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+from typing import Tuple, Union, Optional, Dict, Any
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+from tinygrad import Tensor, Variable, TinyJit, dtypes, nn, Device
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+from tinygrad.helpers import getenv
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+
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+# https://github.com/facebookresearch/llama/blob/1076b9c51c77ad06e9d7ba8a4c6df775741732bd/llama/model.py#L47
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+def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0, dtype=dtypes.half) -> Tensor:
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+ freqs = 1.0 / (theta ** (Tensor.arange(0, dim, 2)[:(dim // 2)] / dim))
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+ freqs = Tensor.arange(end).unsqueeze(dim=1) * freqs.unsqueeze(dim=0)
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+ # TODO: move dtype outside this
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+ return Tensor.stack(freqs.cos().cast(dtype), freqs.sin().cast(dtype), dim=-1).reshape(1, end, 1, dim//2, 2)
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+
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+# (a+i*b) * (c+i*d) = (ac-bd) + i*(ad+bc)
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+def complex_mult(A, c, d):
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+ a,b = A[..., 0:1], A[..., 1:2]
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+ ro = a*c - b*d
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+ co = a*d + b*c
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+ return ro.cat(co, dim=-1)
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+
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+def apply_rotary_emb(xq:Tensor, xk:Tensor, freqs_cis:Tensor) -> Tuple[Tensor, Tensor]:
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+ 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}"
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+ xq = xq.reshape(*xq.shape[0:-1], -1, 2)
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+ xk = xk.reshape(*xk.shape[0:-1], -1, 2)
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+ assert len(xq.shape) == len(xk.shape) == len(freqs_cis.shape) == 5
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+ c, d = freqs_cis[..., 0:1], freqs_cis[..., 1:2]
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+ xq_out = complex_mult(xq, c, d)
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+ xk_out = complex_mult(xk, c, d)
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+ return xq_out.flatten(3), xk_out.flatten(3)
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+
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+def repeat_kv(x:Tensor, n_rep:int) -> Tensor:
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+ bs, seqlen, n_kv_heads, head_dim = x.shape
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+ if n_rep == 1: return x
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+ # NOTE: this is different from x.repeat((1, 1, n_rep, 1))
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+ return x.repeat((1, 1, 1, n_rep)).reshape(bs, seqlen, n_kv_heads * n_rep, head_dim)
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+
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+class Attention:
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+ def __init__(self, dim, n_heads, n_kv_heads, max_context, linear=nn.Linear):
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+ self.n_heads = n_heads
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+ 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]
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+ self.head_dim = dim // n_heads
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+ self.n_rep = self.n_heads // self.n_kv_heads
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+ self.max_context = max_context
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+
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+ self.wq = linear(dim, self.n_heads * self.head_dim, bias=False)
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+ self.wk = linear(dim, self.n_kv_heads * self.head_dim, bias=False)
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+ self.wv = linear(dim, self.n_kv_heads * self.head_dim, bias=False)
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+ self.wo = linear(self.n_heads * self.head_dim, dim, bias=False)
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+
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+ def __call__(self, x:Tensor, start_pos:Union[Variable,int], freqs_cis:Tensor, mask:Optional[Tensor]) -> Tensor:
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+ xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
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+ xq = xq.reshape(xq.shape[0], xq.shape[1], self.n_heads, self.head_dim)
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+ xk = xk.reshape(xk.shape[0], xk.shape[1], self.n_kv_heads, self.head_dim)
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+ xv = xv.reshape(xv.shape[0], xv.shape[1], self.n_kv_heads, self.head_dim)
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+
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+ xq, xk = apply_rotary_emb(xq, xk, freqs_cis)
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+ bsz, seqlen, _, _ = xq.shape
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+
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+ # create kv cache
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+ if not hasattr(self, "cache_kv"):
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+ self.cache_kv = Tensor.zeros(2, bsz, self.max_context, self.n_kv_heads, self.head_dim, dtype=x.dtype).contiguous().realize()
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+ if isinstance(x.device, tuple):
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+ # TODO: instead of specifying how to shard, it can follow how xk and xv are being sharded
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+ self.cache_kv.shard_((x.device), axis=None).realize()
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+
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+ # update the cache
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+ assert xk.dtype == xv.dtype == self.cache_kv.dtype, f"{xk.dtype=}, {xv.dtype=}, {self.cache_kv.dtype=}"
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+ self.cache_kv.shrink((None, None, (start_pos, start_pos+seqlen), None, None)).assign(Tensor.stack(xk, xv)).realize()
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+
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+ keys = self.cache_kv[0].shrink((None, (0, start_pos+seqlen), None, None)) if start_pos > 0 else xk
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+ values = self.cache_kv[1].shrink((None, (0, start_pos+seqlen), None, None)) if start_pos > 0 else xv
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+
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+ keys, values = repeat_kv(keys, self.n_rep), repeat_kv(values, self.n_rep)
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+ xq, keys, values = xq.transpose(1, 2), keys.transpose(1, 2), values.transpose(1, 2)
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+ attn = xq.scaled_dot_product_attention(keys, values, mask).transpose(1, 2)
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+ attn = attn.reshape(bsz, seqlen, -1)
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+ return self.wo(attn)
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+
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+class FeedForward:
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+ def __init__(self, dim:int, hidden_dim:int, linear=nn.Linear):
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+ self.w1 = linear(dim, hidden_dim, bias=False)
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+ self.w2 = linear(hidden_dim, dim, bias=False)
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+ self.w3 = linear(dim, hidden_dim, bias=False) # the gate in Gated Linear Unit
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+
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+ def __call__(self, x:Tensor) -> Tensor:
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+ return self.w2(self.w1(x).silu() * self.w3(x)) # SwiGLU [arxiv/2002.05202, eq (5)]
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+
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+class TransformerBlock:
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+ 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):
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+ self.attention = Attention(dim, n_heads, n_kv_heads, max_context, linear)
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+ self.feed_forward = feed_forward(dim, hidden_dim, linear)
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+ self.attention_norm = nn.RMSNorm(dim, norm_eps)
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+ self.ffn_norm = nn.RMSNorm(dim, norm_eps)
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+
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+ def __call__(self, x:Tensor, start_pos:Union[Variable,int], freqs_cis:Tensor, mask:Optional[Tensor]):
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+ h = x + self.attention(self.attention_norm(x), start_pos, freqs_cis, mask)
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+ return (h + self.feed_forward(self.ffn_norm(h))).contiguous()
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+
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+# standard openai sampling
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+def sample(logits: Tensor, temp: float, k: int, p: float, af: float, ap: float):
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+ assert logits.ndim == 1, "only works on 1d tensors"
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+ assert 0 <= p <= 1, "p must be between 0 and 1"
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+ assert 0 <= k <= logits.numel(), "k must be between 0 and numel"
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+
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+ # if temperature is very low just use argmax
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+ if temp < 1e-6: return logits.argmax()
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+
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+ # alpha sampling
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+ if af or ap:
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+ if not hasattr(sample, "alpha_counter"):
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+ setattr(sample, "alpha_counter", Tensor.zeros_like(logits, dtype=dtypes.int32).contiguous())
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+ logits = logits - (sample.alpha_counter * af + (sample.alpha_counter > 0) * ap)
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+
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+ # replace NaNs with -inf
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+ logits = (logits != logits).where(-float("inf"), logits)
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+
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+ # softmax
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+ t = (logits / temp).softmax()
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+
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+ counter, counter2 = Tensor.arange(t.numel(), device=logits.device).contiguous(), Tensor.arange(t.numel() - 1, -1, -1, device=logits.device).contiguous()
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+ # top k
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+ if k:
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+ output, output_indices = Tensor.zeros(k, device=logits.device).contiguous(), Tensor.zeros(k, device=logits.device, dtype=dtypes.int32).contiguous()
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+ for i in range(k):
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+ t_argmax = (t.numel() - ((t == (t_max := t.max())) * counter2).max() - 1).cast(dtypes.default_int)
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+ output = output + t_max.unsqueeze(0).pad(((i, k - i - 1),))
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+ output_indices = output_indices + t_argmax.unsqueeze(0).pad(((i, k - i - 1),))
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+ t = (counter == t_argmax).where(0, t)
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+
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+ # approximate top p
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+ # because we are already limited to top k elements we can do top p "without sorting"
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+ output_cumsum = output[::-1]._cumsum()[::-1] + t.sum()
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+ output = (output_cumsum >= (1 - p)) * output
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+ output_indices = (output_cumsum >= (1 - p)) * output_indices
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+
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+ # sample
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+ output_idx = output.multinomial()
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+ output_token = output_indices[output_idx]
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+ else:
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+ output_token = t.multinomial()
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+
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+ # increase alpha counter
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+ if af or ap:
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+ sample.alpha_counter = (counter == output_token).where(sample.alpha_counter + 1, sample.alpha_counter)
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+
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+ return output_token
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+
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+class Transformer:
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+ def __init__(self, dim:int, hidden_dim:int, n_heads:int, n_layers:int, norm_eps:float, vocab_size, linear=nn.Linear, n_kv_heads=None, rope_theta=10000, max_context=1024, jit=True, feed_forward=FeedForward):
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+ 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)]
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+ self.norm = nn.RMSNorm(dim, norm_eps)
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+ self.tok_embeddings = nn.Embedding(vocab_size, dim)
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+ self.output = nn.Linear(dim, vocab_size, bias=False)
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+ self.max_context = max_context
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+ self.freqs_cis = precompute_freqs_cis(dim // n_heads, self.max_context * 2, rope_theta)
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+ self.forward_jit = TinyJit(self.forward) if jit else None
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+
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+ def forward(self, tokens:Tensor, start_pos:Union[Variable,int], temperature:float, top_k:int, top_p:float, alpha_f:float, alpha_p:float):
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+ _bsz, seqlen = tokens.shape
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+ freqs_cis = self.freqs_cis.shrink((None, (start_pos, start_pos+seqlen),None,None,None))
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+
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+ h = self.tok_embeddings(tokens)
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+ mask = Tensor.full((1, 1, seqlen, start_pos+seqlen), float("-inf"), dtype=h.dtype, device=h.device).triu(start_pos+1).realize() if seqlen > 1 else None
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+ for i, layer in enumerate(self.layers):
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+ h = layer(h, start_pos, freqs_cis, mask)
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+ print(f"layer {i}", h.tolist().__str__()[0:100])
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+ logits = self.output(self.norm(h)).float()[:, -1, :]
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+
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+ return sample(logits.flatten(), temperature, top_k, top_p, alpha_f, alpha_p).realize()
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+
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+ 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):
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+ # TODO: better way to handle the first call v.s. the rest?
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+ if tokens.shape[0:2] == (1,1) and self.forward_jit is not None:
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+ return self.forward_jit(tokens, Variable("start_pos", 0, self.max_context).bind(start_pos), temperature, top_k, top_p, alpha_f, alpha_p)
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+ return self.forward(tokens, start_pos, temperature, top_k, top_p, alpha_f, alpha_p)
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+
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+ def reset(self):
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+ for layer in self.layers:
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+ print(f"reset layer: {layer.attention.cache_kv}")
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+ layer.attention.cache_kv = layer.attention.cache_kv.zeros_like()
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+
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+# *** helpers ***
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+
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+def convert_from_huggingface(weights:Dict[str, Tensor], model: Transformer, n_heads: int, n_kv_heads: int):
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+ def permute(v: Tensor, n_heads: int):
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+ return v.reshape(n_heads, 2, v.shape[0] // n_heads // 2, v.shape[1]).transpose(1, 2).reshape(*v.shape[:2])
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+
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+ keymap = {
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+ "model.embed_tokens.weight": "tok_embeddings.weight",
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+ **{f"model.layers.{l}.input_layernorm.weight": f"layers.{l}.attention_norm.weight" for l in range(len(model.layers))},
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+ **{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))},
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+ **{f"model.layers.{l}.post_attention_layernorm.weight": f"layers.{l}.ffn_norm.weight" for l in range(len(model.layers))},
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+ **{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))},
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+ "model.norm.weight": "norm.weight",
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+ "lm_head.weight": "output.weight",
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+ }
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+ sd = {}
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+ for k, v in weights.items():
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+ if ".rotary_emb." in k: continue
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+ v = v.to(Device.DEFAULT)
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+ if "model.layers" in k:
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+ if "q_proj" in k:
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+ v = permute(v, n_heads)
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+ elif "k_proj" in k:
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+ v = permute(v, n_kv_heads)
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+ sd[keymap[k]] = v
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+ return sd
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+
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+def fix_bf16(weights:Dict[Any, Tensor]):
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+ if getenv("SUPPORT_BF16", 1):
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+ # TODO: without casting to float16, 70B llama OOM on tinybox.
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+ return {k:v.cast(dtypes.float16) if v.dtype == dtypes.bfloat16 else v for k,v in weights.items()}
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+ # TODO: check if device supports bf16
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+ return {k:v.llvm_bf16_cast(dtypes.half).to(v.device) if v.dtype == dtypes.bfloat16 else v for k,v in weights.items()}
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Alex Cheema