[TOP] split, reshape, concatenate (apache#43)

nnvm/README.md

@@ -3,7 +3,7 @@
 [![Build Status](https://travis-ci.org/dmlc/nnvm.svg?branch=master)](https://travis-ci.org/dmlc/nnvm)
 [![GitHub license](http://dmlc.github.io/img/apache2.svg)](./LICENSE)
 
-NNVM is a reusable computational graph optimization and compilation stack for deep learning systems. It provides modules to:
+NNVM is a reusable computational graph compilation stack for deep learning systems. It provides modules to:
 
 - Represent deep learning workloads from front-end frameworks via a graph IR.
 - Optimize computation graphs to improve performance.
@@ -20,26 +20,23 @@ from tvm.contrib import graph_runtime, rpc
 import nnvm.frontend
 import nnvm.compiler
 
-# get model from frameworks
+# GET model from frameworks
 # change xyz to supported framework name.
 graph, params = nnvm.frontend.from_xyz(...)
 
-# optimize and compile the graph to get a deployable module
+# OPTIMIZE and COMPILE the graph to get a deployable module
 # target can be "opencl", "llvm", "metal" or any target supported by tvm
 target = "cuda"
-graph, lib, params = nnvm.compiler.build(
-   graph, target, shape={"data", data_shape}, params=params)
+graph, lib, params = nnvm.compiler.build(graph, target, {"data", data_shape}, params=params)
 
-# deploy and run on gpu(0)
+# DEPLOY and run on gpu(0)
 module = graph_runtime.create(graph, lib, tvm.gpu(0))
 module.set_input(**params)
+module.run(data=data_array)
 output = tvm.nd.empty(out_shape, ctx=tvm.gpu(0))
-for data_array in dataset:
-    module.set_input("data", data_array)
-    module.run()
-    module.get_output(0, output)
+module.get_output(0, output)
 
-# deploy to remote mobile/rasp/browser with minimum tvm rpc runtime
+# DEPLOY to REMOTE mobile/rasp/browser with minimum tvm rpc runtime
 # useful for quick experiments on mobile devices
 remote = rpc.connect(remote_host, remote_port)
 lib.export_library("mylib.so")

nnvm/python/nnvm/compiler/param_dict.py

@@ -1,3 +1,4 @@
+# pylint: disable=invalid-name
 """Helper utility to save parameter dict"""
 import tvm
 

nnvm/python/nnvm/top/transform.py

@@ -2,13 +2,12 @@
 """Tensor transformation ops"""
 from __future__ import absolute_import
 
-import tvm
 import topi
 from .tensor import _fschedule_broadcast, _fschedule_injective
 from . import registry as reg
 from .registry import OpPattern
 
-# Need add reshape
+# expand_dims
 @reg.register_compute("expand_dims")
 def compute_expand_dims(attrs, inputs, out_info):
     """Compute definition of expand_dims"""
@@ -18,34 +17,46 @@ def compute_expand_dims(attrs, inputs, out_info):
 reg.register_pattern("expand_dims", OpPattern.BROADCAST)
 reg.register_schedule("expand_dims", _fschedule_broadcast)
 
-
+# transpose
 @reg.register_compute("transpose")
 def compute_transpose(attrs, inputs, out_info):
-    """Compute definition of expand_dims"""
+    """Compute definition of transpose"""
     axes = attrs.get_int_tuple("axes")
     axes = tuple(axes) if axes else None
     return topi.transpose(inputs[0], axes)
 reg.register_pattern("transpose", OpPattern.INJECTIVE)
 reg.register_schedule("transpose", _fschedule_injective)
 
-
-def _flatten_index(indices, shape):
-    """flatten the index to 1D"""
-    idx = 0
-    for i, value in enumerate(shape):
-        if i != 0:
-            idx *= value
-        idx = idx + indices[i]
-    return idx
-
 # reshape
 @reg.register_compute("reshape")
 def compute_reshape(attrs, inputs, out_info):
-    """Compute definition of softmax"""
-    # TODO(sxj) add support for general reshape
-    assert len(inputs[0].shape) == 1, "Only support 1d input for now"
+    """Compute definition of reshape"""
     oshape = out_info[0].shape
-    x = inputs[0]
-    return tvm.compute(oshape, lambda *i: x(_flatten_index(i, oshape)))
+    return topi.reshape(inputs[0], oshape)
 reg.register_pattern("reshape", OpPattern.INJECTIVE)
 reg.register_schedule("reshape", _fschedule_injective)
+
+# concatenate
+@reg.register_compute("concatenate")
+def compute_concatenate(attrs, inputs, out_info):
+    """Compute definition of concatenate"""
+    axis = attrs.get_int("axis")
+    return topi.concatenate([x for x in inputs], axis=axis)
+
+reg.register_pattern("concatenate", OpPattern.INJECTIVE)
+reg.register_schedule("concatenate", _fschedule_injective)
+
+# split
+@reg.register_compute("split")
+def compute_split(attrs, inputs, out_info):
+    """Compute definition of split"""
+    x = attrs["indices_or_sections"]
+    if x.startswith("(") or x.startswith("["):
+        indices_or_sections = attrs.get_int_tuple("indices_or_sections")
+    else:
+        indices_or_sections = attrs.get_int("indices_or_sections")
+    return topi.split(inputs[0], indices_or_sections, axis=attrs.get_int("axis"))
+
+
+reg.register_pattern("split", OpPattern.INJECTIVE)
+reg.register_schedule("split", _fschedule_injective)

nnvm/src/compiler/graph_runtime.h

@@ -8,6 +8,7 @@
 
 #include <nnvm/graph.h>
 #include <vector>
+#include <string>
 #include "../../tvm/src/runtime/graph/graph_runtime.h"
 
 namespace nnvm {

nnvm/src/top/tensor/transform.cc

@@ -225,15 +225,17 @@ inline bool SplitInferShape(const NodeAttrs& attrs,
     CHECK_EQ(out_shape->size(), static_cast<size_t>(num_outputs));
     CHECK_LT(param.axis, dshape.ndim());
     TShape oshape = dshape;
-    dim_t total = 0;
-    for (size_t i = 1; i < num_outputs; ++i) {
-      oshape[param.axis] = param.indices_or_sections[i - 1];
-      total += oshape[param.axis];
-      NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, i - 1, oshape);
+    dim_t begin = 0;
+    for (size_t i = 0; i < num_outputs - 1; ++i) {
+      CHECK_GT(param.indices_or_sections[i], begin)
+          << "indices_or_sections need to be a sorted ascending list";
+      oshape[param.axis] = param.indices_or_sections[i] - begin;
+      begin = param.indices_or_sections[i];
+      NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, i, oshape);
     }
-    CHECK_LT(total, dshape[param.axis])
+    CHECK_LT(begin, dshape[param.axis])
         << "The sum of sections must match the input.shape[axis]";
-    oshape[param.axis] = dshape[param.axis] - total;
+    oshape[param.axis] = dshape[param.axis] - begin;
     NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, num_outputs - 1, oshape);
   }
   return true;
@@ -256,11 +258,11 @@ NNVM_REGISTER_OP(split)
 along which to split the array.
 
 )code" NNVM_ADD_FILELINE)
-.add_argument("data", "Tensor", "List of arrays to concatenate")
+.add_argument("data", "Tensor", "Array to be splitted")
 .add_arguments(SplitParam::__FIELDS__())
 .set_attr_parser(SplitParamParser)
 .set_attr<FInferShape>("FInferShape", SplitInferShape)
-.set_attr<FInferType>("FInferType", ElemwiseType<-1, 1>)
+.set_attr<FInferType>("FInferType", ElemwiseType<1, -1>)
 .set_num_inputs(1)
 .set_num_outputs(SplitNumOutputs)
 .set_support_level(1);

nnvm/tests/python/compiler/test_top_level1.py

@@ -177,7 +177,52 @@ def test_batchnorm():
             res.asnumpy(), res_np, atol=1e-5, rtol=1e-5)
 
 
+def verify_concatenate(ishape, axis):
+    x = [sym.Variable("x%d" % i) for i in range(len(ishape))]
+    y = sym.concatenate(*x, axis=axis) + 1
+    dtype = "float32"
+    for target, ctx in ctx_list():
+        # set input
+        data = []
+        for i, shape in enumerate(ishape):
+            data.append(np.random.uniform(size=shape).astype(dtype))
+        pdict = {"x%d" % i :  v for i, v in enumerate(data)}
+        shape = {"x%d" % i :  v.shape for i, v in enumerate(data)}
+        graph, lib, _ = nnvm.compiler.build(y, target, shape)
+        m = graph_runtime.create(graph, lib, ctx)
+        m.run(**pdict)
+        out_np = np.concatenate(data, axis=axis) + 1
+        out = m.get_output(0, tvm.nd.empty(out_np.shape))
+        np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
+
+def test_concatenate():
+    verify_concatenate([(2, 3, 4), (1, 3, 4)], axis=0)
+    verify_concatenate([(2, 4), (2, 7)], axis=1)
+
+
+def verify_split(ishape, indices_or_sections, axis):
+    x = sym.Variable("x")
+    y = sym.split(x, indices_or_sections=indices_or_sections, axis=axis)
+    dtype = "float32"
+    x_np = np.random.uniform(size=ishape).astype(dtype)
+    res = np.split(x_np, indices_or_sections, axis=axis)
+    for target, ctx in ctx_list():
+        # set input
+        graph, lib, _ = nnvm.compiler.build(y, target, {"x": ishape})
+        m = graph_runtime.create(graph, lib, ctx)
+        m.run(x=x_np)
+        for i, arr  in enumerate(res):
+            out = m.get_output(i, tvm.nd.empty(arr.shape))
+            np.testing.assert_allclose(out.asnumpy(), arr, atol=1e-5, rtol=1e-5)
+
+def test_split():
+    verify_split((2, 3), 2, axis=0)
+    verify_split((5, 3), [3], axis=0)
+    verify_split((5, 9, 3), [3, 4], axis=1)
+
 if __name__ == "__main__":
+    test_split()
+    test_concatenate()
     test_log_softmax()
     test_batchnorm()
     test_dense()

nnvm/tests/python/compiler/test_top_level4.py

@@ -51,7 +51,29 @@ def test_reduce():
     verify_reduce((4, 4, 3), np.sum, sym.sum, axis=(0, 2))
 
 
+def verify_reshape(dshape, oshape):
+    x = sym.Variable("x")
+    y = sym.reshape(x, shape=oshape)
+    y = y + 1
+    dtype = "float32"
+    for target, ctx in ctx_list():
+        graph, lib, _ = nnvm.compiler.build(y, target, {"x": dshape})
+        m = graph_runtime.create(graph, lib, ctx)
+        # set input
+        data = tvm.nd.array(np.random.uniform(size=dshape).astype(dtype))
+        m.run(x=data)
+        out_np = data.asnumpy().reshape(oshape) + 1
+        out = m.get_output(0, tvm.nd.empty(out_np.shape))
+        np.testing.assert_allclose(out.asnumpy(), out_np, atol=1e-5, rtol=1e-5)
+
+def test_reshape():
+    verify_reshape((2, 3, 4), (-1, 2, 1))
+    verify_reshape((2, 3, 4), (8, 3))
+    verify_reshape((4, 7), (2, 7, 2))
+
+
 if __name__ == "__main__":
+    test_reshape()
     test_reduce()
     test_tranpose()
     print(nnvm.compiler.engine.dump())