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

whisper/convert.py

@@ -254,7 +254,7 @@ def quantize(weights, config, args):
     model.update(tree_unflatten(list(weights.items())))
 
     # Quantize the model:
-    nn.QuantizedLinear.quantize_module(model, args.q_group_size, args.q_bits)
+    nn.quantize(model, args.q_group_size, args.q_bits)
 
     # Update the config:
     quantized_config["quantization"] = {

whisper/requirements.txt

@@ -1,4 +1,4 @@
-mlx>=0.8
+mlx>=0.11
 numba
 numpy
 torch

whisper/whisper/load_models.py

@@ -32,7 +32,7 @@ def load_model(
     model = whisper.Whisper(model_args, dtype)
 
     if quantization is not None:
-        nn.QuantizedLinear.quantize_module(model, **quantization)
+        nn.quantize(model, **quantization)
 
     model.update(weights)
     mx.eval(model.parameters())

whisper/whisper/whisper.py

@@ -196,7 +196,7 @@ class TextDecoder(nn.Module):
             )
 
         x = self.ln(x)
-        return x @ self.token_embedding.weight.T, kv_cache, cross_qk
+        return self.token_embedding.as_linear(x), kv_cache, cross_qk
 
 
 class Whisper(nn.Module):