Ministral3 (#642)

* attempt ministral3, no tokenizer

* ministral3 works

Author: awni

mlx_lm/models/ministral3.py

@@ -0,0 +1,264 @@
+# Copyright © 2023-2024 Apple Inc.
+
+from dataclasses import dataclass
+from typing import Any, Dict, List, Optional, Union
+
+import mlx.core as mx
+import mlx.nn as nn
+
+from .base import BaseModelArgs, create_attention_mask, scaled_dot_product_attention
+from .cache import KVCache, RotatingKVCache
+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
+    head_dim: Optional[int] = None
+    max_position_embeddings: Optional[int] = None
+    num_key_value_heads: Optional[int] = None
+    rope_parameters: Optional[Dict[str, Union[float, str]]] = None
+    tie_word_embeddings: bool = True
+    layer_types: Optional[List[str]] = None
+    sliding_window: Optional[int] = None
+
+    def __post_init__(self):
+        if self.num_key_value_heads is None:
+            self.num_key_value_heads = self.num_attention_heads
+
+        if self.layer_types is None:
+            self.layer_types = ["full_attention"] * self.num_hidden_layers
+
+
+def _get_llama_4_attn_scale(
+    start: int, stop: int, beta: float, max_position_embeddings: int
+):
+    scaling = 1 + beta * mx.log(
+        1 + mx.floor(mx.arange(start, stop) / max_position_embeddings)
+    )
+    return scaling[:, None]
+
+
+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.head_dim or args.hidden_size // n_heads
+
+        self.scale = head_dim**-0.5
+
+        self.q_proj = nn.Linear(dim, n_heads * head_dim, bias=False)
+        self.k_proj = nn.Linear(dim, n_kv_heads * head_dim, bias=False)
+        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)
+
+        self.rope = initialize_rope(
+            self.head_dim,
+            args.rope_parameters["rope_theta"],
+            False,
+            args.rope_parameters,
+            args.max_position_embeddings,
+        )
+
+    def __call__(
+        self,
+        x: mx.array,
+        attn_scale: 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)
+
+        offset = 0
+        if cache is not None:
+            offset = cache.offset
+            queries = self.rope(queries, offset=offset)
+            keys = self.rope(keys, offset=offset)
+            keys, values = cache.update_and_fetch(keys, values)
+        else:
+            queries = self.rope(queries)
+            keys = self.rope(keys)
+        queries = queries * attn_scale
+        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
+        self.gate_proj = nn.Linear(dim, hidden_dim, bias=False)
+        self.down_proj = nn.Linear(hidden_dim, dim, bias=False)
+        self.up_proj = nn.Linear(dim, hidden_dim, bias=False)
+
+    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, use_sliding: bool = False):
+        super().__init__()
+        self.num_attention_heads = args.num_attention_heads
+        self.hidden_size = args.hidden_size
+        self.use_sliding = use_sliding
+        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.args = args
+
+    def __call__(
+        self,
+        x: mx.array,
+        attn_scale: mx.array,
+        mask: Optional[mx.array] = None,
+        cache: Optional[Any] = None,
+    ) -> mx.array:
+        r = self.self_attn(self.input_layernorm(x), attn_scale, mask, cache)
+        h = x + r
+        r = self.mlp(self.post_attention_layernorm(h))
+        out = h + r
+        return out
+
+
+class LanguageModel(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
+        self.layer_types = args.layer_types
+        self.sliding_window = args.sliding_window
+        self.embed_tokens = nn.Embedding(args.vocab_size, args.hidden_size)
+        self.layers = [
+            TransformerBlock(args=args, use_sliding=layer_type == "sliding_attention")
+            for layer_type in self.layer_types
+        ]
+        self.norm = nn.RMSNorm(args.hidden_size, eps=args.rms_norm_eps)
+        self.fa_idx = self.layer_types.index("full_attention")
+        self.swa_idx = None
+        for e, l in enumerate(self.layers):
+            if l.use_sliding:
+                self.swa_idx = e
+                break
+
+    def __call__(
+        self,
+        inputs: mx.array,
+        cache=None,
+        input_embeddings: Optional[mx.array] = None,
+    ):
+        if input_embeddings is not None:
+            h = input_embeddings
+        else:
+            h = self.embed_tokens(inputs)
+
+        if cache is None:
+            cache = [None] * len(self.layers)
+            offset = 0
+        else:
+            offset = cache[0].offset
+
+        fa_mask = create_attention_mask(h, cache[self.fa_idx])
+        if self.swa_idx is not None:
+            swa_mask = create_attention_mask(
+                h, cache[self.swa_idx], window_size=self.sliding_window
+            )
+
+        attn_scale = _get_llama_4_attn_scale(
+            offset,
+            offset + inputs.shape[1],
+            self.args.rope_parameters["llama_4_scaling_beta"],
+            self.args.rope_parameters["original_max_position_embeddings"],
+        ).astype(h.dtype)
+
+        for layer, cache in zip(self.layers, cache):
+            mask = swa_mask if layer.use_sliding else fa_mask
+            h = layer(h, attn_scale, mask, cache=cache)
+
+        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 = LanguageModel(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,
+        cache=None,
+        input_embeddings: Optional[mx.array] = None,
+    ):
+        out = self.model(inputs, cache, input_embeddings)
+        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
+        weights = {
+            k: v for k, v in weights.items() if "self_attn.rotary_emb.inv_freq" not in k
+        }
+        if self.args.tie_word_embeddings:
+            weights.pop("lm_head.weight", None)
+
+        new_weights = {}
+        for k, v in weights.items():
+            if "weight_scale_inv" in k:
+                scale_inv = v
+                wk = k.replace("_scale_inv", "")
+                weight = weights[wk]
+                new_weights[wk] = weight * scale_inv
+            elif "activation_scale" in k:
+                continue
+            elif k not in new_weights:
+                new_weights[k] = v
+        weights = new_weights
+
+        return weights
+
+    @property
+    def layers(self):
+        return self.model.layers
+
+    def make_cache(self):
+        return [
+            (
+                RotatingKVCache(max_size=self.model.sliding_window)
+                if layer.use_sliding
+                else KVCache()
+            )
+            for layer in self.layers
+        ]

mlx_lm/models/mistral3.py

@@ -7,7 +7,7 @@
 import mlx.nn as nn
 from mlx.utils import tree_flatten, tree_unflatten
 
-from . import llama
+from . import llama, ministral3
 from .base import BaseModelArgs
 
 
@@ -17,15 +17,23 @@ class ModelArgs(BaseModelArgs):
     text_config: dict
 
     def __post_init__(self):
-        self.text_config["tie_word_embeddings"] = False
+        if "tie_word_embeddings" not in self.text_config:
+            self.text_config["tie_word_embeddings"] = False
 
 
 class Model(nn.Module):
     def __init__(self, args: ModelArgs):
         super().__init__()
         self.args = args
         self.model_type = args.model_type
-        self.language_model = llama.Model(llama.ModelArgs.from_dict(args.text_config))
+        if args.text_config.get("model_type") == "ministral3":
+            self.language_model = ministral3.Model(
+                ministral3.ModelArgs.from_dict(args.text_config)
+            )
+        else:
+            self.language_model = llama.Model(
+                llama.ModelArgs.from_dict(args.text_config)
+            )
 
     def __call__(
         self,
@@ -41,6 +49,8 @@ def sanitize(self, weights):
         weights = tree_unflatten(list(weights.items()))
         weights.pop("vision_tower", None)
         weights.pop("multi_modal_projector", None)
+        lm_weights = dict(tree_flatten(weights["language_model"]))
+        weights["language_model"] = self.language_model.sanitize(lm_weights)
         return dict(tree_flatten(weights))
 
     @property