From 195bec2fa3fbde91ceedbf79a7aaa6c2d23148b3 Mon Sep 17 00:00:00 2001 From: Anchen Date: Mon, 15 Jan 2024 07:18:14 -0800 Subject: [PATCH] feat(mlx_lm): add mixtral support in mlx_lm (#318) * feat: add mixtral support in mlx_lm * chore: update doc --- llms/README.md | 4 +- llms/mlx_lm/convert.py | 11 +- llms/mlx_lm/models/mixtral.py | 247 ++++++++++++++++++++++++++++++++++ llms/mlx_lm/utils.py | 13 +- 4 files changed, 266 insertions(+), 9 deletions(-) create mode 100644 llms/mlx_lm/models/mixtral.py diff --git a/llms/README.md b/llms/README.md index fd33e98a..40dbc067 100644 --- a/llms/README.md +++ b/llms/README.md @@ -101,10 +101,12 @@ Here are a few examples of Hugging Face models that work with this example: - [deepseek-ai/deepseek-coder-6.7b-instruct](https://huggingface.co/deepseek-ai/deepseek-coder-6.7b-instruct) - [01-ai/Yi-6B-Chat](https://huggingface.co/01-ai/Yi-6B-Chat) - [microsoft/phi-2](https://huggingface.co/microsoft/phi-2) +- [mistralai/Mixtral-8x7B-Instruct-v0.1](https://huggingface.co/mistralai/Mixtral-8x7B-Instruct-v0.1) Most [Mistral](https://huggingface.co/models?library=transformers,safetensors&other=mistral&sort=trending), [Llama](https://huggingface.co/models?library=transformers,safetensors&other=llama&sort=trending), -and [Phi-2](https://huggingface.co/models?library=transformers,safetensors&other=phi&sort=trending) +and +[Mixtral](https://huggingface.co/models?library=transformers,safetensors&other=mixtral&sort=trending) style models should work out of the box. diff --git a/llms/mlx_lm/convert.py b/llms/mlx_lm/convert.py index 17c17848..91a0aa42 100644 --- a/llms/mlx_lm/convert.py +++ b/llms/mlx_lm/convert.py @@ -10,7 +10,7 @@ import mlx.nn as nn import transformers from mlx.utils import tree_flatten -from .utils import get_model_path, load +from .utils import get_model_path, linear_class_predicate, load MAX_FILE_SIZE_GB = 15 @@ -94,11 +94,10 @@ def quantize_model( model, _ = load(hf_path) model.load_weights(list(weights.items())) - nn.QuantizedLinear.quantize_module(model, q_group_size, q_bits) - quantized_config["quantization"] = { - "group_size": q_group_size, - "bits": q_bits, - } + nn.QuantizedLinear.quantize_module( + model, q_group_size, q_bits, linear_class_predicate=linear_class_predicate + ) + quantized_config["quantization"] = {"group_size": q_group_size, "bits": q_bits} quantized_weights = dict(tree_flatten(model.parameters())) return quantized_weights, quantized_config diff --git a/llms/mlx_lm/models/mixtral.py b/llms/mlx_lm/models/mixtral.py new file mode 100644 index 00000000..d35900cf --- /dev/null +++ b/llms/mlx_lm/models/mixtral.py @@ -0,0 +1,247 @@ +from dataclasses import dataclass +from typing import Dict, Optional, Tuple, Union + +import mlx.core as mx +import mlx.nn as nn + +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.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) + + y = [] + for xt, st, it in zip(x, scores, inds.tolist()): + yt = mx.concatenate([self.experts[e](xt)[:, None] for e in it], axis=-1) + yt = (yt * st).sum(axis=-1) + y.append(yt[None, :]) + y = mx.concatenate(y) + + 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[:, T - 1 : T, :]), 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/llms/mlx_lm/utils.py b/llms/mlx_lm/utils.py index aa464a6e..402f0a9f 100644 --- a/llms/mlx_lm/utils.py +++ b/llms/mlx_lm/utils.py @@ -10,16 +10,21 @@ from huggingface_hub import snapshot_download from transformers import AutoTokenizer, PreTrainedTokenizer # Local imports -from .models import llama, phi2 +from .models import llama, mixtral, phi2 from .models.base import BaseModelArgs # Constants MODEL_MAPPING = { "llama": llama, "mistral": llama, # mistral is compatible with llama + "mixtral": mixtral, "phi": phi2, } +linear_class_predicate = ( + lambda m: isinstance(m, nn.Linear) and m.weight.shape[0] % 32 == 0 +) # TODO remove this once we support quantization for non-multiples of 32 + def _get_classes(config: dict): """ @@ -171,7 +176,11 @@ def load(path_or_hf_repo: str) -> Tuple[nn.Module, PreTrainedTokenizer]: 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=linear_class_predicate, + ) model.load_weights(list(weights.items()))