Made llama and mistral files mypy compatible (#1359)

* Made mypy compatible

* reformatted

* Added more fixes

* Added fixes to speculative-decoding

* Fixes

* fix circle

* revert some stuff

---------

Co-authored-by: Awni Hannun <awni@apple.com>

.circleci/config.yml

@@ -36,7 +36,5 @@ workflows:
           type: approval
       - apple/authenticate:
           context: pr-approval
-      - mlx_lm_build_and_test:
-          requires: [ hold ]
       - linux_build_and_test:
           requires: [ hold ]

llms/gguf_llm/generate.py

@@ -40,7 +40,7 @@ def generate(
     if len(tokens) == 0:
         print("No tokens generated for this prompt")
         return
-    prompt_tps = prompt.size / prompt_time
+    prompt_tps = len(prompt) / prompt_time
     gen_tps = (len(tokens) - 1) / gen_time
     print(f"Prompt: {prompt_tps:.3f} tokens-per-sec")
     print(f"Generation: {gen_tps:.3f} tokens-per-sec")

llms/gguf_llm/models.py

@@ -19,10 +19,10 @@ class ModelArgs:
     rms_norm_eps: float
     vocab_size: int
     context_length: int
-    num_key_value_heads: int = None
+    num_key_value_heads: Optional[int] = None
     rope_theta: float = 10000
     rope_traditional: bool = False
-    model_type: str = None
+    model_type: Optional[str] = None
     rope_scaling: Optional[Dict[str, Union[float, str]]] = None
 
     def __post_init__(self):
@@ -54,7 +54,7 @@ class Attention(nn.Module):
 
         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_kv_heads = n_kv_heads = args.num_key_value_heads or n_heads
 
         self.repeats = n_heads // n_kv_heads
 
@@ -66,7 +66,7 @@ class Attention(nn.Module):
         self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
         self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
         rope_scale = (
-            1 / args.rope_scaling["factor"]
+            1 / float(args.rope_scaling["factor"])
             if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
             else 1
         )
@@ -254,7 +254,7 @@ def translate_weight_names(name):
     return name
 
 
-def load(gguf_file: str, repo: str = None):
+def load(gguf_file: str, repo: Optional[str] = None):
     # If the gguf_file exists, try to load model from it.
     # Otherwise try to download and cache from the HF repo
     if not Path(gguf_file).exists():

llms/llama/convert.py

@@ -7,6 +7,7 @@ import glob
 import json
 import shutil
 from pathlib import Path
+from typing import Dict
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -149,7 +150,8 @@ def quantize(weights, config, args):
 def make_shards(weights: dict, max_file_size_gibibyte: int = 15):
     max_file_size_bytes = max_file_size_gibibyte << 30
     shards = []
-    shard, shard_size = {}, 0
+    shard: Dict[str, mx.array] = {}
+    shard_size = 0
     for k, v in weights.items():
         if shard_size + v.nbytes > max_file_size_bytes:
             shards.append(shard)

llms/mixtral/mixtral.py

@@ -23,7 +23,7 @@ class ModelArgs:
     n_kv_heads: int
     norm_eps: float
     vocab_size: int
-    moe: dict = None
+    moe: dict
 
 
 class Attention(nn.Module):
@@ -91,7 +91,6 @@ class FeedForward(nn.Module):
 class MOEFeedForward(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
-
         self.num_experts = args.moe["num_experts"]
         self.num_experts_per_tok = args.moe["num_experts_per_tok"]
         self.experts = [FeedForward(args) for _ in range(self.num_experts)]
@@ -115,7 +114,6 @@ class MOEFeedForward(nn.Module):
             yt = (yt * st).sum(axis=-1)
             y.append(yt[None, :])
         y = mx.concatenate(y)
-
         return y.reshape(orig_shape)
 
 

llms/speculative_decoding/decoder.py

@@ -160,12 +160,12 @@ class SpeculativeDecoder:
             )
 
             n_accepted += num_to_accept
-            n_draft += draft_tokens.size
+            n_draft += len(draft_tokens)
 
             # Rewind the cache for unaccepted tokens:
-            if (n := draft_tokens.size) > num_to_accept:
+            if (n := len(draft_tokens)) > num_to_accept:
-                self.draft_model.truncate_cache(n - new_tokens.size)
+                self.draft_model.truncate_cache(n - len(new_tokens))
-                self.model.truncate_cache(n - new_tokens.size + 1)
+                self.model.truncate_cache(n - len(new_tokens) + 1)
 
             n_steps += 1
 
@@ -181,7 +181,7 @@ class SpeculativeDecoder:
 
             if ntoks >= max_tokens or new_tokens[-1] == self.tokenizer.eos_id:
                 break
-            draft_inputs = new_tokens[max(new_tokens.size - 2, 0) :]
+            draft_inputs = new_tokens[max(len(new_tokens) - 2, 0) :]
             inputs = draft_inputs[-1:]
 
         print(self.tokenizer.decode(outputs)[skip:], end="", flush=True)

llms/speculative_decoding/model.py

@@ -213,10 +213,10 @@ class TransformerDecoderLayer(nn.Module):
         memory: mx.array,
         mask: mx.array,
         memory_mask: mx.array,
-        cache: Optional[List[Tuple[mx.array, mx.array]]] = None,
+        cache: Optional[Tuple[mx.array, mx.array]] = None,
-    ):
+    ) -> Tuple[mx.array, Tuple[mx.array, mx.array]]:
         y = self.ln1(x)
-        y, cache = self.self_attention(y, y, y, mask, cache)
+        y, new_cache = self.self_attention(y, y, y, mask, cache)
         x = x + y
 
         y = self.ln2(x)
@@ -227,7 +227,7 @@ class TransformerDecoderLayer(nn.Module):
         y = self.dense(y)
         x = x + y
 
-        return x, cache
+        return x, new_cache
 
 
 def create_additive_causal_mask(N: int, offset: int = 0):

lora/lora.py

@@ -3,6 +3,7 @@
 import argparse
 import json
 import math
+import os
 import sys
 import time
 from pathlib import Path
@@ -16,7 +17,7 @@ from mlx.utils import tree_flatten
 from models import LoRALinear
 
 # Disable output buffering to see print statements in real-time
-sys.stdout.reconfigure(line_buffering=True)
+sys.stdout = os.fdopen(sys.stdout.fileno(), "w", buffering=1)
 
 
 def build_parser():

lora/models.py

@@ -17,10 +17,10 @@ class ModelArgs:
     num_attention_heads: int
     rms_norm_eps: float
     vocab_size: int
-    num_key_value_heads: int = None
+    num_key_value_heads: Optional[int] = None
     rope_theta: float = 10000
     rope_traditional: bool = False
-    model_type: str = None
+    model_type: Optional[str] = None
     rope_scaling: Optional[Dict[str, Union[float, str]]] = None
 
     def __post_init__(self):
@@ -146,7 +146,7 @@ class Attention(nn.Module):
         self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
         self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=False)
         rope_scale = (
-            1 / args.rope_scaling["factor"]
+            1 / float(args.rope_scaling["factor"])
             if args.rope_scaling is not None and args.rope_scaling["type"] == "linear"
             else 1
         )

lora/utils.py

@@ -4,7 +4,7 @@ import glob
 import json
 import logging
 from pathlib import Path
-from typing import Generator
+from typing import Any, Dict, Generator, Union
 
 import mlx.core as mx
 import mlx.nn as nn
@@ -72,7 +72,8 @@ python generate.py --model {repo_id} --prompt "My name is"
 def make_shards(weights: dict, max_file_size_gibibyte: int = 15):
     max_file_size_bytes = max_file_size_gibibyte << 30
     shards = []
-    shard, shard_size = {}, 0
+    shard: Dict[str, mx.array] = {}
+    shard_size = 0
     for k, v in weights.items():
         if shard_size + v.nbytes > max_file_size_bytes:
             shards.append(shard)
@@ -83,7 +84,7 @@ def make_shards(weights: dict, max_file_size_gibibyte: int = 15):
     return shards
 
 
-def save_model(save_dir: str, weights, tokenizer, config):
+def save_model(save_dir: Union[str, Path], weights, tokenizer, config):
     save_dir = Path(save_dir)
     save_dir.mkdir(parents=True, exist_ok=True)
 
@@ -96,7 +97,10 @@ def save_model(save_dir: str, weights, tokenizer, config):
     )
 
     total_size = sum(v.nbytes for v in weights.values())
-    index_data = {"metadata": {"total_size": total_size}, "weight_map": {}}
+    index_data: Dict[str, Any] = {
+        "metadata": {"total_size": total_size},
+        "weight_map": {},
+    }
 
     for i, shard in enumerate(shards):
         shard_name = shard_file_format.format(i + 1, shards_count)