From 1415595409874971b5ad96d55980e7d4aa8a8043 Mon Sep 17 00:00:00 2001 From: Anchen Date: Sat, 20 Jan 2024 06:07:45 -0800 Subject: [PATCH] chore(lora): support mixtral in lora example (#343) --- lora/convert.py | 8 +- lora/fuse.py | 2 + lora/lora.py | 4 +- lora/models.py | 7 +- lora/models/mixtral.py | 253 +++++++++++++++++++++++++++++++++++++++++ lora/utils.py | 9 +- 6 files changed, 279 insertions(+), 4 deletions(-) create mode 100644 lora/models/mixtral.py diff --git a/lora/convert.py b/lora/convert.py index 9b2f6de6..bc85eb5e 100644 --- a/lora/convert.py +++ b/lora/convert.py @@ -20,7 +20,13 @@ def quantize(weights, config, args): model.load_weights(list(weights.items())) # Quantize the model: - nn.QuantizedLinear.quantize_module(model, args.q_group_size, args.q_bits) + nn.QuantizedLinear.quantize_module( + model, + args.q_group_size, + args.q_bits, + linear_class_predicate=lambda m: isinstance(m, nn.Linear) + and m.weight.shape[0] != 8, + ) # Update the config: quantized_config["quantization"] = { diff --git a/lora/fuse.py b/lora/fuse.py index bde543b4..2ea265fb 100644 --- a/lora/fuse.py +++ b/lora/fuse.py @@ -56,6 +56,8 @@ if __name__ == "__main__": for l in model.model.layers[-lora_layers:]: l.self_attn.q_proj = LoRALinear.from_linear(l.self_attn.q_proj) l.self_attn.v_proj = LoRALinear.from_linear(l.self_attn.v_proj) + if hasattr(l, "block_sparse_moe"): + l.block_sparse_moe.gate = LoRALinear.from_linear(l.block_sparse_moe.gate) model.update(tree_unflatten(adapters)) fused_linears = [ diff --git a/lora/lora.py b/lora/lora.py index b522dfdb..9efe8893 100644 --- a/lora/lora.py +++ b/lora/lora.py @@ -315,6 +315,8 @@ if __name__ == "__main__": for l in model.model.layers[len(model.model.layers) - args.lora_layers :]: l.self_attn.q_proj = LoRALinear.from_linear(l.self_attn.q_proj) l.self_attn.v_proj = LoRALinear.from_linear(l.self_attn.v_proj) + if hasattr(l, "block_sparse_moe"): + l.block_sparse_moe.gate = LoRALinear.from_linear(l.block_sparse_moe.gate) p = sum(v.size for _, v in tree_flatten(model.parameters())) / 10**6 print(f"Total parameters {p:.3f}M") @@ -349,7 +351,7 @@ if __name__ == "__main__": if args.test: print("Testing") - + model.eval() test_loss = evaluate( model, test_set, diff --git a/lora/models.py b/lora/models.py index 244d8f5a..293b4f96 100644 --- a/lora/models.py +++ b/lora/models.py @@ -328,7 +328,12 @@ def load(path_or_hf_repo: str): model = Model(model_args) if quantization is not None: - nn.QuantizedLinear.quantize_module(model, **quantization) + nn.QuantizedLinear.quantize_module( + model, + **quantization, + linear_class_predicate=lambda m: isinstance(m, nn.Linear) + and m.weight.shape[0] != 8, + ) model.load_weights(list(weights.items())) diff --git a/lora/models/mixtral.py b/lora/models/mixtral.py new file mode 100644 index 00000000..e70e0d2f --- /dev/null +++ b/lora/models/mixtral.py @@ -0,0 +1,253 @@ +from dataclasses import dataclass +from typing import Dict, Optional, Tuple, Union + +import mlx.core as mx +import mlx.nn as nn +import numpy as np + +from .base import BaseModelArgs + + +@dataclass +class ModelArgs(BaseModelArgs): + vocab_size: int = 32000 + max_position_embeddings: int = 4096 * 32 + hidden_size: int = 4096 + intermediate_size: int = 14336 + num_hidden_layers: int = 32 + num_attention_heads: int = 32 + num_experts_per_tok: int = 2 + num_key_value_heads: int = 8 + num_local_experts: int = 8 + rms_norm_eps: float = 1e-5 + vocab_size: int + rope_theta: float = 1e6 + rope_traditional: bool = False + model_type: str = None + rope_scaling: Optional[Dict[str, Union[float, str]]] = None + + def __post_init__(self): + if self.num_key_value_heads is None: + self.num_key_value_heads = self.num_attention_heads + + +class RMSNorm(nn.Module): + def __init__(self, dims: int, eps: float = 1e-5): + super().__init__() + self.weight = mx.ones((dims,)) + self.eps = eps + + def _norm(self, x): + return x * mx.rsqrt(x.square().mean(-1, keepdims=True) + self.eps) + + def __call__(self, x): + output = self._norm(x.astype(mx.float32)).astype(x.dtype) + return self.weight * output + + +class MixtralAttention(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.hidden_size = args.hidden_size + self.num_heads = args.num_attention_heads + self.head_dim = self.hidden_size // self.num_heads + self.num_key_value_heads = args.num_key_value_heads + self.max_position_embeddings = args.max_position_embeddings + self.rope_theta = args.rope_theta + + self.repeats = self.num_heads // self.num_key_value_heads + + self.scale = self.head_dim**-0.5 + + self.q_proj = nn.Linear( + self.hidden_size, self.num_heads * self.head_dim, bias=False + ) + self.k_proj = nn.Linear( + self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False + ) + self.v_proj = nn.Linear( + self.hidden_size, self.num_key_value_heads * self.head_dim, bias=False + ) + self.o_proj = nn.Linear( + self.num_heads * self.head_dim, self.hidden_size, bias=False + ) + + self.rope = nn.RoPE( + self.head_dim, + traditional=args.rope_traditional, + base=args.rope_theta, + ) + + def __call__( + self, + x: mx.array, + mask: Optional[mx.array] = None, + cache: Optional[Tuple[mx.array, mx.array]] = None, + ) -> mx.array: + B, L, D = x.shape + + queries, keys, values = self.q_proj(x), self.k_proj(x), self.v_proj(x) + + # Prepare the queries, keys and values for the attention computation + queries = queries.reshape(B, L, self.num_heads, -1).transpose(0, 2, 1, 3) + keys = keys.reshape(B, L, self.num_key_value_heads, -1).transpose(0, 2, 1, 3) + values = values.reshape(B, L, self.num_key_value_heads, -1).transpose( + 0, 2, 1, 3 + ) + + def repeat(a): + a = mx.concatenate([mx.expand_dims(a, 2)] * self.repeats, axis=2) + return a.reshape([B, self.num_heads, L, -1]) + + if self.repeats > 1: + keys, values = map(repeat, (keys, values)) + + if cache is not None: + key_cache, value_cache = cache + queries = self.rope(queries, offset=key_cache.shape[2]) + keys = self.rope(keys, offset=key_cache.shape[2]) + keys = mx.concatenate([key_cache, keys], axis=2) + values = mx.concatenate([value_cache, values], axis=2) + else: + queries = self.rope(queries) + keys = self.rope(keys) + + scores = (queries * self.scale) @ keys.transpose(0, 1, 3, 2) + if mask is not None: + scores += mask + scores = mx.softmax(scores.astype(mx.float32), axis=-1).astype(scores.dtype) + output = (scores @ values).transpose(0, 2, 1, 3).reshape(B, L, -1) + return self.o_proj(output), (keys, values) + + +class MixtralBLockSparseTop2MLP(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.ffn_dim = args.intermediate_size + self.hidden_dim = args.hidden_size + + self.w1 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) + self.w2 = nn.Linear(self.ffn_dim, self.hidden_dim, bias=False) + self.w3 = nn.Linear(self.hidden_dim, self.ffn_dim, bias=False) + + self.act_fn = nn.silu + + def __call__(self, x: mx.array) -> mx.array: + current_hidden_states = self.act_fn(self.w1(x)) * self.w3(x) + current_hidden_states = self.w2(current_hidden_states) + return current_hidden_states + + +class MixtralSparseMoeBlock(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.hidden_dim = args.hidden_size + self.ffn_dim = args.intermediate_size + self.num_experts = args.num_local_experts + self.num_experts_per_tok = args.num_experts_per_tok + + # gating + self.gate = nn.Linear(self.hidden_dim, self.num_experts, bias=False) + + self.experts = [ + MixtralBLockSparseTop2MLP(args=args) for _ in range(self.num_experts) + ] + + def __call__(self, x: mx.array) -> mx.array: + ne = self.num_experts_per_tok + orig_shape = x.shape + x = x.reshape(-1, x.shape[-1]) + + gates = self.gate(x) + inds = mx.stop_gradient(mx.argpartition(-gates, kth=ne, axis=-1)[:, :ne]) + + scores = mx.softmax( + mx.take_along_axis(gates, inds, axis=-1).astype(mx.float32), + axis=-1, + ).astype(gates.dtype) + + mx.eval(inds) + inds = np.array(inds) + y = mx.zeros((x.shape[0], ne, x.shape[-1])) + for e, expert in enumerate(self.experts): + idx1, idx2 = map(mx.array, np.where(inds == e)) + if idx1.size == 0: + continue + y[idx1, idx2] = expert(x[idx1]) + + y = (y * scores[:, :, None]).sum(axis=1) + + return y.reshape(orig_shape) + + +class MixtralDecoderLayer(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.hidden_size = args.hidden_size + + self.self_attn = MixtralAttention(args) + + self.block_sparse_moe = MixtralSparseMoeBlock(args) + self.input_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps) + self.post_attention_layernorm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps) + + def __call__( + self, + x: mx.array, + mask: Optional[mx.array] = None, + cache: Optional[Tuple[mx.array, mx.array]] = None, + ) -> mx.array: + r, cache = self.self_attn(self.input_layernorm(x), mask, cache) + h = x + r + r = self.block_sparse_moe(self.post_attention_layernorm(h)) + out = h + r + return out, cache + + +class MixtralModel(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.vocab_size = args.vocab_size + self.num_hidden_layers = args.num_hidden_layers + + self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size) + self.layers = [ + MixtralDecoderLayer(args=args) for _ in range(args.num_hidden_layers) + ] + self.norm = RMSNorm(args.hidden_size, eps=args.rms_norm_eps) + + def __call__( + self, + inputs: mx.array, + cache=None, + ): + h = self.embed_tokens(inputs) + + mask = None + T = h.shape[1] + if T > 1: + mask = nn.MultiHeadAttention.create_additive_causal_mask(T) + mask = mask.astype(h.dtype) + + if cache is None: + cache = [None] * len(self.layers) + + for e, layer in enumerate(self.layers): + h, cache[e] = layer(h, mask, cache[e]) + + return self.norm(h), cache + + +class Model(nn.Module): + def __init__(self, args: ModelArgs): + super().__init__() + self.model = MixtralModel(args) + self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False) + + def __call__( + self, + inputs: mx.array, + cache=None, + ): + out, cache = self.model(inputs, cache) + return self.lm_head(out), cache diff --git a/lora/utils.py b/lora/utils.py index b691227d..80d59399 100644 --- a/lora/utils.py +++ b/lora/utils.py @@ -9,6 +9,7 @@ from typing import Generator import mlx.core as mx import mlx.nn as nn import models.llama as llama +import models.mixtral as mixtral import models.phi2 as phi2 import transformers from huggingface_hub import snapshot_download @@ -18,6 +19,7 @@ MODEL_MAPPING = { "llama": llama, "mistral": llama, # mistral is compatible with llama "phi": phi2, + "mixtral": mixtral, } @@ -150,7 +152,12 @@ def load(path_or_hf_repo: str): model_args = model_args_class.from_dict(config) model = model_class(model_args) if quantization is not None: - nn.QuantizedLinear.quantize_module(model, **quantization) + nn.QuantizedLinear.quantize_module( + model, + **quantization, + linear_class_predicate=lambda m: isinstance(m, nn.Linear) + and m.weight.shape[0] != 8, + ) model.load_weights(list(weights.items()))