init__(self, dim, n_heads, n_kv_heads, max_context, linear=nn.Linear):
|
|
|
- self.n_heads = n_heads
|
|
|
- self.n_kv_heads = n_kv_heads if n_kv_heads is not None else n_heads # n_kv_heads != n_heads implies MQA [arxiv/2307.09288, A.2.1]
|
|
|
- self.head_dim = dim // n_heads
|
|
|
- self.n_rep = self.n_heads // self.n_kv_heads
|
|
|
- self.max_context = max_context
|
|
|
-
|
|
|
- self.wq = linear(dim, self.n_heads*self.head_dim, bias=False)
|
|
|
- self.wk = linear(dim, self.n_kv_heads*self.head_dim, bias=False)
|
|
|
- self.wv = linear(dim, self.n_kv_heads*self.head_dim, bias=False)
|
|
|
- self.wo = linear(self.n_heads*self.head_dim, dim, bias=False)
|
|
|
-
|
|
|
- def __call__(self, x: Tensor, start_pos: Union[Variable, int], freqs_cis: Tensor, mask: Optional[Tensor]) -> Tensor:
|
|
|
- if getenv("WQKV"):
|
|
|
- if not hasattr(self, 'wqkv'): self.wqkv = Tensor.cat(self.wq.weight, self.wk.weight, self.wv.weight)
|
|
|
- xqkv = x @ self.wqkv.T
|
|
|
- xq, xk, xv = xqkv.split([self.wq.weight.shape[0], self.wk.weight.shape[0], self.wv.weight.shape[0]], dim=2)
|
|
|
- else:
|
|
|
- xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
|
|
|
-
|
|
|
- xq = xq.reshape(xq.shape[0], xq.shape[1], self.n_heads, self.head_dim)
|
|
|
- xk = xk.reshape(xk.shape[0], xk.shape[1], self.n_kv_heads, self.head_dim)
|
|
|
- xv = xv.reshape(xv.shape[0], xv.shape[1], self.n_kv_heads, self.head_dim)
|
|
|
-
|
|
|
- xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
|
|
|
- bsz, seqlen, _, _ = xq.shape
|
|
|
-
|
|
|
- # create kv cache
|
|
|
- if not hasattr(self, "cache_kv"):
|
|
|
- self.cache_kv = Tensor.zeros(2, bsz, self.max_context, self.n_kv_heads, self.head_dim, dtype=x.dtype).contiguous().realize()
|
|
|
- if isinstance(x.device, tuple):
|
|
|
- # TODO: instead of specifying how to shard, it can follow how xk and xv are being sharded
|
|
|
- self.cache_kv.shard_((x.device), axis=3 if getenv("SHARD_KVCACHE") else None).realize()
|
|
|
-
|
|
|
- # update the cache
|
|
|
- assert xk.dtype == xv.dtype == self.cache_kv.dtype, f"{xk.dtype=}, {xv.dtype=}, {self.cache_kv.dtype=}"
|
|
|
- self.cache_kv.shrink((None, None, (start_pos, start_pos + seqlen), None, None)).assign(Tensor.stack(xk, xv)).realize()
|
|
|
-
|
|
|
- keys = self.cache_kv[0].shrink((None, (0, start_pos + seqlen), None, None)) if start_pos > 0 else xk
|
|
|
- values = self.cache_kv[1].shrink((None, (0, start_pos + seqlen), None, None)) if start_pos > 0 else xv
|
|
|
-
|
|
|
- keys, values = repeat_kv(keys, self.n_rep), repeat_kv(values, self.n_rep)
|
|
|
- xq, keys, values = xq.transpose(1, 2), keys.transpose(1, 2), values.transpose(1, 2)
|
|
|
- attn = xq.scaled_dot_product_attention(keys, values, mask).transpose(1, 2)
|
|
|
- attn = attn.reshape(bsz, seqlen, -1)
|
|
|
- return self.wo(attn)
|
|
|
-
|
|
|
-
|
|
|
-class FeedForward:
|
|
|
- def __init__(self, dim: int, hidden_dim: int, linear=nn.Linear):
|
|
|
- self.w1 = linear(dim, hidden_dim, bias=False)
|
|
|
- self.w2 = linear(hidden_dim, dim, bias=False)
|
|
|
- self.w3 = linear(dim, hidden_dim, bias=False) # the gate in Gated Linear Unit
|
|
|
-
|
|
|
- def __call__(self, x: Tensor) -> Tensor:
|
|
|
- return self.w2(self.w1(x).silu()*self.w3(x)) # SwiGLU [arxiv/2002.05202, eq (5)]
|
|
|
-
|
|
|
-
|
|
|
-class TransformerBlock:
|
|
|
- def __init__(self, dim: int, hidden_dim: int, n_heads: int, n_kv_heads: int, norm_eps: float, max_context: int, linear=nn.Linear, feed_forward=FeedForward):
|
|
|
- self.attention = Attention(dim, n_heads, n_kv_heads, max_context, linear)
|
|
|
- self.feed_forward = feed_forward(dim, hidden_dim, linear)
|
|
|
- self.attention_norm = nn.RMSNorm(dim, norm_eps)
|
|
|
- self.ffn_norm = nn.RMSNorm(dim, norm_eps)
|
|
|
-
|
|
|
- def __call__(self, x: Tensor, start_pos: Union[Variable, int], freqs_cis: Tensor, mask: Optional[Tensor]):
|
|
|
- h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask)
|
|
|
- return (h + self.feed_forward(self.ffn_norm(h))).contiguous()
|
|
|
-
|
|
|
-
|
|
|
-# standard openai sampling
|
|
|
-def sample(logits: Tensor, temp: float, k: int, p: float, af: float, ap: float):
|
|
|
- assert logits.ndim == 1, "only works on 1d tensors"
|
|
|
- assert 0 <= p <= 1, "p must be between 0 and 1"
|
|
|
- assert 0 <= k <= logits.numel(), "k must be between 0 and numel"
|
|
|
-
|
|
|
- # if temperature is very low just use argmax
|
|
|
- if temp < 1e-6: return logits.argmax().reshape(1)
|
|
|
-
|
|
|
- # alpha sampling
|
|
|
- if af or ap:
|
|
|
- if not hasattr(sample, "alpha_counter"):
|
|
|
- setattr(sample, "alpha_counter", Tensor.zeros_like(logits, dtype=dtypes.int32).contiguous())
|
|
|
- logits = logits - (sample.alpha_counter*af + (sample.alpha_counter > 0)*ap)
|
|
|
-
|
|
|
- # replace NaNs with -inf
|
|
|
- logits = (logits != logits).where(-float("inf"), logits)
|
|
|
-
|
|
|
- # softmax
|
|
|
- t = (logits/temp).softmax()
|
|
|
-
|
|
|
- counter, counter2 = Tensor.arange(t.numel(), device=logits.device).contiguous(), Tensor.arange(t.numel() - 1, -1, -1, device=logits.device).contiguous()
|
|
|
- # top k
|
|
|
- if k:
|
|
|
- output, output_indices = Tensor.zeros(k, device=logits.device).contiguous(), Tensor.zeros(k, device=logits.device, dtype=dtypes.int32).contiguous()
|
|
|
- for i in range(k):
|
|
|
- t_argmax = (t.numel() - ((t == (t_max := t.max()))*counter2).max() - 1).cast(dtypes.default_int)
|
|
|
- output = output + t_max.unsqueeze(0).pad(((i, k - i - 1),))
|
|
|
- output_indices = output_indices + t_argmax.unsqueeze(0).pad(((i, k - i - 1),))
|
|
|
- t = (counter == t_argmax).where(0, t)
|
|
|
-
|
|
|
- # approximate top p
|
|
|
- # because we are already limited to top k elements we can do top p "without sorting"
|
|
|
- output_cumsum = output[::-1]._cumsum()[::-1] + t.sum()
|
|
|
- output = (output_cumsum >= (1 - p))*output
|
|
|
- output_indices = (output_cumsum >= (1 - p))*output_indices
|
|
|
-
|
|
|
- # sample
|
|
|
- output_idx = output.multinomial()
|
|
|
- output_token = output_indices[output_idx]
|
|
|
- else:
|
|
|
- output_token = t.multinomial()
|
|
|
-
|
|
|
- # increase alpha counter
|
|
|
- if af or ap:
|
|
|
- sample.alpha_counter = (counter == output_token).where(sample.alpha_counter + 1, sample.alpha_counter)
|
|
|
-
|
|
|
- return output_token
|
|
|
-
|
|
|
-
|
|
|
-from exo.inference.shard import Shard
|
|
|
-
|
|
|
-
|
|
|
-class Transformer:
|
|
|
- def __init__(
|
|
|
- self,
|
|
|
- dim: int,
|
|
|
- hidden_dim: int,
|
|
|
- n_heads: int,
|
|
|
- n_layers: int,
|
|
|
- norm_eps: float,
|
|
|
- vocab_size,
|
|
|
- shard: Shard = None,
|
|
|
- linear=nn.Linear,
|
|
|
- n_kv_heads=None,
|
|
|
- rope_theta=10000,
|
|
|
- max_context=1024,
|
|
|
- jit=True,
|
|
|
- feed_forward=FeedForward
|
|
|
- ):
|
|
|
- self.layers = [TransformerBlock(dim, hidden_dim, n_heads, n_kv_heads, norm_eps, max_context, linear, feed_forward=feed_forward) for _ in range(n_layers)]
|
|
|
- self.norm = nn.RMSNorm(dim, norm_eps)
|
|
|
- self.tok_embeddings = nn.Embedding(vocab_size, dim)
|
|
|
- self.output = nn.Linear(dim, vocab_size, bias=False)
|
|
|
- self.max_context = max_context
|
|
|
- self.freqs_cis = precompute_freqs_cis(dim // n_heads, self.max_context*2, rope_theta).contiguous()
|
|
|
- self.forward_jit = TinyJit(self.forward) if jit else None
|
|
|
- self.shard = shard
|
|
|
-
|
|
|
- def forward(self, x: Tensor, start_pos: Union[Variable, int], temperature: float, top_k: int, top_p: float, alpha_f: float, alpha_p: float):
|
|
|
- seqlen = x.shape[1]
|
|
|
- freqs_cis = self.freqs_cis.shrink((None, (start_pos, start_pos + seqlen), None, None, None))
|
|
|
- mask = Tensor.full((1, 1, seqlen, start_pos + seqlen), float("-100000000"), dtype=x.dtype, device=x.device).triu(start_pos + 1).realize() if seqlen > 1 else None
|
|
|
-
|
|
|
- if self.shard.is_first_layer():
|
|
|
- h = self.tok_embeddings(x)
|
|
|
- else:
|
|
|
- h = x
|
|
|
-
|
|
|
- for i in range(self.shard.start_layer, self.shard.end_layer + 1):
|
|
|
- layer = self.layers[i]
|
|
|
- h = layer(h, start_pos, freqs_cis, mask)
|
|
|
-
|
|
|
- if self.shard.is_last_layer():
|
|
|
- logits = self.output(self.norm(h)).float()[:, -1, :]
|
|
|
- return sample(logits.flatten(), temperature, top_k, top_p, alpha_f, alpha_p).realize()
|
|
|
- else:
|
|
|
- return h
|
|
|
-
|
|
|
- def __call__(self, tokens: Tensor, start_pos: Variable, temperature: float = 0.0, top_k: int = 0, top_p: float = 0.8, alpha_f: float = 0.0, alpha_p: float = 0.0):
|
|
|
- # TODO: better way to handle the first call v.s. the rest?
|
|
|
- if tokens.shape[0:2] == (1, 1) and self.forward_jit is not None:
|
|
|
- return self.forward_jit(tokens, Variable("start_pos", 0, self.max_context).bind(start_pos), temperature, top_k, top_p, alpha_f, alpha_p)
|
|
|
- return self.forward(tokens, start_pos, temperature, top_k, top_p, alpha_f, alpha_p)
|
|
|
-
|
|
|
-
|
|
|
-# *** helpers ***
|
|
|
-
|
|
|
-
|
|
|
-def convert_from_huggingface(weights: Dict[str, Tensor], model: Transformer, n_heads: int, n_kv_heads: int):
|
|
|
- def permute(v: Tensor, n_heads: int):
|
|
|
- return v.reshape(n_heads, 2, v.shape[0] // n_heads // 2, v.shape[1]).transpose(1, 2).reshape(*v.shape[:2])
|
|
|
-
|
|
|
- keymap = {
|
|
|
- "model.embed_tokens.weight": "tok_embeddings.weight",
|
|
|
- **{f"model.layers.{l}.input_layernorm.weight": f"layers.{l}.attention_norm.weight"
|
|
|
- for l in range(len(model.layers))},
|
|
|
- **{f"model.layers.{l}.self_attn.{x}_proj.weight": f"layers.{l}.attention.w{x}.weight"
|
|
|
- for x in ["q", "k", "v", "o"]
|
|
|
- for l in range(len(model.layers))},
|
|
|
- **{f"model.layers.{l}.post_attention_layernorm.weight": f"layers.{l}.ffn_norm.weight"
|
|
|
- for l in range(len(model.layers))},
|
|
|
- **{f"model.layers.{l}.mlp.{x}_proj.weight": f"layers.{l}.feed_forward.w{y}.weight"
|
|
|
- for x, y in {"gate": "1", "down": "2", "up": "3"}.items()
|
|
|
- for l in range(len(model.layers))},
|
|
|
- "model.norm.weight": "norm.weight",
|
|
|
- "lm_head.weight": "output.weight",
|
|
|
- }
|
|
|
- sd = {}
|
|
|
- for k, v in weights.items():
|
|
|
- if ".rotary_emb." in k: continue
|
|
|
- v = v.to(Device.DEFAULT)
|
|
|
- if "model.layers" in k:
|
|
|
- if "q_proj" in k:
|
|
|
- v = permute(v, n_heads)
|
|
|
- elif "k_proj" in k:
|
|
|
- v = permute(v, n_kv_heads)
|
|
|
- sd[keymap[k]] = v
|
|
|
- return sd
|
|
|
-
|
|
|
-
|
|
|
-def fix_bf16(weights: Dict[Any, Tensor]):
|
|
|
- if getenv("SUPPORT_BF16", 1):
|
|
|
- # TODO: without casting to float16, 70B llama OOM on tinybox.
|
|
|
- return {k: v.cast(dtypes.float16) if v.dtype == dtypes.bfloat16 else v for k, v in weights.items()}
|
|
|
- # TODO: check if device supports bf16
|
|
|
- return {k: v.llvm_bf16_cast(dtypes.half).to(v.device) if v.dtype == dtypes.bfloat16 else v for k, v in weights.items()}
|
Alex Cheema