[ASPLOS] Major update to section-4, especially 4c

aie_kernels/aie2/scale.cc

@@ -19,6 +19,7 @@
 
 #include <aie_api/aie.hpp>
 
+// Scalar scale template
 template <typename T>
 void scale_scalar(T *a, T *c, T factor, const int32_t N) {
   event0();
@@ -28,35 +29,70 @@ void scale_scalar(T *a, T *c, T factor, const int32_t N) {
   event1();
 }
 
+// Vectorized scale template
 // Assume N is multiple of 16
 template <typename T>
-void scale_vectorized(T *a, T *c, T factor, const int32_t N) {
-  constexpr int vec_factor = 16;
+void scale_vectorized(T *a, T *c, int32_t factor, const int32_t N) {
   event0();
+  constexpr int vec_factor = 32;
   T *__restrict pA1 = a;
   T *__restrict pC1 = c;
   const int F = N / vec_factor;
+  T fac = factor;
   for (int i = 0; i < F; i++)
     chess_prepare_for_pipelining chess_loop_range(16, ) {
       aie::vector<T, vec_factor> A0 = aie::load_v<vec_factor>(pA1);
       pA1 += vec_factor;
+      aie::accum<acc32, vec_factor> cout = aie::mul(A0, fac);
+      aie::store_v(pC1, cout.template to_vector<T>(0));
+      pC1 += vec_factor;
+    }
+  event1();
+}
+
+// Vectorized scale tempalte for int32_t (acc64 used)
+// Assume N is multiple of 16
+template <>
+void scale_vectorized<int32_t>(int32_t *a, int32_t *c, int32_t factor,
+                               const int32_t N) {
+  event0();
+  constexpr int vec_factor = 32;
+  int32_t *__restrict pA1 = a;
+  int32_t *__restrict pC1 = c;
+  const int F = N / vec_factor;
+  for (int i = 0; i < F; i++)
+    chess_prepare_for_pipelining chess_loop_range(16, ) {
+      aie::vector<int32_t, vec_factor> A0 = aie::load_v<vec_factor>(pA1);
+      pA1 += vec_factor;
       aie::accum<acc64, vec_factor> cout = aie::mul(A0, factor);
-      aie::store_v(pC1, cout.to_vector<T>(0));
+      aie::store_v(pC1, cout.template to_vector<int32_t>(0));
       pC1 += vec_factor;
     }
   event1();
 }
 
 extern "C" {
 
-void vector_scalar_mul_aie(int32_t *a_in, int32_t *c_out, int32_t *factor,
-                           int32_t N) {
+// 16-bit datatype
+void vector_scalar_mul_int32_scalar(int32_t *a_in, int32_t *c_out,
+                                    int32_t *factor, int32_t N) {
+  scale_scalar<int32_t>(a_in, c_out, *factor, N);
+}
+
+void vector_scalar_mul_int32_vector(int32_t *a_in, int32_t *c_out,
+                                    int32_t *factor, int32_t N) {
   scale_vectorized<int32_t>(a_in, c_out, *factor, N);
 }
 
-void vector_scalar_mul_aie_scalar(int32_t *a_in, int32_t *c_out,
-                                  int32_t *factor, int32_t N) {
-  scale_scalar<int32_t>(a_in, c_out, *factor, N);
+// 32-bit datatype
+void vector_scalar_mul_int16_scalar(int16_t *a_in, int16_t *c_out,
+                                    int32_t *factor, int32_t N) {
+  scale_scalar<int16_t>(a_in, c_out, *factor, N);
+}
+
+void vector_scalar_mul_int16_vector(int16_t *a_in, int16_t *c_out,
+                                    int32_t *factor, int32_t N) {
+  scale_vectorized<int16_t>(a_in, c_out, *factor, N);
 }
 
 } // extern "C"

programming_examples/basic/matrix_multiplication/makefile-common

@@ -38,6 +38,8 @@ M?=512
 K?=512
 N?=512
 
+trace_size=16384
+
 mlir_target?=build/aie_${M}x${K}x${N}.mlir
 xclbin_target?=build/final_${M}x${K}x${N}.xclbin
 insts_target?=build/insts_${M}x${K}x${N}.txt
@@ -83,14 +85,19 @@ run: ${targetname}.exe ${xclbin_target} ${insts_target} #sign
 	export XRT_HACK_UNSECURE_LOADING_XCLBIN=1 && \
 	${powershell} ./$< -x ${xclbin_target} -i ${insts_target} -k MLIR_AIE -M $M -K $K -N $N ${runargs}
 
-.PHONY: clean
-clean:
-	rm -rf build _build ${targetname}.exe
+trace: ${targetname}.exe ${xclbin_target} ${insts_target} # sign
+	export XRT_HACK_UNSECURE_LOADING_XCLBIN=1 && \
+	${powershell} ./$< -x ${xclbin_target} -i ${insts_target} -k MLIR_AIE -M $M -K $K -N $N -v 1 --warmup 0 --iters 1 -t ${trace_size}
+	../../../utils/parse_trace.py --filename trace.txt --mlir ${mlir_target} --colshift 1 > parse_trace_mm.json
 
 .PHONY: parse_trace
 parse_trace:
-	../../../utils/parse_eventIR.py --filename trace.txt --mlir ./build/aie.mlir --colshift 1 > trace_eventIR.json
+	../../../utils/parse_trace.py --filename trace.txt --mlir ${mlir_target} --colshift 1 > parse_trace_mm.json
+
+.PHONY: clean
+clean: clean_trace
+	rm -rf build _build ${targetname}.exe
 
 .PHONY: clean_trace
 clean_trace:
-	rm -rf tmpTrace trace_eventIR.json
+	rm -rf tmpTrace parse*.json 

programming_examples/basic/vector_scalar_add/aie2.py

@@ -13,63 +13,67 @@
 
 
 def my_vector_bias_add():
-    with mlir_mod_ctx() as ctx:
 
-        @device(AIEDevice.ipu)
-        def device_body():
-            memRef_16_ty = T.memref(16, T.i32())
-            memRef_8_ty = T.memref(8, T.i32())
+    @device(AIEDevice.ipu)
+    def device_body():
+        memRef_16_ty = T.memref(16, T.i32())
+        memRef_8_ty = T.memref(8, T.i32())
 
-            # Tile declarations
-            ShimTile = tile(0, 0)
-            MemTile = tile(0, 1)
-            ComputeTile2 = tile(0, 2)
+        # Tile declarations
+        ShimTile = tile(0, 0)
+        MemTile = tile(0, 1)
+        ComputeTile2 = tile(0, 2)
 
-            # AIE-array data movement with object fifos
-            # Input
-            of_in0 = object_fifo("in0", ShimTile, MemTile, 2, memRef_16_ty)
-            of_in1 = object_fifo("in1", MemTile, ComputeTile2, 2, memRef_8_ty)
-            object_fifo_link(of_in0, of_in1)
+        # AIE-array data movement with object fifos
+        # Input
+        of_in0 = object_fifo("in0", ShimTile, MemTile, 2, memRef_16_ty)
+        of_in1 = object_fifo("in1", MemTile, ComputeTile2, 2, memRef_8_ty)
+        object_fifo_link(of_in0, of_in1)
 
-            # Output
-            of_out0 = object_fifo("out0", MemTile, ShimTile, 2, memRef_16_ty)
-            of_out1 = object_fifo("out1", ComputeTile2, MemTile, 2, memRef_8_ty)
-            object_fifo_link(of_out1, of_out0)
+        # Output
+        of_out0 = object_fifo("out0", MemTile, ShimTile, 2, memRef_16_ty)
+        of_out1 = object_fifo("out1", ComputeTile2, MemTile, 2, memRef_8_ty)
+        object_fifo_link(of_out1, of_out0)
 
-            # Set up compute tiles
+        # Set up compute tiles
 
-            # Compute tile 2
-            @core(ComputeTile2)
-            def core_body():
-                # Effective while(1)
-                for _ in for_(8):
-                    elem_in = of_in1.acquire(ObjectFifoPort.Consume, 1)
-                    elem_out = of_out1.acquire(ObjectFifoPort.Produce, 1)
-                    for i in for_(8):
-                        v0 = memref.load(elem_in, [i])
-                        v1 = arith.addi(v0, arith.constant(1, T.i32()))
-                        memref.store(v1, elem_out, [i])
-                        yield_([])
-                    of_in1.release(ObjectFifoPort.Consume, 1)
-                    of_out1.release(ObjectFifoPort.Produce, 1)
+        # Compute tile 2
+        @core(ComputeTile2)
+        def core_body():
+            # Effective while(1)
+            for _ in for_(8):
+                elem_in = of_in1.acquire(ObjectFifoPort.Consume, 1)
+                elem_out = of_out1.acquire(ObjectFifoPort.Produce, 1)
+                for i in for_(8):
+                    v0 = memref.load(elem_in, [i])
+                    v1 = arith.addi(v0, arith.constant(1, T.i32()))
+                    memref.store(v1, elem_out, [i])
                     yield_([])
+                of_in1.release(ObjectFifoPort.Consume, 1)
+                of_out1.release(ObjectFifoPort.Produce, 1)
+                yield_([])
 
-            # To/from AIE-array data movement
+        # To/from AIE-array data movement
 
-            memRef_64_ty = T.memref(64, T.i32())
-            memRef_32_ty = T.memref(32, T.i32())
+        memRef_64_ty = T.memref(64, T.i32())
+        memRef_32_ty = T.memref(32, T.i32())
 
-            @FuncOp.from_py_func(memRef_64_ty, memRef_32_ty, memRef_64_ty)
-            def sequence(inTensor, notUsed, outTensor):
-                ipu_dma_memcpy_nd(
-                    metadata="out0", bd_id=0, mem=outTensor, sizes=[1, 1, 1, 64]
-                )
-                ipu_dma_memcpy_nd(
-                    metadata="in0", bd_id=1, mem=inTensor, sizes=[1, 1, 1, 64]
-                )
-                ipu_sync(column=0, row=0, direction=0, channel=0)
+        @FuncOp.from_py_func(memRef_64_ty, memRef_32_ty, memRef_64_ty)
+        def sequence(inTensor, notUsed, outTensor):
+            ipu_dma_memcpy_nd(
+                metadata="out0", bd_id=0, mem=outTensor, sizes=[1, 1, 1, 64]
+            )
+            ipu_dma_memcpy_nd(
+                metadata="in0", bd_id=1, mem=inTensor, sizes=[1, 1, 1, 64]
+            )
+            ipu_sync(column=0, row=0, direction=0, channel=0)
 
-    print(ctx.module)
 
-
-my_vector_bias_add()
+# Declares that subsequent code is in mlir-aie context
+with mlir_mod_ctx() as ctx:
+    my_vector_bias_add()
+    res = ctx.module.operation.verify()
+    if res == True:
+        print(ctx.module)
+    else:
+        print(res)

programming_examples/basic/vector_scalar_mul/Makefile

@@ -36,7 +36,7 @@ build/final_${data_size}.xclbin: build/aie_${data_size}.mlir build/scale.o
 	cd ${@D} && aiecc.py --aie-generate-cdo --no-compile-host --xclbin-name=${@F} \
 				--aie-generate-ipu --ipu-insts-name=insts_${data_size}.txt $(<:%=../%)
 
-build/final_trace_${data_size}.xclbin: build/aie_trace.mlir build/scale.o
+build/final_trace_${data_size}.xclbin: build/aie_trace_${data_size}.mlir build/scale.o
 	mkdir -p ${@D}
 	cd ${@D} && aiecc.py --aie-generate-cdo --no-compile-host --xclbin-name=${@F} \
 				--aie-generate-ipu --ipu-insts-name=insts_${data_size}.txt $(<:%=../%)
@@ -60,16 +60,16 @@ run_py: build/final_${data_size}.xclbin build/insts_${data_size}.txt
 
 trace: ${targetname}_${data_size}.exe build/final_trace_${data_size}.xclbin build/insts_${data_size}.txt 
 	${powershell} ./$< -x build/final_trace_${data_size}.xclbin -i build/insts_${data_size}.txt -k MLIR_AIE -t ${trace_size}
-	../../utils/parse_eventIR.py --filename trace.txt --mlir build/aie_trace__${data_size}.mlir --colshift 1 > parse_eventIR_vs.json
+	../../utils/parse_trace.py --filename trace.txt --mlir build/aie_trace_${data_size}.mlir --colshift 1 > trace_vs.json
 
 trace_py: build/final_trace_${data_size}.xclbin build/insts_${data_size}.txt
 	${powershell} python3 test.py -x build/final_trace_${data_size}.xclbin -i build/insts_${data_size}.txt -k MLIR_AIE -t ${trace_size} -s ${data_size}
-	../../utils/parse_eventIR.py --filename trace.txt --mlir build/aie_trace_${data_size}.mlir --colshift 1 > parse_eventIR_vs.json
+	../../utils/parse_trace.py --filename trace.txt --mlir build/aie_trace_${data_size}.mlir --colshift 1 > trace_vs.json
 
 
 clean_trace:
-	rm -rf tmpTrace trace.txt
+	rm -rf tmpTrace trace.txt parse*json trace*json
 
 clean: clean_trace
-	rm -rf build _build ${targetname}_*.exe
+	rm -rf build _build ${targetname}*.exe
 

programming_examples/basic/vector_scalar_mul/aie2.py

@@ -16,8 +16,10 @@
 
 
 def my_vector_scalar(vector_size, trace_size):
+    word_size_in = 2
     N = vector_size
-    N_in_bytes = N * 4
+    N_in_i32s = N * word_size_in // 4
+    N_in_bytes = N_in_i32s * 4
     N_div_n = 4  # chop input vector into 4 sub-vectors
     n = N // N_div_n
 
@@ -27,17 +29,18 @@ def my_vector_scalar(vector_size, trace_size):
 
     @device(AIEDevice.ipu)
     def device_body():
-        memRef_ty = T.memref(n, T.i32())
+        memRef_ty = T.memref(n, T.i16())
         memRef_ty2 = T.memref(1, T.i32())
 
         # AIE Core Function declarations
 
         scale_scalar = external_func(
-            "vector_scalar_mul_aie_scalar",
+            "vector_scalar_mul_int16_scalar",
             inputs=[memRef_ty, memRef_ty, memRef_ty2, T.i32()],
         )
         scale = external_func(
-            "vector_scalar_mul_aie", inputs=[memRef_ty, memRef_ty, memRef_ty2, T.i32()]
+            "vector_scalar_mul_int16_vector",
+            inputs=[memRef_ty, memRef_ty, memRef_ty2, T.i32()],
         )
 
         # Tile declarations
@@ -78,7 +81,7 @@ def core_body():
                 yield_([])
 
         # To/from AIE-array data movement
-        tensor_ty = T.memref(N, T.i32())
+        tensor_ty = T.memref(N_in_i32s, T.i32())
         scalar_ty = T.memref(1, T.i32())
 
         @FuncOp.from_py_func(tensor_ty, scalar_ty, tensor_ty)
@@ -92,8 +95,10 @@ def sequence(A, F, C):
                     size=trace_size,
                     offset=N_in_bytes,
                 )
-            ipu_dma_memcpy_nd(metadata="out", bd_id=0, mem=C, sizes=[1, 1, 1, N])
-            ipu_dma_memcpy_nd(metadata="in", bd_id=1, mem=A, sizes=[1, 1, 1, N])
+            ipu_dma_memcpy_nd(
+                metadata="out", bd_id=0, mem=C, sizes=[1, 1, 1, N_in_i32s]
+            )
+            ipu_dma_memcpy_nd(metadata="in", bd_id=1, mem=A, sizes=[1, 1, 1, N_in_i32s])
             ipu_dma_memcpy_nd(metadata="infactor", bd_id=2, mem=F, sizes=[1, 1, 1, 1])
             ipu_sync(column=0, row=0, direction=0, channel=0)
 

programming_examples/basic/vector_scalar_mul/test.cpp

@@ -22,7 +22,8 @@
 // Configure this to match your buffer data type
 // ------------------------------------------------------
 // using DATATYPE = std::uint8_t;
-using DATATYPE = std::uint32_t;
+// using DATATYPE = std::uint32_t;
+using DATATYPE = std::uint16_t;
 #endif
 
 const int scaleFactor = 3;
@@ -67,7 +68,7 @@ int main(int argc, const char *argv[]) {
                           XCL_BO_FLAGS_CACHEABLE, kernel.group_id(0));
   auto bo_inA =
       xrt::bo(device, IN_SIZE, XRT_BO_FLAGS_HOST_ONLY, kernel.group_id(2));
-  auto bo_inFactor = xrt::bo(device, 1 * sizeof(DATATYPE),
+  auto bo_inFactor = xrt::bo(device, 1 * sizeof(int32_t),
                              XRT_BO_FLAGS_HOST_ONLY, kernel.group_id(3));
   auto bo_outC =
       xrt::bo(device, OUT_SIZE, XRT_BO_FLAGS_HOST_ONLY, kernel.group_id(4));
@@ -85,8 +86,8 @@ int main(int argc, const char *argv[]) {
     bufInA[i] = i + 1;
 
   // Initialize buffer bo_inFactor
-  DATATYPE *bufInFactor = bo_inFactor.map<DATATYPE *>();
-  *bufInFactor = scaleFactor;
+  int32_t *bufInFactor = bo_inFactor.map<int32_t *>();
+  *bufInFactor = (DATATYPE)scaleFactor;
 
   // Zero out buffer bo_outC
   DATATYPE *bufOut = bo_outC.map<DATATYPE *>();

programming_examples/basic/vector_scalar_mul/test.py

@@ -33,9 +33,9 @@ def main(opts):
     INOUT1_VOLUME = int(1)  # Input only, 1 uint32_t scale factor
     INOUT2_VOLUME = int(opts.size)  # Output only, 64x uint32_t in this example
 
-    INOUT0_DATATYPE = np.int32
+    INOUT0_DATATYPE = np.int16
     INOUT1_DATATYPE = np.int32
-    INOUT2_DATATYPE = np.int32
+    INOUT2_DATATYPE = np.int16
 
     INOUT0_SIZE = INOUT0_VOLUME * INOUT0_DATATYPE().itemsize
     INOUT1_SIZE = INOUT1_VOLUME * INOUT1_DATATYPE().itemsize
@@ -90,7 +90,7 @@ def main(opts):
     bo_inout2.sync(xrt.xclBOSyncDirection.XCL_BO_SYNC_BO_FROM_DEVICE)
 
     # Copy output results and verify they are correct
-    entire_buffer = bo_inout2.read(OUT_SIZE, 0).view(np.uint32)
+    entire_buffer = bo_inout2.read(OUT_SIZE, 0).view(np.uint16)
     output_buffer = entire_buffer[:INOUT2_VOLUME]
     if opts.verify:
         if opts.verbosity >= 1: