mirror of
https://github.com/ml-explore/mlx-examples.git
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195 lines
8.1 KiB
Python
195 lines
8.1 KiB
Python
from transformers import LlamaConfig, AutoModelForCausalLM
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import mlx.core as mx
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import mlx.nn as nn
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from mlx.utils import tree_unflatten, tree_map
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import mlx.core as mx
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import mlx.nn as nn
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from typing import Optional, Tuple
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class RMSNorm(nn.Module):
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def __init__(self, dims: int, eps: float = 1e-5):
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super().__init__()
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self.weight = mx.ones((dims,))
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self.eps = eps
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def _norm(self, x):
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return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps)
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def __call__(self, x):
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output = self._norm(x.astype(mx.float32)).astype(x.dtype)
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return self.weight * output
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class Attention(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.config = config
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self.n_heads: int = config.num_attention_heads
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self.n_kv_heads: int = config.num_key_value_heads
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self.repeats = self.n_heads // self.n_kv_heads
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# print("heads", self.n_heads, "kv heads", self.n_kv_heads, "repeats", self.repeats)
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self.head_dim = config.hidden_size // self.n_heads
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self.scale = self.head_dim ** -0.5
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self.q_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
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self.k_proj = nn.Linear(config.hidden_size, config.hidden_size // self.repeats, bias=False)
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self.v_proj = nn.Linear(config.hidden_size, config.hidden_size // self.repeats, bias=False)
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self.o_proj = nn.Linear(config.hidden_size, config.hidden_size, bias=False)
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self.rope = nn.RoPE(self.head_dim, traditional=False)
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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B, L, D = x.shape
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queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x)
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# Prepare the queries, keys and values for the attention computation
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queries = queries.reshape(B, L, self.n_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3) # B, n_kv_heads, L, head_dim
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def repeat(a):
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a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2)
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kv_size = a.shape[-1]
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# can't use the L from x here, this is like cross-attention during decoding
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return a.reshape([B, self.n_heads, -1, kv_size])
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# cache should be with unrepeated kv, otherwise GQA is pointless lol
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# keys, values = map(repeat, (keys, values))
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if cache is not None:
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key_cache, value_cache = cache
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queries = self.rope(queries, offset=key_cache.shape[2])
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keys = self.rope(keys, offset=key_cache.shape[2])
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keys = mx.concatenate([key_cache, keys], axis=2)
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values = mx.concatenate([value_cache, values], axis=2)
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else:
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queries = self.rope(queries)
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keys = self.rope(keys)
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# print("queries shape", queries.shape, "keys shape", keys.shape, "values shape", values.shape)
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scores = (queries * self.scale) @ repeat(keys).transpose(0, 1, 3, 2)
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if mask is not None:
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# print("we need to add mask of shape", mask.shape, "to scores of shape", scores.shape)
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if cache is None:
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scores += mask
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else:
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# we're doing "cross-attn"; add mask to the "end" of the attn matrix along the K dimension
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a, b = mx.split(scores, indices_or_sections=[-mask.shape[-1]], axis=-1)
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scores = mx.concatenate([a, b + mask], axis=-1)
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scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype)
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output = (scores @ repeat(values)).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.o_proj(output), (keys, values)
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class FeedForward(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.gate_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
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self.down_proj = nn.Linear(config.intermediate_size, config.hidden_size, bias=False)
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self.up_proj = nn.Linear(config.hidden_size, config.intermediate_size, bias=False)
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def __call__(self, x) -> mx.array:
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return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
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class TransformerBlock(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.n_heads = config.num_attention_heads
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self.dim = config.hidden_size
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self.self_attn = Attention(config=config)
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self.mlp = FeedForward(config=config)
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self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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def __call__(
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self,
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x: mx.array,
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mask: Optional[mx.array] = None,
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cache: Optional[Tuple[mx.array, mx.array]] = None,
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) -> mx.array:
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r, cache = self.self_attn(self.input_layernorm(x), mask, cache)
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h = x + r
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r = self.mlp(self.post_attention_layernorm(h))
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out = h + r
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return out, cache
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class Llama(nn.Module):
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def __init__(self, config: LlamaConfig):
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super().__init__()
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self.config = config
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self.vocab_size = config.vocab_size
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self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)
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self.layers = [TransformerBlock(config=config) for _ in range(config.num_hidden_layers)]
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self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)
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self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
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self.kv_cache = []
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def truncate_kv_cache(self, num_to_truncate):
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cache_length = self.kv_cache[0][0].shape[2]
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num_to_truncate = min(num_to_truncate, cache_length)
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if num_to_truncate == 0:
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return False
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else:
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self.kv_cache = tree_map(lambda x: x[:, :, :-num_to_truncate, :], self.kv_cache)
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return True
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def __call__(
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self,
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x: mx.array,
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read_cache: bool = False,
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write_cache: bool = False,
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next_token_only: bool = False
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):
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mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
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mask = mask.astype(self.embed_tokens.weight.dtype)
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if read_cache and len(self.kv_cache) != len(self.layers):
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raise RuntimeError(f"Length of cache ({len(self.kv_cache)}) must match number of layers ({len(self.layers)})")
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x = self.embed_tokens(x)
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for idx, layer in enumerate(self.layers):
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x, c = layer(x, mask, cache=self.kv_cache[idx] if read_cache else None)
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if write_cache:
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if len(self.kv_cache) == 0:
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self.kv_cache = [None] * len(self.layers)
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self.kv_cache[idx] = c
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x = self.norm(x)
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if next_token_only:
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x = x[:, -1]
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return self.lm_head(x)
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@classmethod
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def from_hugging_face(cls, model_path: str):
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config = LlamaConfig.from_pretrained(model_path)
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torch_weights = AutoModelForCausalLM.from_pretrained(model_path).state_dict()
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mx_weights = {k.replace("model.", ""):mx.array(v.numpy()) for k, v in torch_weights.items()}
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for k in mx_weights.keys():
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mx_weights[k] = mx_weights[k].astype(mx.float16)
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mlx_model = cls(config)
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mlx_model.update(tree_unflatten(list(mx_weights.items())))
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return mlx_model
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def generate(
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self,
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x: mx.array,
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temp=1.0,
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read_cache: bool = False
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):
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# Make an additive causal mask. We will need that to process the prompt.
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mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
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mask = mask.astype(self.embed_tokens.weight.dtype)
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logit = self(x, read_cache=read_cache, write_cache=True, next_token_only=True)
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tok = mx.random.categorical(logit * (1 / temp))
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yield tok
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while True:
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x = tok.reshape(-1, 1)
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logit = self(x, read_cache=True, write_cache=True, next_token_only=True)
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tok = mx.random.categorical(logit * (1 / temp))
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yield tok |