[RUNTIME][SDACCEL] Add support for multiple kernels (#1424)

python/tvm/contrib/sdaccel.py

@@ -6,16 +6,14 @@ from ..api import register_func
 
 
 @register_func("tvm_callback_sdaccel_compile")
-def compile_vhls(code, kernel):
+def compile_vhls(kernel_info):
     """Compile Vivado HLS code for SDAccel.
 
     Parameters
     ----------
-    code : str
-        The Vivado HLS code.
-
-    kernel : str
-        The kernel to compile or link.
+    kernel_info : list of (str, str)
+        List of kernel information.  The kernel information is a tuple of
+        function name and source code.
 
     Return
     ------
@@ -23,12 +21,6 @@ def compile_vhls(code, kernel):
         The bytearray of the xclbin
     """
     tmp_dir = util.tempdir()
-    tmp_cpp = tmp_dir.relpath("input.cpp")
-    tmp_xo = tmp_dir.relpath("output.xo")
-    tmp_xclbin = tmp_dir.relpath("output.xclbin")
-
-    with open(tmp_cpp, "wb") as out_file:
-        out_file.write(bytes(code))
 
     sdk = os.environ.get("XILINX_SDX", None)
     xocc = os.path.join(sdk, "bin/xocc") if sdk else "xocc"
@@ -41,15 +33,29 @@ def compile_vhls(code, kernel):
     if platform is None:
         raise RuntimeError("No Xlinx device specified.")
 
+    tmp_xo_files = []
+    for funcname, code  in kernel_info:
+        funcname = funcname.value
+        code = code.value
+
+        tmp_cpp = tmp_dir.relpath(funcname + ".cpp")
+        tmp_xo = tmp_dir.relpath(funcname + ".xo")
+
+        with open(tmp_cpp, "wb") as out_file:
+            out_file.write(bytes(code))
+
         # build xo
-    args = [xocc, "-c", "-t", target, "--platform", platform, "-o", tmp_xo, "-k", kernel] + \
+        args = [xocc, "-c", "-t", target, "--platform", platform, "-o", tmp_xo, "-k", funcname] + \
                advanced_params + [tmp_cpp]
         returncode = subprocess.call(args)
         if returncode != 0:
             raise RuntimeError("Compile error")
 
+        tmp_xo_files.append(tmp_xo)
+
     # build xclbin
-    args = [xocc, "-l", "-t", target, "--platform", platform, "-o", tmp_xclbin, tmp_xo] + \
+    tmp_xclbin = tmp_dir.relpath("output.xclbin")
+    args = [xocc, "-l", "-t", target, "--platform", platform, "-o", tmp_xclbin] + tmp_xo_files + \
            advanced_params
     returncode = subprocess.call(args)
     if returncode != 0:

src/codegen/codegen_vhls.cc

@@ -72,26 +72,33 @@ runtime::Module BuildSDAccel(Array<LoweredFunc> funcs) {
   bool output_ssa = false;
   CodeGenVivadoHLS cg;
 
-  CHECK_EQ(funcs.size(), 1);
-  const std::string funcname = funcs[0]->name;
-
+  // Generate source code for get_source().
   cg.Init(output_ssa);
-
   for (LoweredFunc f : funcs) {
     cg.AddFunction(f);
   }
+  std::string whole_code = cg.Finish();
+
+  // Generate source code for compilation.
+  Array<Array<Expr> > kernel_info;
+  for (LoweredFunc f : funcs) {
+    CodeGenVivadoHLS cg;
+    cg.Init(output_ssa);
+    cg.AddFunction(f);
     std::string code = cg.Finish();
     if (const auto* f = runtime::Registry::Get("tvm_callback_vhls_postproc")) {
       code = (*f)(code).operator std::string();
     }
+    kernel_info.push_back(Array<Expr>({f->name, code}));
+  }
 
   std::string xclbin;
   if (const auto* f = Registry::Get("tvm_callback_sdaccel_compile")) {
-    xclbin = (*f)(code, funcname).operator std::string();
+    xclbin = (*f)(kernel_info).operator std::string();
   } else {
     LOG(FATAL) << "Cannot compile Vivado HLS code.";
   }
-  return SDAccelModuleCreate(xclbin, "xclbin", ExtractFuncInfo(funcs), code);
+  return SDAccelModuleCreate(xclbin, "xclbin", ExtractFuncInfo(funcs), whole_code);
 }
 
 TVM_REGISTER_API("codegen.build_sdaccel")

tests/python/integration/test_ewise_fpga.py

@@ -21,7 +21,7 @@ def test_exp():
     s[B].bind(px, tvm.thread_axis("pipeline"))
 
     # one line to build the function.
-    def check_device(device, host="stackvm"):
+    def check_device(device, host="llvm"):
         if not tvm.module.enabled(host):
             return
         ctx = tvm.context(device, 0)
@@ -42,5 +42,44 @@ def test_exp():
     check_device("sdaccel")
 
 
+def test_multi_kernel():
+    # graph
+    n = tvm.convert(1024)
+    A = tvm.placeholder((n,), name='A')
+    B = tvm.placeholder((n,), name='B')
+    C = tvm.compute(A.shape, lambda *i: A(*i) + B(*i), name='C')
+    D = tvm.compute(A.shape, lambda *i: A(*i) + C(*i), name='D')
+    s = tvm.create_schedule(D.op)
+    # create iter var and assign them tags.
+    px, x = s[C].split(C.op.axis[0], nparts=1)
+    s[C].bind(px, tvm.thread_axis("pipeline"))
+    px, x = s[D].split(D.op.axis[0], nparts=1)
+    s[D].bind(px, tvm.thread_axis("pipeline"))
+
+    # one line to build the function.
+    def check_device(device, host="llvm"):
+        if not tvm.module.enabled(host):
+            return
+        ctx = tvm.context(device, 0)
+        if not ctx.exist:
+            return
+        fadd = tvm.build(s, [A, B, C, D],
+                         device, host,
+                         name="myadd")
+        ctx = tvm.context(device, 0)
+        # launch the kernel.
+        n = 1024
+        a = tvm.nd.array(np.random.uniform(size=n).astype(A.dtype), ctx)
+        b = tvm.nd.array(np.random.uniform(size=n).astype(B.dtype), ctx)
+        c = tvm.nd.array(np.random.uniform(size=n).astype(C.dtype), ctx)
+        d = tvm.nd.array(np.random.uniform(size=n).astype(D.dtype), ctx)
+        fadd(a, b, c, d)
+        np.testing.assert_allclose(
+            d.asnumpy(), a.asnumpy() * 2 + b.asnumpy(), rtol=1e-5)
+
+    check_device("sdaccel")
+
+
 if __name__ == "__main__":
     test_exp()
+    test_multi_kernel()