Quantize embedding / Update quantize API (#680)

* more async eval

* quantize embedding / update quantize api

* more updates for quantize

* update for quantize embeddings

* update sd quant API

* update sdxl quants

* error for datasets < batch_size

* async

* fix config loading

* fix quant

* fix tests

* fix req

* remove lm head if tie weights is true

* fix test

llms/mixtral/convert.py

@@ -60,13 +60,10 @@ def quantize(weights, config, args):
     model.update(all_weights)
 
     # Quantize the model:
-    nn.QuantizedLinear.quantize_module(
+    nn.quantize(
         model,
         args.q_group_size,
         args.q_bits,
-        # TODO: Quantize gate matrices when < 32 tiles supported
-        linear_class_predicate=lambda m: isinstance(m, nn.Linear)
-        and m.weight.shape[0] != 8,
     )
 
     # Extract the subset of quantized weights:

llms/mixtral/mixtral.py

@@ -217,11 +217,7 @@ def load_model(folder: str):
     weights = tree_unflatten(list(weights.items()))
     model = Mixtral(model_args)
     if quantization is not None:
-        # TODO: Quantize gate matrices when < 32 tiles supported
-        quantization["linear_class_predicate"] = (
-            lambda m: isinstance(m, nn.Linear) and m.weight.shape[0] != 8
-        )
-        nn.QuantizedLinear.quantize_module(model, **quantization)
+        nn.quantize(model, **quantization)
 
     model.update(weights)
     return model, tokenizer

llms/mixtral/requirements.txt

@@ -1,4 +1,4 @@
-mlx>=0.8.0
+mlx>=0.11.0
 sentencepiece
 torch
 numpy