comments

llms/mlx_lm/evaluate.py

@@ -10,7 +10,7 @@ import logging
 import os
 from importlib.metadata import version
 from pathlib import Path
-from typing import Optional, Union
+from typing import Optional
 
 import lm_eval
 import mlx.core as mx
@@ -20,11 +20,10 @@ from lm_eval.api.model import LM
 from lm_eval.api.registry import register_model
 from tqdm import tqdm
 
+from .models.base import create_causal_mask
 from .models.cache import make_prompt_cache
 from .utils import load, stream_generate
 
-PAD = 0
-
 
 def _len_longest_common_prefix(a, b):
     l = 0
@@ -43,31 +42,14 @@ def _rstrip_until(s, untils):
     return s[: min(f)]
 
 
-def _pad_inputs(
-    inputs,
-    maxlen,
-    genlen=0,
-    pad_left=False,
-    pad_multiple=32,
-    truncate=False,
-):
-    # pad the prompts to the left with at least genlen tokens.
-    actual_maxlen = max(len(p) for p in inputs) + genlen
-    if actual_maxlen > maxlen:
-        if not truncate:
-            raise ValueError("Inputs are too long.")
-        else:  # drop begining
-            actual_maxlen = maxlen
-            inputs = [p[max(0, len(p) - maxlen) :] for p in inputs]
-    if pad_multiple > 0:
-        maxlen = (actual_maxlen + pad_multiple - 1) // pad_multiple
-        maxlen *= pad_multiple
-    assert PAD == 0
-    lr = np.array((1, 0) if pad_left else (0, 1))
-    return np.stack(
-        [np.pad(np.array(x, np.int32), lr * (maxlen - len(x))) for x in inputs],
+def _pad_inputs(inputs):
+    lengths = np.array([len(x) for x in inputs])
+    maxlen = lengths.max()
+    padded = np.stack(
+        [np.pad(x, (0, maxlen - len(x))) for x in inputs],
         axis=0,
     )
+    return mx.array(padded), mx.array(lengths)
 
 
 @register_model("mlxlm")
@@ -83,32 +65,33 @@ class MLXLM(LM):
         self._batch_size = batch_size
         self._model, self.tokenizer = load(path_or_hf_repo)
         self._max_tokens = max_tokens or self.tokenizer.model_max_length
-        self.use_chat_template = use_chat_template or (
+        self.use_chat_template = use_chat_template and (
             self.tokenizer.chat_template is not None
         )
 
-    def _score_fn(self, inputs, tokenize=True, step_size=32):
-        if tokenize:
-            inputs = self._tokenize(inputs)
-        inputs = _pad_inputs(inputs, self._max_tokens, truncate=False)
-        inputs = mx.array(inputs)
+    def _score_fn(self, inputs, step_size: int = 64):
+        inputs, lengths = _pad_inputs(inputs)
         inputs, targets = inputs[..., :-1], inputs[..., 1:]
 
         cache = make_prompt_cache(self._model)
 
-        mask = targets != PAD
-
         scores, is_greedy = [], []
         for i in range(0, inputs.shape[1], step_size):
-            logits = self._model(inputs[:, i : i + step_size], cache=cache)
+            inp = inputs[:, i : i + step_size]
+            T = inp.shape[1]
 
+            offset = cache[0].offset
+            mask = create_causal_mask(T, offset, lengths=lengths)
+            mask = mask == 0
+
+            logits = self._model(inp, cache=cache, mask=mask)
             log_probs = nn.log_softmax(logits.astype(mx.float32))
+
             score = mx.take_along_axis(
                 log_probs, targets[:, i : i + step_size, mx.newaxis], axis=-1
             )[..., 0]
-            ig = mask[:, i : i + step_size] * (
-                targets[:, i : i + step_size] == mx.argmax(logits, axis=-1)
-            )
+            ig = targets[:, i : i + step_size] == mx.argmax(logits, axis=-1)
+            ig = mx.where(mx.arange(T) + offset < lengths[:, None], ig, False)
 
             mx.eval(score, ig)
             mx.metal.clear_cache()
@@ -119,38 +102,32 @@ class MLXLM(LM):
         scores = mx.concatenate(scores, axis=1)
         is_greedy = mx.concatenate(is_greedy, axis=1)
 
-        return scores, mask.sum(axis=-1), is_greedy
+        return scores, lengths, is_greedy
 
-    def _loglikelihood(self, texts, score_spans=None, tokenize=True):
-        # sort by length to get batches with little padding.
-        sorted_indices = sorted(range(len(texts)), key=lambda i: -len(texts[i]))
-        sorted_inputs = [texts[sorted_indices[i]] for i in range(len(texts))]
-        sorted_spans = None
+    def _loglikelihood(self, texts, score_spans=None):
+        all_scores = mx.zeros(len(texts))
+        all_is_greedy = mx.zeros(len(texts), dtype=mx.bool_)
+        for i in tqdm(range(0, len(texts), self._batch_size)):
+            batch = texts[i : i + self._batch_size]
+            scores, lengths, is_greedy = self._score_fn(batch)
+
+            ind = np.arange(scores.shape[-1])
             if score_spans is not None:
-            sorted_spans = [score_spans[sorted_indices[i]] for i in range(len(texts))]
+                spans = score_spans[i : i + self._batch_size]
+                lengths = [end - start for start, end in spans]
+                masks = mx.array(
+                    np.array([(ind >= start) & (ind < end) for start, end in spans])
+                )
+            else:
+                masks = ind[None] < lengths[:, None]
 
-        results = []
-        for i in tqdm(range(0, len(sorted_inputs), self._batch_size)):
-            batch = sorted_inputs[i : i + self._batch_size]
-            scores, length, is_greedy = self._score_fn(batch, tokenize=tokenize)
-            for j in range(len(batch)):
-                if sorted_spans is None:  # full sequence score
-                    mask = mx.arange(scores[j].shape[-1]) < length
-                    score = (scores[j].astype(mx.float32) * mask).sum(axis=-1)
-                    ig = (is_greedy[j].astype(mx.int32) * mask).sum(axis=-1)
-                else:  # subsequence score
-                    start, end = sorted_spans[i + j]
-                    score = scores[j][start:end].astype(mx.float32).sum()
-                    ig = is_greedy[j][start:end].astype(mx.int32).sum()
-                    length = end - start
+            scores = (masks * scores).sum(axis=-1)
+            is_greedy = (masks * is_greedy).sum(axis=-1)
 
-                results.append((score.item(), ig.item(), length))
+            all_scores[i : i + self._batch_size] = scores
+            all_is_greedy[i : i + self._batch_size] = is_greedy == lengths
 
-        # reorder the outputs
-        inv_sort = np.argsort(sorted_indices)
-        results = [results[inv_sort[i]] for i in range(len(results))]
-
-        return results
+        return all_scores, all_is_greedy
 
     def _tokenize(self, texts):
         return [
@@ -222,16 +199,53 @@ class MLXLM(LM):
                 + "completion longer than context."
             )
 
+        num_results = len(shortened)
+
+        # sort by length to get batches with little padding.
+        sorted_indices = sorted(range(len(shortened)), key=lambda i: -len(shortened[i]))
+        shortened = [shortened[i] for i in sorted_indices]
+        completion_spans = [completion_spans[i] for i in sorted_indices]
+
+        group = mx.distributed.init()
+
+        # split strided so we have approximately the same lengths on each node
+        shortened = shortened[group.rank() :: group.size()]
+        completion_spans = completion_spans[group.rank() :: group.size()]
+
         # model scoring, returns num_requests x (logp, is_greedy, length).
-        results = self._loglikelihood(
+        scores, is_greedy = self._loglikelihood(
             shortened,
             score_spans=completion_spans,
-            tokenize=False,
         )
-        return [(r[0], r[1] == r[2]) for r in results]
+
+        # all gather the results across groups
+        if group.size() > 1:
+            per_group = int(np.ceil(num_results / group.size()))
+            scores = mx.pad(scores, ((0, per_group - len(scores)),))
+            is_greedy = mx.pad(is_greedy, ((0, per_group - len(is_greedy))))
+            scores = mx.distributed.all_gather(scores[mx.newaxis], stream=mx.cpu)
+            is_greedy = mx.distributed.all_gather(is_greedy[mx.newaxis], stream=mx.cpu)
+            scores = scores.T.reshape(-1)
+            is_greedy = is_greedy.T.reshape(-1)
+
+        scores = np.array(scores[:num_results])
+        is_greedy = np.array(is_greedy[:num_results])
+
+        results = [(score, ig) for score, ig in zip(scores, is_greedy)]
+        inv_sort = np.argsort(sorted_indices)
+        results = [results[inv_sort[i]] for i in range(len(inv_sort))]
+        return results
 
     tokenizer_name = lm_eval.models.huggingface.HFLM.tokenizer_name
-    apply_chat_template = lm_eval.models.huggingface.HFLM.apply_chat_template
+
+    def apply_chat_template(
+        self, chat_history: list[dict[str, str]], add_generation_prompt: bool = True
+    ) -> str:
+        if len(chat_history) == 0:
+            return ""
+        return lm_eval.models.huggingface.HFLM.apply_chat_template(
+            chat_history, add_generation_prompt
+        )
 
     def loglikelihood_rolling(self, requests) -> list[float]:
         """Compute full log-likelihood of a string, with no truncation, for perplexity computation
@@ -268,8 +282,9 @@ class MLXLM(LM):
         logging.info(
             "Estimating loglikelihood rolling for %d sequences." % len(requests)
         )
-        inputs = [req.args[0] for req in requests]
-        return [t[0] for t in self._loglikelihood(inputs)]
+        inputs = self._tokenize([req.args[0] for req in requests])
+        scores, _ = self._loglikelihood(inputs)
+        return scores.tolist()
 
     def generate_until(self, requests) -> list[str]:
         """Generate greedily until a stopping sequence
@@ -332,7 +347,7 @@ def main():
     )
     parser.add_argument(
         "--limit",
-        default=1.0,
+        default=None,
         help="Limit the number of examples per task.",
         type=float,
     )
@@ -346,11 +361,8 @@ def main():
     )
     parser.add_argument(
         "--apply-chat-template",
-        action=argparse.BooleanOptionalAction,
-        help="Specifies whether to apply a chat template to the prompt. If "
-        "the model has a chat template, this defaults to `True`, "
-        "otherwise `False`.",
-        default=None,
+        action="store_true",
+        help="Specifies whether to apply a chat template to the prompt.",
     )
     args = parser.parse_args()
 

llms/mlx_lm/examples/pipeline_generate.py

@@ -22,6 +22,11 @@ import mlx.core as mx
 from mlx_lm import load, stream_generate
 
 parser = argparse.ArgumentParser(description="LLM pipelined inference example")
+parser.add_argument(
+    "--model",
+    default="mlx-community/DeepSeek-R1-3bit",
+    help="HF repo or path to local model.",
+)
 parser.add_argument(
     "--prompt",
     "-p",
@@ -37,9 +42,7 @@ parser.add_argument(
 )
 args = parser.parse_args()
 
-model_repo = "mlx-community/DeepSeek-V3-3bit"
-
-model, tokenizer = load(model_repo, lazy=True)
+model, tokenizer = load(args.model, lazy=True)
 
 messages = [{"role": "user", "content": args.prompt}]
 prompt = tokenizer.apply_chat_template(messages, add_generation_prompt=True)

llms/mlx_lm/lora.py

@@ -78,6 +78,7 @@ def build_parser():
         "--train",
         action="store_true",
         help="Do training",
+        default=None,
     )
     parser.add_argument(
         "--data",
@@ -135,6 +136,7 @@ def build_parser():
         "--test",
         action="store_true",
         help="Evaluate on the test set after training",
+        default=None,
     )
     parser.add_argument(
         "--test-batches",
@@ -156,6 +158,7 @@ def build_parser():
         "--grad-checkpoint",
         action="store_true",
         help="Use gradient checkpointing to reduce memory use.",
+        default=None,
     )
     parser.add_argument("--seed", type=int, help="The PRNG seed")
     return parser

llms/mlx_lm/models/deepseek_v3.py

@@ -400,6 +400,8 @@ class DeepseekV3Model(nn.Module):
 
         pipeline_rank = self.pipeline_rank
         pipeline_size = self.pipeline_size
+        # Hack to avoid time-outs during prompt-processing
+        dist_stream = mx.cpu if h.shape[1] > 1 else mx.gpu
         if mask is None:
             mask = create_attention_mask(h, cache)
 
@@ -407,18 +409,21 @@ class DeepseekV3Model(nn.Module):
             cache = [None] * len(self.layers)
 
         # Receive from the previous process in the pipeline
+
         if pipeline_rank < pipeline_size - 1:
-            h = mx.distributed.recv_like(h, (pipeline_rank + 1))
+            h = mx.distributed.recv_like(h, (pipeline_rank + 1), stream=dist_stream)
 
         for layer, c in zip(self.layers, cache):
             h = layer(h, mask, c)
 
         # Send to the next process in the pipeline
         if pipeline_rank != 0:
-            h = mx.distributed.send(h, (pipeline_rank - 1) % pipeline_size)
+            h = mx.distributed.send(
+                h, (pipeline_rank - 1) % pipeline_size, stream=dist_stream
+            )
 
         # Broadcast h while keeping it in the graph
-        h = mx.distributed.all_gather(h)[: h.shape[0]]
+        h = mx.distributed.all_gather(h, stream=dist_stream)[: h.shape[0]]
 
         return self.norm(h)
 

llms/mlx_lm/models/internlm3.py

@@ -0,0 +1,241 @@
+# Copyright © 2023-2024 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Tuple, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+
+
+@dataclass
+class ModelArgs(BaseModelArgs):
+    model_type: str
+    hidden_size: int
+    num_hidden_layers: int
+    intermediate_size: int
+    num_attention_heads: int
+    rms_norm_eps: float
+    vocab_size: int
+    bias: bool = False
+    qkv_bias: bool = False
+    max_position_embeddings: int = 32768
+    num_key_value_heads: int = None
+    rope_theta: float = 10000
+    rope_traditional: bool = False
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    tie_word_embeddings: bool = False
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        if self.rope_scaling:
+            required_keys = {"factor", "rope_type"}
+            if not all(key in self.rope_scaling for key in required_keys):
+                raise ValueError(f"rope_scaling must contain keys {required_keys}")
+
+            if self.rope_scaling["rope_type"] not in ["linear", "dynamic"]:
+                raise ValueError(
+                    "rope_scaling 'rope_type' currently only supports 'linear' or 'dynamic"
+                )
+
+
+class DynamicNTKScalingRoPE(nn.Module):
+    """Implements the rotary positional encoding with Dynamic NTK scaling."""
+
+    def __init__(
+        self,
+        dims: int,
+        max_position_embeddings: int = 2048,
+        traditional: bool = False,
+        base: float = 10000,
+        scale: float = 1.0,
+    ):
+        super().__init__()
+        self.max_position_embeddings = max_position_embeddings
+        self.original_base = base
+        self.dims = dims
+        self.traditional = traditional
+        self.scale = scale
+
+    def extra_repr(self):
+        return f"{self.dims}, traditional={self.traditional}, max_position_embeddings={self.max_position_embeddings}, scaling_factor={self.scaling_factor}"
+
+    def __call__(self, x, offset: int = 0):
+        seq_len = x.shape[1] + offset
+        if seq_len > self.max_position_embeddings:
+            base = self.original_base * (
+                (self.scale * seq_len / self.max_position_embeddings) - (self.scale - 1)
+            ) ** (self.dims / (self.dims - 2))
+        else:
+            base = self.original_base
+
+        return mx.fast.rope(
+            x,
+            self.dims,
+            traditional=self.traditional,
+            base=base,
+            scale=self.scale,
+            offset=offset,
+        )
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+
+        dim = args.hidden_size
+        qkv_bias = args.qkv_bias
+        self.n_heads = n_heads = args.num_attention_heads
+        self.n_kv_heads = n_kv_heads = args.num_key_value_heads
+        self.n_kv_groups = n_heads // args.num_key_value_heads
+
+        self.head_dim = head_dim = args.hidden_size // n_heads
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=qkv_bias)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=qkv_bias)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=qkv_bias)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=qkv_bias)
+
+        rope_scale = (
+            1 / args.rope_scaling["factor"]
+            if args.rope_scaling is not None
+            and args.rope_scaling["rope_type"] == "linear"
+            else 2.0
+        )
+
+        self.rope = DynamicNTKScalingRoPE(
+            head_dim,
+            max_position_embeddings=args.max_position_embeddings,
+            traditional=args.rope_traditional,
+            base=args.rope_theta,
+            scale=rope_scale,
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = 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.n_heads, -1).transpose(0, 2, 1, 3)
+        keys = keys.reshape(B, L, self.n_kv_heads, -1).transpose(0, 2, 1, 3)
+        values = values.reshape(B, L, self.n_kv_heads, -1).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)
+
+        output = scaled_dot_product_attention(
+            queries, keys, values, cache=cache, scale=self.scale, mask=mask
+        )
+
+        output = output.transpose(0, 2, 1, 3).reshape(B, L, -1)
+        return self.o_proj(output)
+
+
+class MLP(nn.Module):
+    def __init__(self, dim, hidden_dim, bias):
+        super().__init__()
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=bias)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=bias)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=bias)
+
+    def __call__(self, x) -> mx.array:
+        return self.down_proj(nn.silu(self.gate_proj(x)) * self.up_proj(x))
+
+
+class TransformerBlock(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.self_attn = Attention(args)
+        self.mlp = MLP(args.hidden_size, args.intermediate_size, args.bias)
+        self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.post_attention_layernorm = nn.RMSNorm(
+            args.hidden_size, eps=args.rms_norm_eps
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = 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 InternLM2Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        assert args.vocab_size > 0
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            TransformerBlock(args=args) for _ in range(args.num_hidden_layers)
+        ]
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: mx.array = None,
+        cache=None,
+    ):
+        h = self.embed_tokens(inputs)
+
+        if mask is None:
+            mask = create_attention_mask(h, cache)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+
+        for layer, c in zip(self.layers, cache):
+            h = layer(h, mask, cache=c)
+
+        return self.norm(h)
+
+
+class Model(nn.Module):
+    def __init__(self, args: ModelArgs):
+        super().__init__()
+        self.args = args
+        self.model_type = args.model_type
+        self.model = InternLM2Model(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: mx.array = None,
+        cache=None,
+    ):
+        out = self.model(inputs, mask, cache)
+        if self.args.tie_word_embeddings:
+            out = self.model.embed_tokens.as_linear(out)
+        else:
+            out = self.lm_head(out)
+        return out
+
+    def sanitize(self, weights):
+        # Remove unused precomputed rotary freqs
+        return {k: v for k, v in weights.items() if "attention.rope.inv_freq" not in k}
+
+    @property
+    def layers(self):
+        return self.model.layers

llms/mlx_lm/tuner/datasets.py

@@ -170,7 +170,7 @@ def load_custom_hf_dataset(args, tokenizer: PreTrainedTokenizer):
         if prompt_feature and completion_feature:
             return CompletionsDataset(ds, tokenizer, prompt_feature, completion_feature)
         elif text_feature:
-            return Dataset(train_ds, tokenizer, text_key=text_feature)
+            return Dataset(ds, tokenizer, text_key=text_feature)
         else:
             raise ValueError(
                 "Specify either a prompt and completion feature or a text "

llms/mlx_lm/tuner/trainer.py

@@ -159,8 +159,8 @@ def evaluate(
         ntokens += toks
         mx.eval(all_losses, ntokens)
 
-    all_losses = mx.distributed.all_sum(all_losses)
-    ntokens = mx.distributed.all_sum(ntokens)
+    all_losses = mx.distributed.all_sum(all_losses, stream=mx.cpu)
+    ntokens = mx.distributed.all_sum(ntokens, stream=mx.cpu)
 
     return (all_losses / ntokens).item()
 
@@ -272,9 +272,9 @@ def train(
         if it % args.steps_per_report == 0 or it == args.iters:
             stop = time.perf_counter()
 
-            train_loss = mx.distributed.all_sum(losses).item()
+            train_loss = mx.distributed.all_sum(losses, stream=mx.cpu).item()
             train_loss /= steps * mx.distributed.init().size()
-            n_tokens = mx.distributed.all_sum(n_tokens).item()
+            n_tokens = mx.distributed.all_sum(n_tokens, stream=mx.cpu).item()
             learning_rate = optimizer.learning_rate.item()
             it_sec = args.steps_per_report / (stop - start)
             tokens_sec = float(n_tokens) / (stop - start)

llms/mlx_lm/tuner/utils.py

@@ -100,6 +100,7 @@ def linear_to_lora_layers(
         "minicpm",
         "deepseek",
         "olmo2",
+        "internlm3",
     ]:
         keys = set(["self_attn.q_proj", "self_attn.v_proj"])
         if model.model_type in ["mixtral", "phimoe"]:

llms/tests/test_finetune.py

@@ -21,7 +21,7 @@ from mlx_lm.tuner.utils import build_schedule
 @contextmanager
 def swapped_with_identity(obj, func):
     old_func = getattr(obj, func)
-    setattr(obj, func, lambda x: x)
+    setattr(obj, func, lambda x, **kwargs: x)
     yield
     setattr(obj, func, old_func)
 

llms/tests/test_models.py

@@ -927,6 +927,23 @@ class TestModels(unittest.TestCase):
             model, args.model_type, args.vocab_size, args.num_hidden_layers
         )
 
+    def test_internlm3(self):
+        from mlx_lm.models import internlm3
+
+        args = internlm3.ModelArgs(
+            model_type="internlm3",
+            hidden_size=1024,
+            num_hidden_layers=4,
+            intermediate_size=2048,
+            num_attention_heads=4,
+            rms_norm_eps=1e-5,
+            vocab_size=10_000,
+        )
+        model = internlm3.Model(args)
+        self.model_test_runner(
+            model, args.model_type, args.vocab_size, args.num_hidden_layers
+        )
+
 
 if __name__ == "__main__":
     unittest.main()