mirror of
https://github.com/ml-explore/mlx-examples.git
synced 2025-06-24 17:31:18 +08:00
3cf436b529
* testing quantization * conversion + quantization working * one config processor * quantization in mistral / nits in llama * args for quantization * llama / mistral conversion in good shape * phi2 quantized * mixtral * qwen conversion
243 lines
7.3 KiB
Python
243 lines
7.3 KiB
Python
import argparse
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import json
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import math
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from dataclasses import dataclass
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from pathlib import Path
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from typing import Optional
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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
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from transformers import AutoTokenizer
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@dataclass
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class ModelArgs:
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max_sequence_length: int = 2048
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num_vocab: int = 51200
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model_dim: int = 2560
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num_heads: int = 32
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num_layers: int = 32
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rotary_dim: int = 32
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class LayerNorm(nn.LayerNorm):
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def __call__(self, x: mx.array) -> mx.array:
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return super().__call__(x.astype(mx.float32)).astype(x.dtype)
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class RoPEAttention(nn.Module):
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def __init__(self, dims: int, num_heads: int, rotary_dim: int):
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super().__init__()
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self.num_heads = num_heads
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self.rope = nn.RoPE(rotary_dim, traditional=False)
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self.Wqkv = nn.Linear(dims, 3 * dims)
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self.out_proj = nn.Linear(dims, dims)
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def __call__(self, x, mask=None, cache=None):
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qkv = self.Wqkv(x)
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queries, keys, values = mx.split(qkv, 3, axis=-1)
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# Extract some shapes
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num_heads = self.num_heads
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B, L, D = queries.shape
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# Prepare the queries, keys and values for the attention computation
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queries = queries.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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keys = keys.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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values = values.reshape(B, L, num_heads, -1).transpose(0, 2, 1, 3)
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# Add RoPE to the queries and keys and combine them with the cache
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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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queries = queries.astype(mx.float32)
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keys = keys.astype(mx.float32)
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# Finally perform the attention computation
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scale = math.sqrt(1 / queries.shape[-1])
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scores = (queries * scale) @ keys.transpose(0, 1, 3, 2)
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if mask is not None:
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scores = scores + mask
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scores = mx.softmax(scores, axis=-1).astype(values.dtype)
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values_hat = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1)
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return self.out_proj(values_hat), (keys, values)
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class ParallelBlock(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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dims = config.model_dim
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mlp_dims = dims * 4
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self.mixer = RoPEAttention(dims, config.num_heads, config.rotary_dim)
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self.ln = LayerNorm(dims)
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self.fc1 = nn.Linear(dims, mlp_dims)
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self.fc2 = nn.Linear(mlp_dims, dims)
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self.act = nn.GELU(approx="precise")
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def __call__(self, x, mask, cache):
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h = self.ln(x)
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attn_h, cache = self.mixer(h, mask, cache)
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ff_h = self.fc2(self.act(self.fc1(h)))
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return attn_h + ff_h + x, cache
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class TransformerDecoder(nn.Module):
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def __init__(self, config: ModelArgs):
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super().__init__()
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self.h = [ParallelBlock(config) for i in range(config.num_layers)]
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def __call__(self, x, mask, cache):
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if cache is None:
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cache = [None] * len(self.h)
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for e, layer in enumerate(self.h):
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x, cache[e] = layer(x, mask, cache[e])
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return x, cache
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class OutputHead(nn.Module):
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def __init__(self, config: ModelArgs) -> None:
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self.ln = LayerNorm(config.model_dim)
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self.linear = nn.Linear(config.model_dim, config.num_vocab)
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def __call__(self, inputs):
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return self.linear(self.ln(inputs))
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class Phi2(nn.Module):
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def __init__(self, config: ModelArgs):
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self.wte = nn.Embedding(config.num_vocab, config.model_dim)
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self.transformer = TransformerDecoder(config)
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self.lm_head = OutputHead(config)
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def __call__(
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self,
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inputs: mx.array,
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mask: mx.array = None,
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cache: mx.array = None,
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) -> tuple[mx.array, mx.array]:
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x = self.wte(inputs)
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mask = None
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if x.shape[1] > 1:
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mask = nn.MultiHeadAttention.create_additive_causal_mask(x.shape[1])
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mask = mask.astype(x.dtype)
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y, cache = self.transformer(x, mask, cache)
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return self.lm_head(y), cache
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def generate(prompt: mx.array, model: Phi2, temp: Optional[float] = 0.0):
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def sample(logits):
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if temp == 0:
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return mx.argmax(logits, axis=-1)
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else:
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return mx.random.categorical(logits * (1 / temp))
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logits, cache = model(prompt)
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y = sample(logits[:, -1, :])
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yield y
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while True:
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logits, cache = model(y[:, None], cache=cache)
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y = sample(logits.squeeze(1))
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yield y
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def load_model(model_path: str):
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model = Phi2(ModelArgs())
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model_path = Path(model_path)
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with open(model_path / "config.json", "r") as f:
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config = json.loads(f.read())
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config.pop("model_type", None)
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quantization = config.pop("quantization", None)
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weights = mx.load(str(model_path / "weights.npz"))
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weights = tree_unflatten(list(weights.items()))
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if quantization is not None:
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nn.QuantizedLinear.quantize_module(model, **quantization)
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model.update(weights)
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tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
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return model, tokenizer
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(description="Phi-2 inference script")
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parser.add_argument(
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"--model-path",
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type=str,
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default="mlx_model",
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help="The path to the model weights",
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)
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parser.add_argument(
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"--prompt",
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help="The message to be processed by the model",
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default="Write a detailed analogy between mathematics and a lighthouse.",
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)
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parser.add_argument(
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"--max-tokens",
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"-m",
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type=int,
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default=100,
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help="Maximum number of tokens to generate",
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)
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parser.add_argument(
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"--temp",
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help="The sampling temperature.",
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type=float,
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default=0.0,
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)
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parser.add_argument("--seed", type=int, default=0, help="The PRNG seed")
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args = parser.parse_args()
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mx.random.seed(args.seed)
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model, tokenizer = load_model(args.model_path)
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prompt = tokenizer(
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args.prompt,
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return_tensors="np",
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return_attention_mask=False,
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)["input_ids"]
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prompt = mx.array(prompt)
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print("[INFO] Generating with Phi-2...", flush=True)
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print(args.prompt, end="", flush=True)
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tokens = []
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for token, _ in zip(generate(prompt, model, args.temp), range(args.max_tokens)):
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tokens.append(token)
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if (len(tokens) % 10) == 0:
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mx.eval(tokens)
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eos_index = next(
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(i for i, t in enumerate(tokens) if t.item() == tokenizer.eos_token_id),
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None,
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)
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if eos_index is not None:
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tokens = tokens[:eos_index]
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s = tokenizer.decode([t.item() for t in tokens])
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print(s, end="", flush=True)
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tokens = []
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if eos_index is not None:
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break
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mx.eval(tokens)
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s = tokenizer.decode([t.item() for t in tokens])
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print(s, flush=True)
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