Merge branch 'main' into adding-orpo-training

llms/mlx_lm/LORA.md

@@ -78,6 +78,14 @@ You can specify the output location with `--adapter-path`.
 You can resume fine-tuning with an existing adapter with
 `--resume-adapter-file <path_to_adapters.safetensors>`.
 
+#### Prompt Masking
+
+The default training computes a loss for every token in the sample. You can
+ignore the prompt and compute loss for just the completion by passing
+`--mask-prompt`. Note this is only supported for `chat` and `completion`
+datasets. For `chat` datasets the final message in the message list is
+considered the completion. See the [dataset section](#Data) for more details.
+
 ### ORPO Training
 
 Odds Ratio Preference Optimization (ORPO) training fine-tunes models using human preference data. Usage:
@@ -343,11 +351,27 @@ hf_dataset:
 
 - Use `prompt_feature` and `completion_feature` to specify keys for a
   `completions` dataset. Use `text_feature` to specify the key for a `text`
-  dataset. 
+  dataset. Use `chat_feature` to specify the key for a chat dataset.
 
 - To specify the train, valid, or test splits, set the corresponding
   `{train,valid,test}_split` argument. 
 
+You can specify a list of Hugging Face datasets with a list of records each
+with the same structure as above. For example:
+
+```yaml
+hf_dataset:
+  - name: "Open-Orca/OpenOrca"
+    train_split: "train[:90%]"
+    valid_split: "train[-10%:]"
+    prompt_feature: "question"
+    completion_feature: "response"
+  - name: "trl-lib/ultrafeedback_binarized"
+    train_split: "train[:90%]"
+    valid_split: "train[-10%:]"
+    chat_feature: "chosen"
+```
+
 - Arguments specified in `config` will be passed as keyword arguments to
   [`datasets.load_dataset`](https://huggingface.co/docs/datasets/v2.20.0/en/package_reference/loading_methods#datasets.load_dataset).
 

llms/mlx_lm/cache_prompt.py

@@ -152,7 +152,7 @@ def main():
     print("Saving...")
     metadata = {}
     metadata["model"] = args.model
-    metadata["chat_template"] = tokenizer.chat_template
+    metadata["chat_template"] = json.dumps(tokenizer.chat_template)
     metadata["tokenizer_config"] = json.dumps(tokenizer_config)
     save_prompt_cache(args.prompt_cache_file, cache, metadata)
 

llms/mlx_lm/examples/chat.py

@@ -23,7 +23,6 @@ response = generate(
     tokenizer,
     prompt=prompt,
     verbose=True,
-    temp=0.0,
     prompt_cache=prompt_cache,
 )
 

llms/mlx_lm/generate.py

@@ -93,6 +93,12 @@ def setup_arg_parser():
         action="store_true",
         help="Use the default chat template",
     )
+    parser.add_argument(
+        "--chat-template-config",
+        help="Additional config for `apply_chat_template`. Should be a dictionary of"
+        " string keys to values represented as a JSON decodable string.",
+        default=None,
+    )
     parser.add_argument(
         "--verbose",
         type=str2bool,
@@ -149,7 +155,6 @@ def setup_arg_parser():
 def main():
     parser = setup_arg_parser()
     args = parser.parse_args()
-
     mx.random.seed(args.seed)
 
     # Load the prompt cache and metadata if a cache file is provided
@@ -195,11 +200,15 @@ def main():
     for eos_token in args.extra_eos_token:
         tokenizer.add_eos_token(eos_token)
 
+    template_kwargs = {}
+    if args.chat_template_config is not None:
+        template_kwargs = json.loads(args.chat_template_config)
+
     if args.use_default_chat_template:
         if tokenizer.chat_template is None:
             tokenizer.chat_template = tokenizer.default_chat_template
     elif using_cache:
-        tokenizer.chat_template = metadata["chat_template"]
+        tokenizer.chat_template = json.loads(metadata["chat_template"])
 
     prompt = args.prompt.replace("\\n", "\n").replace("\\t", "\t")
     prompt = sys.stdin.read() if prompt == "-" else prompt
@@ -209,8 +218,12 @@ def main():
         else:
             messages = []
         messages.append({"role": "user", "content": prompt})
+
         prompt = tokenizer.apply_chat_template(
-            messages, tokenize=False, add_generation_prompt=True
+            messages,
+            tokenize=False,
+            add_generation_prompt=True,
+            **template_kwargs,
         )
 
         # Treat the prompt as a suffix assuming that the prefix is in the

llms/mlx_lm/lora.py

@@ -101,6 +101,14 @@ def build_parser():
         choices=["lora", "dora", "full"],
         help="Type of fine-tuning to perform: lora, dora, or full.",
     )
+
+    parser.add_argument(
+        "--mask-prompt",
+        action="store_true",
+        help="Mask the prompt in the loss when training",
+        default=False,
+    )
+
     parser.add_argument(
         "--training-mode",
         type=str,

llms/mlx_lm/models/deepseek_v2.py

@@ -282,12 +282,12 @@ class MoEGate(nn.Module):
         if self.topk_method == "group_limited_greedy":
             bsz, seq_len = x.shape[:2]
             scores = scores.reshape(bsz, seq_len, self.n_group, -1)
-            group_scores = scores.max(axis=-1)
+            group_scores = scores.max(axis=-1, keepdims=True)
             k = self.n_group - self.topk_group
-            group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-1)[..., :k]
-            batch_idx = mx.expand_dims(mx.arange(bsz), (1, 2))
-            seq_idx = mx.expand_dims(mx.arange(seq_len), (0, 2))
-            scores[batch_idx, seq_idx, group_idx] = 0.0
+            group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :]
+            scores = mx.put_along_axis(
+                scores, group_idx, mx.array(0.0, scores.dtype), axis=-2
+            )
             scores = scores.reshape(bsz, seq_len, -1)
 
         k = self.top_k

llms/mlx_lm/models/deepseek_v3.py

@@ -271,6 +271,38 @@ class DeepseekV3MLP(nn.Module):
         return down_proj
 
 
+@mx.compile
+def group_expert_select(
+    gates,
+    e_score_correction_bias,
+    top_k,
+    n_group,
+    topk_group,
+    routed_scaling_factor,
+    norm_topk_prob,
+):
+
+    k = top_k
+    scores = mx.sigmoid(gates.astype(mx.float32))
+    scores = scores + e_score_correction_bias
+    scores = mx.unflatten(scores, axis=-1, shape=(n_group, -1))
+    group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1, keepdims=True)
+    k = n_group - topk_group
+    group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-2)[..., :k, :]
+    scores = mx.put_along_axis(scores, group_idx, mx.array(0.0), axis=-2)
+    scores = mx.flatten(scores, -2, -1)
+
+    k = top_k
+    inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
+    scores = mx.take_along_axis(scores, inds, axis=-1)
+    if top_k > 1 and norm_topk_prob:
+        denominator = scores.sum(axis=-1, keepdims=True) + 1e-20
+        scores = scores / denominator
+    scores = scores * routed_scaling_factor
+
+    return inds, scores
+
+
 class MoEGate(nn.Module):
     def __init__(self, config: ModelArgs):
         super().__init__()
@@ -279,38 +311,22 @@ class MoEGate(nn.Module):
         self.norm_topk_prob = config.norm_topk_prob
         self.n_routed_experts = config.n_routed_experts
         self.routed_scaling_factor = config.routed_scaling_factor
-        self.topk_method = config.topk_method
         self.n_group = config.n_group
         self.topk_group = config.topk_group
         self.weight = mx.zeros((self.n_routed_experts, config.hidden_size))
         self.e_score_correction_bias = mx.zeros((self.n_routed_experts,))
+        assert config.topk_method == "noaux_tc", "Unsupported topk method."
 
     def __call__(self, x):
-        gates = x @ self.weight.T
-
-        scores = mx.sigmoid(gates.astype(mx.float32))
-
-        assert self.topk_method == "noaux_tc", "Unsupported topk method."
-        bsz, seq_len = x.shape[:2]
-        scores = scores + self.e_score_correction_bias
-        scores = scores.reshape(bsz, seq_len, self.n_group, -1)
-        group_scores = mx.topk(scores, 2, axis=-1).sum(axis=-1)
-        k = self.n_group - self.topk_group
-        group_idx = mx.argpartition(group_scores, kth=k - 1, axis=-1)[..., :k]
-        batch_idx = mx.expand_dims(mx.arange(bsz), (1, 2))
-        seq_idx = mx.expand_dims(mx.arange(seq_len), (0, 2))
-        scores[batch_idx, seq_idx, group_idx] = 0.0
-        scores = scores.reshape(bsz, seq_len, -1)
-
-        k = self.top_k
-        inds = mx.argpartition(-scores, kth=k - 1, axis=-1)[..., :k]
-        scores = mx.take_along_axis(scores, inds, axis=-1)
-        if self.top_k > 1 and self.norm_topk_prob:
-            denominator = scores.sum(axis=-1, keepdims=True) + 1e-20
-            scores = scores / denominator
-        scores = scores * self.routed_scaling_factor
-
-        return inds, scores
+        return group_expert_select(
+            x @ self.weight.T,
+            self.e_score_correction_bias,
+            self.top_k,
+            self.n_group,
+            self.topk_group,
+            self.routed_scaling_factor,
+            self.norm_topk_prob,
+        )
 
 
 class DeepseekV3MoE(nn.Module):

llms/mlx_lm/models/granite.py

@@ -0,0 +1,195 @@
+# Copyright © 2023-2024 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Dict, Optional, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+from .rope_utils import initialize_rope
+
+
+@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
+    logits_scaling: float
+    attention_multiplier: float
+    embedding_multiplier: float
+    residual_multiplier: float
+    max_position_embeddings: int
+    num_key_value_heads: int
+    attention_bias: bool
+    mlp_bias: bool
+    rope_theta: float
+    rope_scaling: Optional[Dict[str, Union[float, str]]] = None
+    tie_word_embeddings: bool = True
+
+
+class Attention(nn.Module):
+    def __init__(self, args: ModelArgs):
+        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.head_dim = head_dim = args.hidden_size // n_heads
+
+        self.scale = args.attention_multiplier
+        attention_bias = args.attention_bias
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=attention_bias)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
+        self.v_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=attention_bias)
+        self.o_proj = nn.Linear(n_heads * head_dim, dim, bias=attention_bias)
+
+        self.rope = initialize_rope(
+            self.head_dim,
+            args.rope_theta,
+            False,
+            args.rope_scaling,
+            args.max_position_embeddings,
+        )
+
+    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, args: ModelArgs):
+        super().__init__()
+
+        dim = args.hidden_size
+        hidden_dim = args.intermediate_size
+        if hasattr(args, "mlp_bias"):
+            mlp_bias = args.mlp_bias
+        else:
+            mlp_bias = False
+
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=mlp_bias)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=mlp_bias)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=mlp_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.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.self_attn = Attention(args)
+        self.mlp = MLP(args)
+        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
+        )
+        self.residual_multiplier = args.residual_multiplier
+
+    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 * self.residual_multiplier
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r * self.residual_multiplier
+        return out
+
+
+class GraniteModel(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.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)
+        self.embedding_multiplier = args.embedding_multiplier
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        mask: mx.array = None,
+        cache=None,
+    ):
+        h = self.embed_tokens(inputs) * self.embedding_multiplier
+
+        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 = GraniteModel(args)
+        if not args.tie_word_embeddings:
+            self.lm_head = nn.Linear(args.hidden_size, args.vocab_size, bias=False)
+        self.logits_scaling = args.logits_scaling
+
+    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 / self.logits_scaling
+
+    @property
+    def layers(self):
+        return self.model.layers

llms/mlx_lm/models/hunyuan.py

@@ -76,7 +76,6 @@ class Attention(nn.Module):
 
         head_dim = args.hidden_size // n_heads
         self.scale = head_dim**-0.5
-
         self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=args.attention_bias)
         if kv_proj:
             self.k_proj = nn.Linear(
@@ -107,7 +106,6 @@ class Attention(nn.Module):
         B, L, D = x.shape
 
         queries = self.q_proj(x)
-
         if kv_states is None:
             keys, values = self.k_proj(x), self.v_proj(x)
             kv_states = keys, values
@@ -198,7 +196,10 @@ class DecoderLayer(nn.Module):
         super().__init__()
         self.hidden_size = args.hidden_size
         self.self_attn = Attention(kv_proj, args)
-        self.mlp = MoeBlock(args)
+        if args.num_experts == 1:
+            self.mlp = MLP(args.hidden_size, args.intermediate_size)
+        else:
+            self.mlp = MoeBlock(args)
 
         self.input_layernorm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
         self.post_attention_layernorm = nn.RMSNorm(
@@ -231,7 +232,10 @@ class HunYuanModel(nn.Module):
         assert self.vocab_size > 0
         self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
         self.layers = [
-            DecoderLayer(args=args, kv_proj=(i % args.cla_share_factor) == 0)
+            DecoderLayer(
+                args=args,
+                kv_proj=(not args.use_cla) or (i % args.cla_share_factor) == 0,
+            )
             for i in range(args.num_hidden_layers)
         ]
         self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
@@ -251,7 +255,7 @@ class HunYuanModel(nn.Module):
             cache = [None] * len(self.layers)
 
         for i, (layer, c) in enumerate(zip(self.layers, cache)):
-            if i % self.args.cla_share_factor == 0:
+            if (not self.args.use_cla) or i % self.args.cla_share_factor == 0:
                 shared_kv_states = None
             h, shared_kv_states = layer(h, mask, c, shared_kv_states)
 
@@ -275,6 +279,29 @@ class Model(nn.Module):
         return self.model.embed_tokens.as_linear(out)
 
     def sanitize(self, weights):
+
+        if "model.layers.0.mlp.gate_and_up_proj.weight" in weights:
+            new_weights = {}
+            D = self.args.hidden_size
+            n_kv_heads = self.args.num_key_value_heads
+            n_kv_groups = self.args.num_attention_heads // n_kv_heads
+            head_dim = D // self.args.num_attention_heads
+            for k, v in weights.items():
+                if "qkv_proj" in k:
+                    v = v.reshape(n_kv_heads, n_kv_groups + 2, head_dim, -1)
+                    splits = v.split([n_kv_groups, n_kv_groups + 1], axis=1)
+                    for k_up, v_new in zip(["q_proj", "k_proj", "v_proj"], splits):
+                        k_new = k.replace("qkv_proj", k_up)
+                        new_weights[k_new] = mx.flatten(v_new, 0, 2)
+                elif "gate_and_up_proj" in k:
+                    splits = v.split(2, axis=0)
+                    for k_up, v_new in zip(["up_proj", "gate_proj"], splits):
+                        k_new = k.replace("gate_and_up_proj", k_up)
+                        new_weights[k_new] = v_new
+                else:
+                    new_weights[k] = v
+            weights = new_weights
+
         if "model.layers.0.mlp.experts.0.up_proj.weight" not in weights:
             return weights
         for l in range(self.args.num_hidden_layers):

llms/mlx_lm/tokenizer_utils.py

@@ -1,5 +1,6 @@
 import json
 from functools import partial
+from typing import List
 
 from transformers import AutoTokenizer
 
@@ -368,3 +369,8 @@ def load_tokenizer(model_path, tokenizer_config_extra={}, eos_token_ids=None):
         detokenizer_class,
         eos_token_ids=eos_token_ids,
     )
+
+
+def no_bos_or_eos(sequence: List, bos: int, eos: int) -> List:
+    removed_bos = sequence if sequence[0] != bos else sequence[1:]
+    return removed_bos[:-1] if removed_bos[-1] == eos else removed_bos

llms/mlx_lm/tuner/datasets.py

@@ -1,6 +1,8 @@
+import itertools
 import json
+import types
 from pathlib import Path
-from typing import Dict, List, Optional, Union
+from typing import Any, Dict, List, Optional, Union
 
 from transformers import PreTrainedTokenizer
 
@@ -92,14 +94,24 @@ class ChatDataset:
     https://platform.openai.com/docs/guides/fine-tuning/example-format
     """
 
-    def __init__(self, data: List[Dict[str, str]], tokenizer: PreTrainedTokenizer):
-        self._data = [
-            tokenizer.apply_chat_template(
-                d["messages"],
-                tools=d.get("tools", None),
-            )
-            for d in data
-        ]
+    def __init__(
+        self,
+        data: List[Dict[str, str]],
+        tokenizer: PreTrainedTokenizer,
+        chat_key: str = "messages",
+        mask_prompt: bool = False,
+    ):
+        self._data = []
+        for d in data:
+            messages = d[chat_key]
+            tools = d.get("tools", None)
+            tokens = tokenizer.apply_chat_template(messages, tools=tools)
+            if mask_prompt:
+                messages = messages[:-1]
+                offset = len(tokenizer.apply_chat_template(messages, tools=tools))
+                self._data.append((tokens, offset))
+            else:
+                self._data.append(tokens)
 
     def __getitem__(self, idx: int):
         return self._data[idx]
@@ -121,16 +133,25 @@ class CompletionsDataset:
         tokenizer: PreTrainedTokenizer,
         prompt_key: str,
         completion_key: str,
+        mask_prompt: bool,
     ):
-        self._data = [
-            tokenizer.apply_chat_template(
+        self._data = []
+        for d in data:
+            tokens = tokenizer.apply_chat_template(
                 [
                     {"role": "user", "content": d[prompt_key]},
                     {"role": "assistant", "content": d[completion_key]},
                 ],
             )
-            for d in data
-        ]
+            if mask_prompt:
+                offset = len(
+                    tokenizer.apply_chat_template(
+                        [{"role": "user", "content": d[prompt_key]}]
+                    )
+                )
+                self._data.append((tokens, offset))
+            else:
+                self._data.append(tokens)
 
     def __getitem__(self, idx: int):
         return self._data[idx]
@@ -139,52 +160,60 @@ class CompletionsDataset:
         return len(self._data)
 
 
+class ConcatenatedDataset:
+    def __init__(self, data: List[Any]):
+        self._data = list(itertools.chain(*data))
+
+    def __getitem__(self, idx: int):
+        return self._data[idx]
+
+    def __len__(self):
+        return len(self._data)
+
 def create_dataset(
     args,
     data,
     tokenizer: PreTrainedTokenizer,
-    prompt_feature: Optional[str] = None,
-    completion_feature: Optional[str] = None,
+    config,
 ):
-    prompt_feature = prompt_feature or "prompt"
-    completion_feature = completion_feature or "completion"
+    mask_prompt = getattr(config, "mask_prompt", False)
+    prompt_feature = getattr(config, "prompt_feature", "prompt")
+    text_feature = getattr(config, "text_feature", "text")
+    completion_feature = getattr(config, "completion_feature", "completion")
+    chat_feature = getattr(config, "chat_feature", "messages")
     sample = data[0]
-
+    
     if args.training_mode == "normal":
-        if "messages" in sample:
-            return ChatDataset(data, tokenizer)
+        if chat_feature in sample:
+            return ChatDataset(data, tokenizer, chat_key=chat_feature, mask_prompt=mask_prompt)
         elif prompt_feature in sample and completion_feature in sample:
-            return CompletionsDataset(data, tokenizer, prompt_feature, completion_feature)
-        elif "text" in sample:
-            return Dataset(data, tokenizer)
+            return CompletionsDataset(data, tokenizer, prompt_feature, completion_feature, mask_prompt)
+        elif text_feature in sample:
+            if mask_prompt:
+                raise ValueError("Prompt masking not supported for text dataset.")
+            return Dataset(data, tokenizer, text_key=text_feature)
         else:
             raise ValueError(
                 "Unsupported data format, check the supported formats here:\n"
                 "https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md#data."
             )
-    elif args.training_mode == "orpo":
+    else:
         if "chosen" in sample and "rejected" in sample:
             return ORPODataset(data, tokenizer)
-    else:
-        raise ValueError(
-            "Unsupported training mode, check the supported training modes and their formats here:\n"
-            "https://github.com/ml-explore/mlx-examples/blob/main/llms/mlx_lm/LORA.md#training-modes."
-        )
-
 
 def load_local_dataset(
     args,
     data_path: Path,
     tokenizer: PreTrainedTokenizer,
-    prompt_feature: Optional[str] = None,
-    completion_feature: Optional[str] = None,
+    config,
 ):
     def load_subset(path):
         if not path.exists():
             return []
         with open(path, "r") as fid:
             data = [json.loads(l) for l in fid]
-        return create_dataset(args, data, tokenizer, prompt_feature, completion_feature)
+
+        return create_dataset(data, tokenizer, config)
 
     names = ("train", "valid", "test")
     train, valid, test = [load_subset(data_path / f"{n}.jsonl") for n in names]
@@ -195,8 +224,7 @@ def load_hf_dataset(
     args,
     data_id: str,
     tokenizer: PreTrainedTokenizer,
-    prompt_feature: Optional[str] = None,
-    completion_feature: Optional[str] = None,
+    config,
 ):
     from datasets import exceptions, load_dataset
 
@@ -207,9 +235,7 @@ def load_hf_dataset(
 
         train, valid, test = [
             (
-                create_dataset(
-                    args, dataset[n], tokenizer, prompt_feature, completion_feature
-                )
+                create_dataset(args, dataset[n], tokenizer, config)
                 if n in dataset.keys()
                 else []
             )
@@ -225,42 +251,61 @@ def load_hf_dataset(
 def load_custom_hf_dataset(args, tokenizer: PreTrainedTokenizer):
     import datasets
 
-    hf_args = args.hf_dataset
-    dataset_name = hf_args["name"]
-    print(f"Loading Hugging Face dataset {dataset_name}.")
-    text_feature = hf_args.get("text_feature")
-    prompt_feature = hf_args.get("prompt_feature")
-    completion_feature = hf_args.get("completion_feature")
-
-    def create_hf_dataset(split: str = None):
+    def create_hf_dataset(dataset_name, config, split, hf_config):
         ds = datasets.load_dataset(
             dataset_name,
             split=split,
-            **hf_args.get("config", {}),
+            **hf_config,
         )
-        if prompt_feature and completion_feature:
-            return CompletionsDataset(ds, tokenizer, prompt_feature, completion_feature)
-        elif text_feature:
-            return Dataset(ds, tokenizer, text_key=text_feature)
-        else:
-            raise ValueError(
-                "Specify either a prompt and completion feature or a text "
-                "feature for the Hugging Face dataset."
+        return create_dataset(ds, tokenizer, config)
+
+    dataset_collection = args.hf_dataset
+    if isinstance(dataset_collection, dict):
+        dataset_collection = [dataset_collection]
+
+    collection = []
+    for ds in dataset_collection:
+        ds_name = ds["name"]
+        print(f"Loading Hugging Face dataset {ds_name}.")
+        ds["mask_prompt"] = getattr(args, "mask_prompt", False)
+        config = types.SimpleNamespace(**ds)
+        hf_config = ds.get("config", {})
+        if args.train:
+            train_split = ds.get("train_split", "train[:80%]")
+            valid_split = ds.get("valid_split", "train[-10%:]")
+            train = create_hf_dataset(
+                ds_name,
+                config,
+                train_split,
+                hf_config,
             )
+            valid = create_hf_dataset(
+                ds_name,
+                config,
+                valid_split,
+                hf_config,
+            )
+        else:
+            train, valid = [], []
 
-    if args.train:
-        train_split = hf_args.get("train_split", "train[:80%]")
-        valid_split = hf_args.get("valid_split", "train[-10%:]")
-        train = create_hf_dataset(split=train_split)
-        valid = create_hf_dataset(split=valid_split)
-    else:
-        train, valid = [], []
-    if args.test:
-        test = create_hf_dataset(split=hf_args.get("test_split"))
-    else:
-        test = []
+        if args.test:
+            test_split = ds.get("test_split")
+            test = create_hf_dataset(
+                ds_name,
+                config,
+                test_split,
+                hf_config,
+            )
+        else:
+            test = []
 
-    return train, valid, test
+        collection.append((train, valid, test))
+
+    if len(collection) == 1:
+        return collection[0]
+
+    # Otherwise concatenate them
+    return tuple(map(ConcatenatedDataset, zip(*collection)))
 
 
 def load_dataset(args, tokenizer: PreTrainedTokenizer):
@@ -268,18 +313,11 @@ def load_dataset(args, tokenizer: PreTrainedTokenizer):
         train, valid, test = load_custom_hf_dataset(args, tokenizer)
     else:
         data_path = Path(args.data)
-
-        prompt_feature = getattr(args, "prompt_feature", None)
-        completion_feature = getattr(args, "completion_feature", None)
         if data_path.exists():
-            train, valid, test = load_local_dataset(
-                args, data_path, tokenizer, prompt_feature, completion_feature
-            )
+            train, valid, test = load_local_dataset(args, data_path, tokenizer, args)
         else:
             print(f"Loading Hugging Face dataset {args.data}.")
-            train, valid, test = load_hf_dataset(
-                args, args.data, tokenizer, prompt_feature, completion_feature
-            )
+            train, valid, test = load_hf_dataset(args.data, tokenizer, args)
 
     if args.train and len(train) == 0:
         raise ValueError(

llms/mlx_lm/tuner/trainer.py

@@ -5,13 +5,16 @@ import shutil
 import time
 from dataclasses import dataclass, field
 from pathlib import Path
-from typing import Union
+from typing import List, Optional, Tuple
 
 import mlx.core as mx
 import mlx.nn as nn
 import numpy as np
 from mlx.nn.utils import average_gradients
 from mlx.utils import tree_flatten
+from transformers import PreTrainedTokenizer
+
+from .datasets import CompletionsDataset
 
 
 def grad_checkpoint(layer):
@@ -63,20 +66,30 @@ class TrainingArgs:
     )
 
 
-def default_loss(model, inputs, targets, lengths):
+def default_loss(model, batch, lengths):
+    inputs = batch[:, :-1]
+    targets = batch[:, 1:]
+
     logits = model(inputs)
     logits = logits.astype(mx.float32)
 
-    length_mask = mx.arange(inputs.shape[1])[None, :] < lengths[:, None]
+    steps = mx.arange(1, targets.shape[1] + 1)
+    mask = mx.logical_and(steps >= lengths[:, 0:1], steps <= lengths[:, 1:])
 
-    ce = nn.losses.cross_entropy(logits, targets) * length_mask
-    ntoks = length_mask.sum()
+    ce = nn.losses.cross_entropy(logits, targets) * mask
+    ntoks = mask.sum()
     ce = ce.sum() / ntoks
 
     return ce, ntoks
 
 
-def iterate_batches(dataset, tokenizer, batch_size, max_seq_length, train=False):
+def iterate_batches(
+    dataset,
+    tokenizer,
+    batch_size,
+    max_seq_length,
+    train=False,
+):
     # Sort by length:
     idx = sorted(range(len(dataset)), key=lambda idx: len(dataset[idx]))
     if len(dataset) < batch_size:
@@ -101,6 +114,10 @@ def iterate_batches(dataset, tokenizer, batch_size, max_seq_length, train=False)
         indices = np.random.permutation(len(batch_idx))
         for i in indices:
             batch = [dataset[j] for j in batch_idx[i]]
+            if len(batch[0]) == 2:
+                batch, offsets = zip(*batch)
+            else:
+                offsets = [0] * len(batch)
             lengths = [len(x) for x in batch]
             if max(lengths) > max_seq_length:
                 print(
@@ -123,8 +140,7 @@ def iterate_batches(dataset, tokenizer, batch_size, max_seq_length, train=False)
                     truncated_length  # Update lengths to match truncated lengths
                 )
             batch = mx.array(batch_arr)
-
-            yield batch[:, :-1], batch[:, 1:], mx.array(lengths)
+            yield batch, mx.array(list(zip(offsets, lengths)))
 
         if not train:
             break

llms/mlx_lm/tuner/utils.py

@@ -94,6 +94,7 @@ def linear_to_lora_layers(
         "phimoe",
         "gemma",
         "gemma2",
+        "granite",
         "helium",
         "starcoder2",
         "cohere",

llms/tests/test_datsets.py

@@ -78,14 +78,15 @@ class TestDatasets(unittest.TestCase):
         self.assertTrue(isinstance(train, datasets.ChatDataset))
 
     def test_hf(self):
+        hf_args = {
+            "name": "billsum",
+            "prompt_feature": "text",
+            "completion_feature": "summary",
+            "train_split": "train[:2%]",
+            "valid_split": "train[-2%:]",
+        }
         args = types.SimpleNamespace(
-            hf_dataset={
-                "name": "billsum",
-                "prompt_feature": "text",
-                "completion_feature": "summary",
-                "train_split": "train[:2%]",
-                "valid_split": "train[-2%:]",
-            },
+            hf_dataset=hf_args,
             test=False,
             train=True,
         )
@@ -97,6 +98,16 @@ class TestDatasets(unittest.TestCase):
         self.assertTrue(len(valid[0]) > 0)
         self.assertEqual(len(test), 0)
 
+        args = types.SimpleNamespace(
+            hf_dataset=[hf_args, hf_args],
+            test=False,
+            train=True,
+        )
+        train_double, valid_double, test_double = datasets.load_dataset(args, tokenizer)
+        self.assertEqual(2 * len(train), len(train_double))
+        self.assertEqual(2 * len(valid), len(valid_double))
+        self.assertEqual(2 * len(test), len(test_double))
+
 
 if __name__ == "__main__":
     unittest.main()

whisper/mlx_whisper/timing.py

@@ -134,9 +134,7 @@ def find_alignment(
     logits, cross_qk = model.forward_with_cross_qk(mel[None, :], tokens[None, :])
     # consider only the logits associated with predicting text
     sampled_logits = logits[0][len(tokenizer.sot_sequence) : -2, : tokenizer.eot]
-    token_probs = mx.softmax(sampled_logits.astype(mx.float32), axis=-1).astype(
-        sampled_logits.dtype
-    )
+    token_probs = mx.softmax(sampled_logits, precise=True, axis=-1)
     text_token_probs = mx.take_along_axis(
         token_probs, mx.array(text_tokens)[:, None], axis=1
     ).squeeze(1)
@@ -144,10 +142,11 @@ def find_alignment(
 
     # heads * tokens * frames
     weights = mx.stack(
-        [cross_qk[_l.item()][0, _h.item()] for _l, _h in model.alignment_heads]
+        [cross_qk[_l][0, _h] for _l, _h in model.alignment_heads.tolist()]
     )
     weights = weights[:, :, : num_frames // 2]
-    weights = mx.softmax(weights * qk_scale, axis=-1)
+    weights = mx.softmax(weights * qk_scale, axis=-1, precise=True)
+    weights = weights.astype(mx.float32)
     mean = mx.mean(weights, axis=-2, keepdims=True)
     std = mx.var(weights, axis=-2, keepdims=True, ddof=0).sqrt()
     weights = (weights - mean) / std

whisper/mlx_whisper/whisper.py

@@ -84,7 +84,7 @@ class MultiHeadAttention(nn.Module):
         w = mx.softmax(qk, axis=-1, precise=True)
         out = (w @ v).transpose(0, 2, 1, 3)
         out = out.reshape(n_batch, n_ctx, n_state)
-        return out, qk.astype(mx.float32)
+        return out, qk
 
 
 class ResidualAttentionBlock(nn.Module):