[RUNTIME][COMPILER] Formal compiler pipeline, runtime wrapper module (a…

…pache#21)

* [RUNTIME][COMPILER] Formal compiler pipeline, runtime wrapper module

* more detailed comments

nnvm/python/nnvm/compiler/__init__.py

@@ -4,7 +4,7 @@
 import tvm
 
 from . import build_module
-from . build_module import build, precompute_prune, _run_graph
+from . build_module import build, optimize, build_config
 
 from .. import symbol as _symbol
 from .. import graph as _graph

nnvm/python/nnvm/compiler/build_module.py

@@ -3,10 +3,74 @@
 from __future__ import absolute_import as _abs
 
 import tvm
-from . import graph_attr, graph_pass
+from . import graph_attr, graph_util
 from .. import graph as _graph
 from .. import runtime
 
+OPT_PASS_LEVEL = {
+    "SimplifyBatchNormInference": 2,
+    "PrecomputePrune": 2,
+    "OpFusion": 1
+}
+
+# List of optimization pass and level when switch on
+class BuildConfig(object):
+    """Configuration scope to set a build config option.
+
+    Parameters
+    ----------
+    kwargs
+        Keyword arguments of configurations to set.
+    """
+    current = None
+    defaults = {
+        "opt_level": 2,
+    }
+    def __init__(self, **kwargs):
+        self._old_scope = None
+        for k, _ in kwargs.items():
+            if k not in BuildConfig.defaults:
+                raise ValueError(
+                    "invalid argument %s, candidates are %s" % (k, BuildConfig.defaults.keys()))
+        self._attr = kwargs
+
+    def __getattr__(self, name):
+        if name not in self._attr:
+            return BuildConfig.defaults[name]
+        return self._attr[name]
+
+    def __enter__(self):
+        # pylint: disable=protected-access
+        self._old_scope = BuildConfig.current
+        attr = BuildConfig.current._attr.copy()
+        attr.update(self._attr)
+        self._attr = attr
+        BuildConfig.current = self
+        return self
+
+    def __exit__(self, ptype, value, trace):
+        assert self._old_scope
+        BuildConfig.current = self._old_scope
+
+
+BuildConfig.current = BuildConfig()
+
+def build_config(**kwargs):
+    """Configure the build behavior by setting config variables.
+
+    Parameters
+    ----------
+    opt_level: int, default=2
+        Optimization level. See OPT_PASS_LEVEL for level of each pass.
+
+    Returns
+    -------
+    config: BuildConfig
+        The build configuration
+    """
+    return BuildConfig(**kwargs)
+
+
 @tvm.register_func("nnvm.compiler.lower")
 def _lower(sch, inputs, func_name):
     f = tvm.lower(sch, inputs, name=func_name)
@@ -19,23 +83,45 @@ def _build(funcs, target):
     return tvm.build(funcs, target=target)
 
 
-def optimize(graph):
-    """Perform graph optimization
+def _update_shape_dtype(shape, dtype, params):
+    """Update shape dtype given params information"""
+    if not params:
+        return shape, dtype
+    shape = shape.copy()
+    shape.update({k : v.shape for k, v in params.items()})
+    if isinstance(dtype, str):
+        for k, v in params.items():
+            if v.dtype != dtype:
+                raise ValueError(
+                    "%s: dtype not expected %s vs %s" % (k, dtype, v.dtype))
+    else:
+        dtype = dtype.copy()
+        dtype.update({k : str(v.dtype) for k, v in params.items()})
+    return shape, dtype
+
+
+def optimize(graph, shape, dtype="float32"):
+    """Perform target and parameter invariant graph optimization.
 
     Parameters
     ----------
     graph : Graph
-        The graph to be used in lowering.
+        The graph to be used in optimized.
 
     Returns
     -------
     graph : Graph
-        The optimized execution graph.
+        The optimized graph.
     """
+    # pylint: disable=unused-argument
+    cfg = BuildConfig.current
+    if cfg.opt_level >= OPT_PASS_LEVEL["SimplifyBatchNormInference"]:
+        graph = graph_attr.set_shape_inputs(graph, shape)
+        graph = graph.apply(["InferShape", "SimplifyBatchNormInference"])
     return graph
 
 
-def build(graph, target, shape, dtype="float32"):
+def build(graph, target, shape, dtype="float32", params=None):
     """Build graph into runtime library.
 
     This is the final step of graph compilation.
@@ -54,27 +140,45 @@ def build(graph, target, shape, dtype="float32"):
     dtype : str or dict of str to str
         The input types to the graph
 
+    params : dict of str to NDArray
+        Input parameetrs to the graph that do not change
+        during inference time. Used for pre-compute
+        folding optimization.
+
     Returns
     -------
     graph : Graph
         The final execution graph.
 
     libmod : tvm.Module
         The modue that comes with the execution graph
+
+    params : dict of str to NDArray
+        The updated parameters of graph if params is passed.
+        This can be different from the params passed in.
     """
     if not isinstance(target, str):
         raise TypeError("require target to be str")
     if not isinstance(shape, dict):
         raise TypeError("require shape to be dict")
-
+    cfg = BuildConfig.current
     graph = graph if isinstance(graph, _graph.Graph) else _graph.create(graph)
+    shape, dtype = _update_shape_dtype(shape, dtype, params)
+    # Apply optimization
+    graph = optimize(graph, shape, dtype)
+    # Precompute prune
+    if params and cfg.opt_level >= OPT_PASS_LEVEL["PrecomputePrune"]:
+        graph, params = precompute_prune(graph, params)
+        shape, dtype = _update_shape_dtype(shape, dtype, params)
+    # Operator Fusion and generatiom
     graph = graph_attr.set_shape_inputs(graph, shape)
     graph = graph_attr.set_dtype_inputs(graph, dtype)
     graph._set_json_attr("target", target, "str")
+    graph._set_json_attr("opt_level", cfg.opt_level, "int")
     graph = graph.apply("InferShape").apply("InferType")
     graph = graph.apply("GraphFusePartition").apply("GraphFuse")
     libmod = graph_attr._move_out_module(graph, "module")
-    return graph, libmod
+    return graph, libmod, params
 
 
 def _run_graph(graph, params):
@@ -98,9 +202,9 @@ def _run_graph(graph, params):
     dtype = {k : v.dtype for k, v in params.items()}
     target = "llvm"
     ctx = tvm.cpu(0)
-    _, oshape = graph_pass.infer_shape(graph, **shape)
-    _, odtype = graph_pass.infer_dtype(graph, **dtype)
-    graph, libmod = build(graph, target, shape, dtype)
+    _, oshape = graph_util.infer_shape(graph, **shape)
+    _, odtype = graph_util.infer_dtype(graph, **dtype)
+    graph, libmod, _ = build(graph, target, shape, dtype)
     m = runtime.create(graph, libmod, ctx)
     set_input, run, get_output = m["set_input"], m["run"], m["get_output"]
     for k, v in params.items():

nnvm/python/nnvm/compiler/graph_pass.py

@@ -6,81 +6,3 @@
 - Composable API: break graph transformation pass as segments of small transformations.
 """
 from __future__ import absolute_import as _abs
-
-import tvm
-from . import graph_attr
-
-
-def infer_shape(graph, **shape):
-    """Infer the shape given the shape of inputs.
-
-    Parameters
-    ----------
-    graph : Graph
-        The graph to perform shape inference from
-
-    Returns
-    -------
-    in_shape : list of tuple
-         Shape of inputs
-
-    out_shape: list of tuple
-         Shape of outputs
-    """
-    graph = graph_attr.set_shape_inputs(graph, shape)
-    graph = graph.apply("InferShape")
-    shape = graph.json_attr("shape")
-    index = graph.index
-    input_shape = [shape[index.entry_id(x)] for x in index.input_names]
-    output_shape = [shape[index.entry_id(x)] for x in index.output_entries]
-    return input_shape, output_shape
-
-
-def infer_dtype(graph, **dtype):
-    """Infer the type given the typeS of inputs.
-
-    Parameters
-    ----------
-    graph : Graph
-        The graph to perform type inference from
-
-    Returns
-    -------
-    in_dtype : list of tuple
-         Dtype of inputs
-
-    out_dtype: list of tuple
-         Dtype of outputs
-    """
-    graph = graph_attr.set_dtype_inputs(graph, dtype)
-    graph = graph.apply("InferType")
-    dtype = graph.json_attr("dtype")
-    index = graph.index
-    input_dtype = [graph_attr.TCODE_TO_DTYPE[dtype[index.entry_id(x)]]
-                   for x in index.input_names]
-    output_dtype = [graph_attr.TCODE_TO_DTYPE[dtype[index.entry_id(x)]]
-                    for x in index.output_entries]
-    return input_dtype, output_dtype
-
-
-_deep_compare = tvm.get_global_func("nnvm.graph.DeepCompare")
-
-def check_graph_equal(grapha, graphb):
-    """Check if two graphs have equal structure.
-
-    Parameters
-    ----------
-    grapha : Graph
-        The first graph
-
-    graphb : Graph
-        The second graph
-
-    Raises
-    ------
-    ValueError
-        ValueError is raised with error message when graph not equal
-    """
-    err = _deep_compare(grapha, graphb)
-    if err:
-        raise ValueError("Graph compare error: " + err)

nnvm/python/nnvm/compiler/graph_util.py

@@ -0,0 +1,80 @@
+# pylint: disable=invalid-name
+"""Utility function to get information from graph."""
+from __future__ import absolute_import as _abs
+
+import tvm
+from . import graph_attr
+
+def infer_shape(graph, **shape):
+    """Infer the shape given the shape of inputs.
+
+    Parameters
+    ----------
+    graph : Graph
+        The graph to perform shape inference from
+
+    Returns
+    -------
+    in_shape : list of tuple
+         Shape of inputs
+
+    out_shape: list of tuple
+         Shape of outputs
+    """
+    graph = graph_attr.set_shape_inputs(graph, shape)
+    graph = graph.apply("InferShape")
+    shape = graph.json_attr("shape")
+    index = graph.index
+    input_shape = [shape[index.entry_id(x)] for x in index.input_names]
+    output_shape = [shape[index.entry_id(x)] for x in index.output_entries]
+    return input_shape, output_shape
+
+
+def infer_dtype(graph, **dtype):
+    """Infer the type given the typeS of inputs.
+
+    Parameters
+    ----------
+    graph : Graph
+        The graph to perform type inference from
+
+    Returns
+    -------
+    in_dtype : list of tuple
+         Dtype of inputs
+
+    out_dtype: list of tuple
+         Dtype of outputs
+    """
+    graph = graph_attr.set_dtype_inputs(graph, dtype)
+    graph = graph.apply("InferType")
+    dtype = graph.json_attr("dtype")
+    index = graph.index
+    input_dtype = [graph_attr.TCODE_TO_DTYPE[dtype[index.entry_id(x)]]
+                   for x in index.input_names]
+    output_dtype = [graph_attr.TCODE_TO_DTYPE[dtype[index.entry_id(x)]]
+                    for x in index.output_entries]
+    return input_dtype, output_dtype
+
+
+_deep_compare = tvm.get_global_func("nnvm.graph.DeepCompare")
+
+def check_graph_equal(grapha, graphb):
+    """Check if two graphs have equal structure.
+
+    Parameters
+    ----------
+    grapha : Graph
+        The first graph
+
+    graphb : Graph
+        The second graph
+
+    Raises
+    ------
+    ValueError
+        ValueError is raised with error message when graph not equal
+    """
+    err = _deep_compare(grapha, graphb)
+    if err:
+        raise ValueError("Graph compare error: " + err)