cleanup for webgpu option

src/python/py/models/builder.py

@@ -65,7 +65,7 @@ def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
                 "tunable_op_tuning_enable": "1",
             },
             "dml": {},
-            "web": {},
+            "webgpu": {},
         }
 
         # Map input names to their types and shapes
@@ -246,6 +246,8 @@ def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
             ("cuda", TensorProto.FLOAT16),
             ("rocm", TensorProto.FLOAT16),
             ("dml", TensorProto.FLOAT16),
+            ("webgpu", TensorProto.FLOAT16),
+            ("webgpu", TensorProto.FLOAT),
         ]
         if (self.ep, self.io_dtype) in valid_gqa_configurations:
             # Change model settings for GroupQueryAttention
@@ -254,10 +256,10 @@ def __init__(self, config, io_dtype, onnx_dtype, ep, cache_dir, extra_options):
 
             # DML doesn't support packed Q/K/V for GQA yet
             # Packed MatMul with LoRA/QLoRA is not currently supported
-            self.attention_attrs["use_packed_matmul"] = self.ep != "dml" and not self.matmul_attrs["use_lora"]
+            self.attention_attrs["use_packed_matmul"] = self.ep not in ["dml", "webgpu"] and not self.matmul_attrs["use_lora"]
 
             # GQA + Rot.Emb. does not require `position ids` as input
-            if self.ep != "dml":
+            if self.ep not in ["dml", "webgpu"]:
                 self.attention_attrs["use_rotemb_in_attn"] = True
                 self.input_names.remove("position_ids")
 
@@ -3195,7 +3197,8 @@ def create_model(model_name, input_path, output_dir, precision, execution_provid
         config.update(peft_config.__dict__)
 
     # Set input/output precision of ONNX model
-    io_dtype = TensorProto.FLOAT if precision in {"int8", "fp32"} or (precision == "int4" and execution_provider == "cpu") else TensorProto.FLOAT16
+    use_webgpu_fp32 = extra_options.get("use_webgpu_fp32", "0") == "1"
+    io_dtype = TensorProto.FLOAT if precision in {"int8", "fp32"} or (precision == "int4" and execution_provider == "cpu") or use_webgpu_fp32 else TensorProto.FLOAT16
 
     if "config_only" not in extra_options:
         # List architecture options in alphabetical order
@@ -3299,8 +3302,8 @@ def get_args():
         "-e",
         "--execution_provider",
         required=True,
-        choices=["cpu", "cuda", "rocm", "dml", "web"],
-        help="Execution provider to target with precision of model (e.g. FP16 CUDA, INT4 CPU, INT4 WEB)",
+        choices=["cpu", "cuda", "rocm", "dml", "webgpu"],
+        help="Execution provider to target with precision of model (e.g. FP16 CUDA, INT4 CPU, INT4 WEBGPU)",
     )
 
     parser.add_argument(
@@ -3358,6 +3361,8 @@ def get_args():
                     If true, the QMoE op will use 8-bit quantization. If false, the QMoE op will use 4-bit quantization.
                 use_qdq = Use the QDQ decomposition for ops.
                     Use this option when you want to use quantize-dequantize ops. For example, you will have a quantized MatMul op instead of the MatMulNBits op.
+                use_webgpu_fp32 = Use fp32 for webgpu ep.
+                    Use this option to enable gpus that do not support fp16 on webgpu, ie gtx10xx.
                 adapter_path = Path to folder on disk containing the adapter files (adapter_config.json and adapter model weights).
                     Use this option for LoRA models.
                 include_prompt_templates = Include prompt templates in the GenAI config file. Default is false.
@@ -3366,7 +3371,7 @@ def get_args():
     )
 
     args = parser.parse_args()
-    print("Valid precision + execution provider combinations are: FP32 CPU, FP32 CUDA, FP16 CUDA, FP16 DML, INT4 CPU, INT4 CUDA, INT4 DML")
+    print("Valid precision + execution provider combinations are: FP32 CPU, FP32 CUDA, FP16 CUDA, FP16 DML, INT4 CPU, INT4 CUDA, INT4 DML, INT4 WEBGPU")
     return args
 
 if __name__ == '__main__':