Port of phi3small (#794)

* start port of phi3small

* fix phi3

* use block sparsity

* compile activation

* nits in readme / mlx lm version

README.md

@@ -9,10 +9,10 @@ Some more useful examples are listed below.
 
 ### Text Models 
 
+- [MLX LM](llms/README.md) a package for LLM text generation, fine-tuning, and more.
 - [Transformer language model](transformer_lm) training.
-- Large scale text generation with [LLaMA](llms/llama),
+- Minimal examples of large scale text generation with [LLaMA](llms/llama),
-  [Mistral](llms/mistral), [Phi-2](llms/phi2), and more in the [LLMs](llms)
+  [Mistral](llms/mistral), and more in the [LLMs](llms) directory.
-  directory.
 - A mixture-of-experts (MoE) language model with [Mixtral 8x7B](llms/mixtral).
 - Parameter efficient fine-tuning with [LoRA or QLoRA](lora).
 - Text-to-text multi-task Transformers with [T5](t5).
@@ -21,7 +21,7 @@ Some more useful examples are listed below.
 ### Image Models 
 
 - Image classification using [ResNets on CIFAR-10](cifar).
-- Generating images with [Stable Diffusion](stable_diffusion).
+- Generating images with [Stable Diffusion or SDXL](stable_diffusion).
 - Convolutional variational autoencoder [(CVAE) on MNIST](cvae).
 
 ### Audio Models

llms/mlx_lm/LORA.md

@@ -129,7 +129,7 @@ For example, to fuse and upload a model derived from Mistral-7B-v0.1, run:
 ```shell
 mlx_lm.fuse \
     --model mistralai/Mistral-7B-v0.1 \
-    --upload-repo mlx-community/my-4bit-lora-mistral \
+    --upload-repo mlx-community/my-lora-mistral-7b \
     --hf-path mistralai/Mistral-7B-v0.1
 ```
 
@@ -249,7 +249,7 @@ of memory. Here are some tips to reduce memory use should you need to do so:
 For example, for a machine with 32 GB the following should run reasonably fast:
 
 ```
-python lora.py \
+mlx_lm.lora \
     --model mistralai/Mistral-7B-v0.1 \
     --train \
     --batch-size 1 \

llms/mlx_lm/models/phi3small.py

@@ -0,0 +1,318 @@
+from dataclasses import dataclass
+from functools import partial
+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):
+    model_type: str
+    hidden_size: int
+    dense_attention_every_n_layers: int
+    ff_intermediate_size: int
+    gegelu_limit: float
+    num_hidden_layers: int
+    num_attention_heads: int
+    layer_norm_epsilon: float
+    vocab_size: int
+    num_key_value_heads: int = None
+    mup_attn_multiplier: float = 1.0
+    mup_use_scaling: bool = True
+    mup_embedding_multiplier: float = 10.0
+    mup_width_multiplier: float = 8.0
+    rope_embedding_base: float = 1000000
+    rope_position_scale: float = 1.0
+    blocksparse_block_size: int = (64,)
+    blocksparse_num_local_blocks: int = 16
+    blocksparse_vert_stride: int = 8
+
+
+@partial(mx.compile, shapeless=True)
+def gegelu_impl(a_gelu, a_linear, limit):
+    a_gelu = mx.where(
+        mx.isinf(a_gelu),
+        a_gelu,
+        mx.clip(a_gelu, a_min=None, a_max=limit),
+    )
+    a_linear = mx.where(
+        mx.isinf(a_linear),
+        a_linear,
+        mx.clip(a_linear, a_min=-limit, a_max=limit),
+    )
+    out_gelu = a_gelu * mx.sigmoid(1.702 * a_gelu)
+    return out_gelu * (a_linear + 1.0)
+
+
+def gegelu(x, limit):
+    a_gelu, a_linear = x[..., ::2], x[..., 1::2]
+    return gegelu_impl(a_gelu, a_linear, limit)
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx):
+        super().__init__()
+
+        dim = args.hidden_size
+        self.n_heads = n_heads = args.num_attention_heads
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+        self.n_q_per_kv = n_heads // n_kv_heads
+
+        self.head_dim = head_dim = args.hidden_size // n_heads
+
+        self.query_key_value = nn.Linear(
+            dim, (self.n_heads + 2 * self.n_kv_heads) * head_dim
+        )
+        self.dense = nn.Linear(dim, dim)
+
+        if args.mup_use_scaling:
+            norm_factor = head_dim / args.mup_attn_multiplier
+        else:
+            norm_factor = math.sqrt(head_dim)
+        self.scale = 1.0 / norm_factor
+
+        self.rope = nn.RoPE(
+            head_dim,
+            traditional=False,
+            base=args.rope_embedding_base,
+            scale=args.rope_position_scale,
+        )
+
+        if layer_idx % args.dense_attention_every_n_layers == 0:
+            self.block_sparse = True
+            self.blocksparse_block_size = args.blocksparse_block_size
+            if self.blocksparse_block_size not in (32, 64):
+                raise ValueError(
+                    f"Unsupported block size {self.blocksparse_block_size}"
+                )
+            self.blocksparse_num_local_blocks = args.blocksparse_num_local_blocks
+            self.blocksparse_vert_stride = args.blocksparse_vert_stride
+        else:
+            self.block_sparse = False
+
+    def _block_sparse_mask(self, q_len, kv_len):
+        vert_stride = self.blocksparse_vert_stride
+        local_blocks = self.blocksparse_num_local_blocks
+        block_size = self.blocksparse_block_size
+        n_heads = self.n_heads
+
+        kv_blocks = (kv_len + block_size - 1) // block_size
+        q_blocks = (q_len + block_size - 1) // block_size
+        q_pos = mx.arange(kv_blocks - q_blocks, kv_blocks)[None, :, None]
+        k_pos = mx.arange(kv_blocks)[None, None]
+
+        mask_vert_strided = (
+            mx.arange(kv_blocks)[None, :] + mx.arange(1, n_heads + 1)[:, None]
+        ) % vert_stride
+        mask_vert_strided = (mask_vert_strided == 0)[:, None, :]
+
+        block_mask = (q_pos >= k_pos) & (
+            (q_pos - k_pos < local_blocks) | mask_vert_strided
+        )
+        block_mask = block_mask.reshape(
+            self.n_kv_heads, self.n_q_per_kv, *block_mask.shape[-2:]
+        )
+        dense_mask = mx.repeat(
+            mx.repeat(block_mask, block_size, axis=-1), block_size, axis=-2
+        )
+        return block_mask, dense_mask[..., -q_len:, :kv_len]
+
+    def _block_sparse_attention(self, queries, keys, values, scale, mask):
+        queries = scale * queries
+        B = queries.shape[0]
+        L = queries.shape[2]
+        queries = mx.reshape(queries, (B, self.n_kv_heads, self.n_q_per_kv, L, -1))
+        keys = mx.expand_dims(keys, 2)
+        values = mx.expand_dims(values, 2)
+
+        # TODO get rid of dense mask if we have a fill value
+        block_mask, dense_mask = self._block_sparse_mask(L, keys.shape[-2])
+        scores = queries @ mx.swapaxes(keys, -1, -2)
+        # TODO, uncomment when faster
+        # scores = mx.block_masked_mm(
+        #   queries,
+        #   mx.swapaxes(keys, -1, -2),
+        #   mask_out=block_mask,
+        #   block_size=self.blocksparse_block_size,
+        # )
+
+        if mask is not None:
+            scores = scores + mask
+        scores = scores + mx.where(
+            dense_mask, mx.array(0, scores.dtype), mx.array(-float("inf"), scores.dtype)
+        )
+        scores = mx.softmax(scores, axis=-1, precise=True)
+
+        output = scores @ values
+        # TODO, uncomment when faster
+        # output = mx.block_masked_mm(
+        #    scores, values, mask_lhs=block_mask, block_size=self.blocksparse_block_size
+        # )
+        return mx.reshape(output, (B, self.n_heads, L, -1))
+
+    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
+
+        qkv = self.query_key_value(x)
+        qkv = qkv.reshape(B, L, -1, self.n_q_per_kv + 2, self.head_dim)
+        queries = qkv[..., :-2, :].flatten(-3, -2)
+        keys = qkv[..., -2, :]
+        values = qkv[..., -1, :]
+
+        # Prepare the queries, keys and values for the attention computation
+        queries = queries.transpose(0, 2, 1, 3)
+        keys = keys.transpose(0, 2, 1, 3)
+        values = values.transpose(0, 2, 1, 3)
+
+        if cache is not None:
+            queries = self.rope(queries, offset=cache.offset)
+            keys = self.rope(keys, offset=cache.offset)
+            keys, values = cache.update_and_fetch(keys, values)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+
+        if self.block_sparse:
+            output = self._block_sparse_attention(
+                queries, keys, values, scale=self.scale, mask=mask
+            )
+        else:
+            output = mx.fast.scaled_dot_product_attention(
+                queries, keys, values, scale=self.scale, mask=mask
+            )
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.dense(output)
+
+
+class MLP(nn.Module):
+    def __init__(self, args):
+        super().__init__()
+        dim = args.hidden_size
+        hidden_dim = args.ff_intermediate_size
+        self.gegelu_limit = args.gegelu_limit
+        self.up_proj = nn.Linear(dim, 2 * hidden_dim)
+        self.down_proj = nn.Linear(hidden_dim, dim)
+
+    def __call__(self, x) -> mx.array:
+        x = self.up_proj(x)
+        return self.down_proj(gegelu(x, self.gegelu_limit))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs, layer_idx):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.self_attn = Attention(args, layer_idx)
+        self.mlp = MLP(args)
+        self.input_layernorm = nn.LayerNorm(
+            args.hidden_size, eps=args.layer_norm_epsilon
+        )
+        self.post_attention_layernorm = nn.LayerNorm(
+            args.hidden_size,
+            eps=args.layer_norm_epsilon,
+        )
+        self.args = args
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
+    ) -> mx.array:
+        r = self.self_attn(self.input_layernorm(x), mask, cache)
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        return out
+
+
+class Phi3Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.vocab_size = args.vocab_size
+        self.num_hidden_layers = args.num_hidden_layers
+        assert self.vocab_size > 0
+        self.mup_embedding_multiplier = args.mup_embedding_multiplier
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            TransformerBlock(args=args, layer_idx=l)
+            for l in range(args.num_hidden_layers)
+        ]
+        self.final_layernorm = nn.LayerNorm(
+            args.hidden_size, eps=args.layer_norm_epsilon
+        )
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        h = self.embed_tokens(inputs)
+        if self.mup_embedding_multiplier:
+            h = self.mup_embedding_multiplier * h
+
+        mask = None
+        if h.shape[1] > 1:
+            mask = nn.MultiHeadAttention.create_additive_causal_mask(h.shape[1])
+            mask = mask.astype(h.dtype)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        for layer, c in zip(self.layers, cache):
+            h = layer(h, mask, c)
+
+        return self.final_layernorm(h)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.model_type = args.model_type
+        self.model = Phi3Model(args)
+        self.args = args
+        self.mup_width_multiplier = args.mup_width_multiplier
+        self._dummy_tokenizer_ids = mx.array(
+            [100256, 100258, 100259, 100260, 100264, 100265]
+            + list(range(100267, 100352))
+        )
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+    ):
+        out = self.model(inputs, cache)
+        out = self.model.embed_tokens.as_linear(out)
+        if self.mup_width_multiplier:
+            out = out / self.mup_width_multiplier
+        out[self._dummy_tokenizer_ids] = -float("inf")
+        return out
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    @property
+    def head_dim(self):
+        return self.args.hidden_size // self.args.num_attention_heads
+
+    def sanitize(self, weights):
+        # Remove unused precomputed rotary freqs
+        return {
+            k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
+        }
+
+    @property
+    def n_kv_heads(self):
+        return self.args.num_key_value_heads

llms/mlx_lm/requirements.txt

@@ -1,4 +1,4 @@
-mlx>=0.14
+mlx>=0.14.1
 numpy
 transformers>=4.39.3
 protobuf

llms/mlx_lm/version.py

@@ -1,3 +1,3 @@
 # Copyright © 2023-2024 Apple Inc.
 
-__version__ = "0.14.0"
+__version__ = "0.14.1"