Fix embedding quantization

modeling_chatglm.py

@@ -1408,6 +1408,11 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
 
         self.transformer = quantize(self.transformer, bits, use_quantization_cache=use_quantization_cache, empty_init=empty_init, **kwargs)
 
+        if self.device == torch.device("cpu"):
+            dtype = torch.float32
+        else:
+            dtype = torch.half
+
         if quantize_embeddings:
             logger.info("Applying quantization to embeddings")
             self.transformer.word_embeddings = QuantizedEmbedding(
@@ -1415,11 +1420,11 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
                 weight_tensor=self.transformer.word_embeddings.weight.to(self.device),
                 num_embeddings=self.transformer.word_embeddings.num_embeddings,
                 embedding_dim=self.transformer.word_embeddings.embedding_dim,
-                dtype=torch.half,
-                empty_init=True,
+                dtype=dtype,
+                empty_init=empty_init,
                 device=self.transformer.word_embeddings.weight.device,
             )
-            self.lm_head =  QuantizedLinear(
+            self.lm_head = QuantizedLinear(
                 weight_bit_width=bits,
                 weight_tensor=self.lm_head.weight.to(self.device),
                 bias_tensor=None,
@@ -1428,8 +1433,8 @@ class ChatGLMForConditionalGeneration(ChatGLMPreTrainedModel):
                 bias=False,
                 quantized_weight=self.transformer.word_embeddings.weight,
                 quantized_weight_scale=self.transformer.word_embeddings.weight_scale,
-                dtype=torch.half,
-                empty_init=True,
+                dtype=dtype,
+                empty_init=empty_init,
                 device=self.lm_head.weight.device,
             )