Add OnnxIOFloat16ToFloat32 Pass

docs/source/api/passes.rst

@@ -42,6 +42,12 @@ OnnxFloatToFloat16
 --------------------
 .. autoconfigclass:: olive.passes.OnnxFloatToFloat16
 
+.. _onnx_io_float16_to_float32:
+
+OnnxIOFloat16ToFloat32
+----------------------
+.. autoconfigclass:: olive.passes.OnnxIOFloat16ToFloat32
+
 .. _ort_mixed_precision:
 
 OrtMixedPrecision

docs/source/features/passes/onnx.md

@@ -428,6 +428,19 @@ b. More fine-grained control of the conversion conditions is also possible:
 
 See [Float16 Conversion](https://onnxruntime.ai/docs/performance/model-optimizations/float16.html#float16-conversion) for more detailed description of the available configuration parameters.
 
+## Inputs/Outputs Float16 to Float32 Conversion
+
+Certain environments such as Onnxruntime WebGPU prefers Float32 logits. The `OnnxIOFloat16ToFloat32` pass converts the inputs and outputs to use Float32 instead of Float16.
+
+### Example Configuration
+
+a. The most basic configuration, which is suitable for many models, leaves all configuration options set to their default values:
+```json
+{
+    "type": "OnnxIOFloat16ToFloat32"
+}
+```
+
 ## Mixed Precision Conversion
 Converting model to mixed precision.
 

examples/phi2/phi2.py

@@ -22,6 +22,7 @@
     "cuda_fp16": [["convert", "optimize_cuda", "perf_tuning"]],
     "cuda_int4": [["convert", "optimize_cuda", "blockwise_quant_int4", "perf_tuning"]],
     "slicegpt": [["slice"]],
+    "web": [["builder", "io_float16_to_float32"]],
 }
 SUPPORTED_INFERENCE_CONFIG = {
     "cpu_fp32": {
@@ -54,6 +55,7 @@
 DEVICE_TO_EP = {
     "cpu": "CPUExecutionProvider",
     "gpu": "CUDAExecutionProvider",
+    "web": "JsExecutionProvider",
 }
 
 
@@ -64,8 +66,8 @@ def get_args(raw_args):
         "--model_type",
         type=str,
         default=None,
-        choices=["cpu_fp32", "cpu_int4", "cuda_fp16", "cuda_int4"],
-        help="Choose from cpu_fp32, cpu_int4, cuda_fp16, cuda_int4",
+        choices=["cpu_fp32", "cpu_int4", "cuda_fp16", "cuda_int4", "web"],
+        help="Choose from cpu_fp32, cpu_int4, cuda_fp16, cuda_int4 or web",
     )
     parser.add_argument(
         "--finetune_method",
@@ -141,11 +143,22 @@ def main(raw_args=None):
             template_json["systems"]["local_system"]["config"]["accelerators"] = [
                 {"device": device, "execution_providers": [DEVICE_TO_EP[device.lower()]]}
             ]
-
-        new_json_file = f"phi2_genai_{device.lower()}.json"
+        new_json_file = "phi2_web.json"
+        with open(new_json_file, "w") as f:
+            json.dump(template_json, f, indent=4)
+    elif model_type == "web":
+        json_file_template = "phi2_genai.json"
+        with open(json_file_template) as f:
+            template_json = json.load(f)
+            template_json["passes"]["builder"]["config"]["precision"] = "int4"
+            template_json["systems"]["local_system"]["config"]["accelerators"] = [
+                {"device": "GPU", "execution_providers": ["JsExecutionProvider"]}
+            ]
+            fl_type = {"type": "OnnxIOFloat16ToFloat32"}
+            template_json["passes"]["fp32_logits"] = fl_type
+        new_json_file = "phi2_web.json"
         with open(new_json_file, "w") as f:
             json.dump(template_json, f, indent=4)
-
     else:
         if not args.optimum_optimization and not args.slicegpt and version.parse(OrtVersion) < version.parse("1.18.0"):
             # Check if onnxruntime version is supported

examples/phi3/README.md

@@ -0,0 +1,48 @@
+# Phi3 optimization with Olive
+This folder contains an example of optimizing [the Phi-3-Mini-4K-Instruct](https://huggingface.co/microsoft/Phi-3-mini-4k-instruct) model in HF for different hardware targets with Olive.
+
+
+## Prerequisites
+* einops
+* Pytorch: >=2.2.0 \
+  _The [official website](https://pytorch.org/) offers packages compatible with CUDA 11.8 and 12.1. Please select the appropriate version according to your needs._
+* [Package onnxruntime](https://onnxruntime.ai/docs/install/#inference-install-table-for-all-languages): >=1.18.0
+* [Package onnxruntime-genai](https://github.com/microsoft/onnxruntime-genai): >=0.2.0. If you target GPU, pls install onnxruntime and onnxruntime-genai gpu packages.
+
+Install the dependencies
+```
+pip install -r requirements.txt
+```
+
+## Usage
+we will use the `phi3.py` script to generate optimized model for a chosen hardware target by running the following commands.
+
+```
+python phi3.py [--target HARDWARE_TARGET] [--precision DATA_TYPE] [--inference] [--prompt PROMPT] [--max_length LENGTH]
+
+# Examples
+python phi3.py --target web
+
+python phi3.py --target mobile --inference --prompt "Write a story starting with once upon a time" --max_length 200
+```
+
+- `--target`: cpu, cuda, mobile, web
+- `--precision`: optional. fp32, fp16, int4. fp32 or int4(default) for cpu target; fp32 or fp16 or int4(default) for gpu target; int4(default) for mobile or web
+- `--inference`: run the optimized model, for non-web models inference.
+- `--prompt`: optional, the prompt text fed into the model. Take effect only when `--inference` is set.
+- `--max_length`: optional, the max length of the output from the model. Take effect only when `--inference` is set.
+
+
+This script includes
+1. Generate the Olive configuration file for your need including the chosen HW target, the preferred model precision.
+2. Generate optimized model with Olive based on the configuration file for the chosen HW target
+3. (optional) Inference the optimized model with ONNX Runtime Generation API. Not supported for web target
+
+
+If you have an Olive configuration file, you can also run the olive command for model generation:
+```
+olive run [--config CONFIGURATION_FILE]
+
+# Examples
+olive run --config phi3_mobile_int4.json
+```

examples/phi3/phi3.py

@@ -0,0 +1,212 @@
+# -------------------------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# Licensed under the MIT License.
+# --------------------------------------------------------------------------
+
+import argparse
+import json
+import time
+from pathlib import Path
+
+import onnxruntime_genai as og
+
+from olive.workflows import run as olive_run
+
+# flake8: noqa: T201
+
+
+TARGETS = ["cpu", "cuda", "mobile", "web"]
+
+TARGET_TO_EP = {
+    "cpu": "CPUExecutionProvider",
+    "mobile": "CPUExecutionProvider",
+    "cuda": "CUDAExecutionProvider",
+    "web": "JsExecutionProvider",
+}
+
+
+def get_args(raw_args):
+    parser = argparse.ArgumentParser(description="phi3 optimization")
+
+    parser.add_argument(
+        "--target",
+        type=str,
+        default=None,
+        required=True,
+        choices=TARGETS,
+        help="Choose from cpu, cuda, mobile or web",
+    )
+    parser.add_argument(
+        "--precision",
+        type=str,
+        default="int4",
+        choices=["fp32", "fp16", "int4"],
+        help="Choose from fp32 or int4(default) for cpu target; "
+        "fp32 or fp16 or int4(default) for gpu target; int4(default) for mobile or web",
+    )
+    parser.add_argument(
+        "--inference",
+        action="store_true",
+        help="Run inference with optimized model",
+    )
+    parser.add_argument(
+        "--prompt",
+        nargs="*",
+        type=str,
+        default=["Write a joke"],
+        help="The prompt text fed into the model. Not supported with Web target.",
+    )
+    parser.add_argument(
+        "--max_length",
+        type=int,
+        default=200,
+        help="Max length for generation. Not supported with Web target.",
+    )
+
+    return parser.parse_args(raw_args)
+
+
+def main(raw_args=None):
+    args = get_args(raw_args)
+    if args.target in ("mobile", "web") and args.precision != "int4":
+        raise ValueError("mobile or web only supports int4(default)")
+    elif args.target == "cpu" and args.precision == "fp16":
+        raise ValueError("Choose from fp32 or int4(default) for cpu target")
+
+    if args.inference and args.target == "web":
+        raise ValueError("Web model inference is not supported in this script")
+
+    # Generate Olive configuration file for specific target
+    print("\nGenerating Olive configuration file...")
+    config_file = generate_config(args)
+    print("Olive configuration file is generated...\n")
+
+    # Generate optimized model for specific target
+    print("Generating optimized model for", args.target, " ...\n")
+    footprints = olive_run(config_file)
+    if footprints:
+        print("\nOptimized model is generated...")
+
+    if args.inference:
+        prompts = "Write a joke" if not args.prompt else "".join(args.prompt)
+
+        chat_template = "<|user|>\n{input}<|end|>\n<|assistant|>"
+        prompts = f"{chat_template.format(input=prompts)}"
+
+        max_length = 200 if not args.max_length else args.max_length
+
+        output_model_path = get_output_model_path(footprints)
+        genai_run(prompts, str(output_model_path), max_length)
+
+
+def generate_config(args):
+
+    json_file_template = "phi3_template.json"
+    with open(json_file_template) as f:
+        template_json = json.load(f)
+
+    target = str(args.target)
+    device = "GPU" if target in ("cuda", "web") else "CPU"
+    execution_providers = [TARGET_TO_EP[target.lower()]]
+    template_json["systems"]["local_system"]["config"]["accelerators"] = [
+        {"device": device, "execution_providers": execution_providers}
+    ]
+
+    model_builder = {
+        "type": "ModelBuilder",
+        "config": {
+            "precision": args.precision,
+        },
+    }
+    template_json["passes"]["builder"] = model_builder
+
+    if target == "mobile":
+        template_json["passes"]["builder"]["config"]["int4_accuracy_level"] = 4
+
+    elif target == "web":
+        fl_type = {"type": "OnnxIOFloat16ToFloat32"}
+        template_json["passes"]["fp32_logits"] = fl_type
+
+    new_json_file = f"phi3_{target.lower()}_{args.precision}.json"
+    with open(new_json_file, "w") as f:
+        json.dump(template_json, f, indent=4)
+
+    return new_json_file
+
+
+def get_output_model_path(footprints):
+    # only one model output in phi2 optimization
+    for footprint in footprints.values():
+        for model_id in footprint.nodes:
+            model_path = Path(footprint.get_model_path(model_id))
+            break
+    return model_path
+
+
+def genai_run(prompt, model_path, max_length):
+
+    print("\nModel inference starts...")
+
+    print("Loading model...")
+    app_started_timestamp = time.time()
+    model = og.Model(model_path)
+    model_loaded_timestamp = time.time()
+    print("Model loaded in {:.2f} seconds".format(model_loaded_timestamp - app_started_timestamp))
+
+    print("Creating tokenizer...")
+    tokenizer = og.Tokenizer(model)
+    tokenizer_stream = tokenizer.create_stream()
+    input_tokens = tokenizer.encode(prompt)
+    started_timestamp = time.time()
+
+    print("Creating generator ...")
+    params = og.GeneratorParams(model)
+    # optimal search options for Phi3
+    search_options = {
+        "max_length": max_length,
+        "top_k": 40,
+        "top_p": 0.95,
+        "temperature": 0.8,
+        "repetition_penalty": 1.0,
+    }
+    params.set_search_options(**search_options)
+    params.input_ids = input_tokens
+    generator = og.Generator(model, params)
+    print("Generator created")
+
+    first = True
+    first_token_timestamp = None
+    new_tokens = []
+
+    print("\n", prompt)
+
+    try:
+        while not generator.is_done():
+            generator.compute_logits()
+            generator.generate_next_token()
+            if first:
+                first_token_timestamp = time.time()
+                first = False
+
+            new_token = generator.get_next_tokens()[0]
+            print(tokenizer_stream.decode(new_token), end="", flush=True)
+            new_tokens.append(new_token)
+    except KeyboardInterrupt:
+        print("  --control+c pressed, aborting generation--")
+
+    del generator
+
+    run_time = time.time() - started_timestamp
+    if first_token_timestamp is None:
+        print("\n\nNo tokens generated")
+    else:
+        print(
+            "\n\n"
+            f"Prompt tokens: {len(input_tokens)}, New tokens: {len(new_tokens)},"
+            f" Time to first: {(first_token_timestamp - started_timestamp):.2f}s,"
+            f" New tokens per second: {len(new_tokens)/run_time:.2f} tps"
+        )
+
+
+if __name__ == "__main__":
+    main()

examples/phi3/phi3_template.json

@@ -0,0 +1,38 @@
+{
+    "input_model":{
+        "type": "PyTorchModel",
+        "config": {
+            "hf_config": {
+                "model_name": "microsoft/Phi-3-mini-4k-instruct",
+                "task": "text-generation",
+                "from_pretrained_args": {
+                    "trust_remote_code": true
+                }
+            }
+        }
+    },
+    "systems": {
+        "local_system": {
+            "type": "LocalSystem",
+            "config": {
+                "accelerators": [
+                    {
+                        "device": "CPU",
+                        "execution_providers": [
+                            "CPUExecutionProvider"
+                        ]
+                    }
+                ]
+            }
+        }
+    },
+    "passes": {
+
+    },
+    "engine": {
+        "cache_dir": "cache",
+        "output_dir": "Opt_model",
+        "host": "local_system",
+        "target": "local_system"
+    }
+}

examples/phi3/requirements.txt

@@ -0,0 +1,5 @@
+einops
+onnx>=1.15.0
+onnxscript>=0.1.0.dev20240126
+torch>=2.2.0
+transformers>=4.36.2