fix(canary): use dynamo export, single input_ids and avoid 0/1 specialization (#2348)

@@ -197,12 +197,12 @@ def export_decoder(canary_model):
     decoder = DecoderWrapper(canary_model)
     decoder = DecoderWrapper(canary_model)
     decoder_input_ids = torch.tensor([[1, 0]], dtype=torch.int32)
     decoder_input_ids = torch.tensor([[1, 0]], dtype=torch.int32)
 
 
-    decoder_mems_list_0 = torch.zeros(1, 1, 1024)
-    decoder_mems_list_1 = torch.zeros(1, 1, 1024)
-    decoder_mems_list_2 = torch.zeros(1, 1, 1024)
-    decoder_mems_list_3 = torch.zeros(1, 1, 1024)
-    decoder_mems_list_4 = torch.zeros(1, 1, 1024)
-    decoder_mems_list_5 = torch.zeros(1, 1, 1024)
+    decoder_mems_list_0 = torch.zeros(1, 10, 1024)
+    decoder_mems_list_1 = torch.zeros(1, 10, 1024)
+    decoder_mems_list_2 = torch.zeros(1, 10, 1024)
+    decoder_mems_list_3 = torch.zeros(1, 10, 1024)
+    decoder_mems_list_4 = torch.zeros(1, 10, 1024)
+    decoder_mems_list_5 = torch.zeros(1, 10, 1024)
 
 
     enc_states = torch.zeros(1, 1000, 1024)
     enc_states = torch.zeros(1, 1000, 1024)
     enc_mask = torch.ones(1, 1000).bool()
     enc_mask = torch.ones(1, 1000).bool()
@@ -221,7 +221,9 @@ def export_decoder(canary_model):
             enc_mask,
             enc_mask,
         ),
         ),
         "decoder.onnx",
         "decoder.onnx",
-        opset_version=14,
+        dynamo=True,
+        opset_version=18,
+        external_data=False,
         input_names=[
         input_names=[
             "decoder_input_ids",
             "decoder_input_ids",
             "decoder_mems_list_0",
             "decoder_mems_list_0",
@@ -272,13 +274,11 @@ def main():
     export_decoder(canary_model)
     export_decoder(canary_model)
 
 
     for m in ["encoder", "decoder"]:
     for m in ["encoder", "decoder"]:
-        if m == "encoder":
-            # we don't quantize the decoder with int8 since the accuracy drops
-            quantize_dynamic(
-                model_input=f"./{m}.onnx",
-                model_output=f"./{m}.int8.onnx",
-                weight_type=QuantType.QUInt8,
-            )
+        quantize_dynamic(
+            model_input=f"./{m}.onnx",
+            model_output=f"./{m}.int8.onnx",
+            weight_type=QuantType.QUInt8,
+        )
 
 
         export_onnx_fp16(f"{m}.onnx", f"{m}.fp16.onnx")
         export_onnx_fp16(f"{m}.onnx", f"{m}.fp16.onnx")
 
 

@@ -263,16 +263,15 @@ def main():
     decoder_input_ids.append(token2id["<|notimestamp|>"])
     decoder_input_ids.append(token2id["<|notimestamp|>"])
     decoder_input_ids.append(token2id["<|nodiarize|>"])
     decoder_input_ids.append(token2id["<|nodiarize|>"])
 
 
-    decoder_input_ids.append(0)
-
     decoder_mems_list = [np.zeros((1, 0, 1024), dtype=np.float32) for _ in range(6)]
     decoder_mems_list = [np.zeros((1, 0, 1024), dtype=np.float32) for _ in range(6)]
 
 
-    logits, decoder_mems_list = model.run_decoder(
-        np.array([decoder_input_ids], dtype=np.int32),
-        decoder_mems_list,
-        enc_states,
-        enc_masks,
-    )
+    for pos, decoder_input_id in enumerate(decoder_input_ids):
+        logits, decoder_mems_list = model.run_decoder(
+            np.array([[decoder_input_id,pos]], dtype=np.int32),
+            decoder_mems_list,
+            enc_states,
+            enc_masks,
+        )
     tokens = [logits.argmax()]
     tokens = [logits.argmax()]
     print("decoder_input_ids", decoder_input_ids)
     print("decoder_input_ids", decoder_input_ids)
     eos = token2id["<|endoftext|>"]
     eos = token2id["<|endoftext|>"]