Compare commits
3 Commits
| Author | SHA1 | Date | |
|---|---|---|---|
| 8d95c604a6 | |||
| 55eda487dc | |||
| 061139aefb |
@@ -1,7 +1,6 @@
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#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
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#include "mlir/IR/BuiltinTypeInterfaces.h"
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#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
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#include "src/Accelerators/PIM/Common/PimCommon.hpp"
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using namespace mlir;
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@@ -1,8 +1,7 @@
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#include "src/Accelerators/PIM/Common/Support/ReportUtils.hpp"
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#include "llvm/Support/Format.h"
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#include "src/Accelerators/PIM/Common/Support/FileSystemUtils.hpp"
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#include "src/Accelerators/PIM/Common/Support/ReportUtils.hpp"
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namespace onnx_mlir {
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@@ -1,10 +1,9 @@
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#pragma once
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#include "llvm/ADT/STLExtras.h"
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#include "llvm/ADT/ArrayRef.h"
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#include "llvm/ADT/STLExtras.h"
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#include "llvm/Support/raw_ostream.h"
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#include <cstdint>
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#include <fstream>
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#include <limits>
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#include <string>
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@@ -70,9 +70,7 @@ inline void writeUint32LE(llvm::raw_ostream& os, uint32_t value) {
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os.write(bytes.data(), bytes.size());
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}
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inline void writeInt32LE(llvm::raw_ostream& os, int32_t value) {
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writeUint32LE(os, static_cast<uint32_t>(value));
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}
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inline void writeInt32LE(llvm::raw_ostream& os, int32_t value) { writeUint32LE(os, static_cast<uint32_t>(value)); }
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inline void writeHeader(llvm::raw_ostream& os) {
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os.write(kMagic, sizeof(kMagic));
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@@ -186,39 +184,39 @@ inline Opcode opcodeFromString(llvm::StringRef opName) {
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inline llvm::StringRef opcodeToString(Opcode opcode) {
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switch (opcode) {
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case Opcode::nop: return "nop";
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case Opcode::sldi: return "sldi";
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case Opcode::sld: return "sld";
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case Opcode::sadd: return "sadd";
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case Opcode::ssub: return "ssub";
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case Opcode::smul: return "smul";
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case Opcode::saddi: return "saddi";
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case Opcode::smuli: return "smuli";
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case Opcode::setbw: return "setbw";
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case Opcode::mvmul: return "mvmul";
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case Opcode::vvadd: return "vvadd";
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case Opcode::vvsub: return "vvsub";
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case Opcode::vvmul: return "vvmul";
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case Opcode::vvdmul: return "vvdmul";
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case Opcode::vvmax: return "vvmax";
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case Opcode::vvsll: return "vvsll";
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case Opcode::vvsra: return "vvsra";
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case Opcode::vavg: return "vavg";
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case Opcode::vrelu: return "vrelu";
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case Opcode::vtanh: return "vtanh";
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case Opcode::vsigm: return "vsigm";
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case Opcode::nop: return "nop";
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case Opcode::sldi: return "sldi";
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case Opcode::sld: return "sld";
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case Opcode::sadd: return "sadd";
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case Opcode::ssub: return "ssub";
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case Opcode::smul: return "smul";
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case Opcode::saddi: return "saddi";
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case Opcode::smuli: return "smuli";
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case Opcode::setbw: return "setbw";
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case Opcode::mvmul: return "mvmul";
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case Opcode::vvadd: return "vvadd";
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case Opcode::vvsub: return "vvsub";
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case Opcode::vvmul: return "vvmul";
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case Opcode::vvdmul: return "vvdmul";
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case Opcode::vvmax: return "vvmax";
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case Opcode::vvsll: return "vvsll";
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case Opcode::vvsra: return "vvsra";
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case Opcode::vavg: return "vavg";
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case Opcode::vrelu: return "vrelu";
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case Opcode::vtanh: return "vtanh";
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case Opcode::vsigm: return "vsigm";
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case Opcode::vsoftmax: return "vsoftmax";
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case Opcode::vmv: return "vmv";
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case Opcode::vrsu: return "vrsu";
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case Opcode::vrsl: return "vrsl";
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case Opcode::ld: return "ld";
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case Opcode::st: return "st";
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case Opcode::lldi: return "lldi";
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case Opcode::lmv: return "lmv";
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case Opcode::send: return "send";
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case Opcode::recv: return "recv";
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case Opcode::wait: return "wait";
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case Opcode::sync: return "sync";
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case Opcode::vmv: return "vmv";
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case Opcode::vrsu: return "vrsu";
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case Opcode::vrsl: return "vrsl";
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case Opcode::ld: return "ld";
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case Opcode::st: return "st";
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case Opcode::lldi: return "lldi";
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case Opcode::lmv: return "lmv";
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case Opcode::send: return "send";
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case Opcode::recv: return "recv";
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case Opcode::wait: return "wait";
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case Opcode::sync: return "sync";
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}
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llvm_unreachable("Unsupported PIM binary opcode");
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}
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@@ -235,9 +233,7 @@ inline InstructionRecord makeInstructionRecord(const llvm::json::Object& instruc
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case Opcode::sldi:
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case Opcode::saddi:
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case Opcode::smuli:
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case Opcode::lldi:
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record.r2OrImm = getOptionalInt(instruction, "imm");
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break;
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case Opcode::lldi: record.r2OrImm = getOptionalInt(instruction, "imm"); break;
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case Opcode::mvmul:
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record.r2OrImm = getOptionalInt(instruction, "mbiw");
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record.generic1 = getOptionalInt(instruction, "relu");
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@@ -252,9 +248,7 @@ inline InstructionRecord makeInstructionRecord(const llvm::json::Object& instruc
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record.r2OrImm = getOptionalInt(instruction, "core");
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record.generic3 = getOptionalInt(instruction, "size");
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break;
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default:
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record.r2OrImm = getOptionalInt(instruction, "rs2");
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break;
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default: record.r2OrImm = getOptionalInt(instruction, "rs2"); break;
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}
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if (record.opcode != Opcode::mvmul && record.opcode != Opcode::setbw) {
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@@ -371,8 +365,7 @@ inline llvm::json::Object makeInstructionJson(const InstructionRecord& record) {
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break;
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case Opcode::wait:
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case Opcode::sync:
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case Opcode::nop:
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break;
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case Opcode::nop: break;
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}
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return instruction;
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@@ -367,7 +367,7 @@ void PimCodeGen::emitMemCopyOp(StringRef opName,
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instruction.generic1 = 0;
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instruction.generic2 = 0;
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instruction.generic3 = static_cast<int32_t>(size);
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(void)sizeFieldName;
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(void) sizeFieldName;
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emitInstruction(instruction);
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}
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@@ -1,5 +1,4 @@
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#include "src/Accelerators/PIM/Conversion/SpatialToPim/TensorPackingPatterns.hpp"
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#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
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using namespace mlir;
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@@ -75,16 +74,14 @@ struct PackSpatialConcatInputsPattern final : OpRewritePattern<spatial::SpatConc
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return failure();
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auto outputType = cast<ShapedType>(concatOp.getOutput().getType());
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auto newConcat = pim::PimConcatOp::create(rewriter,
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concatOp.getLoc(),
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concatOp.getOutput().getType(),
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concatOp.getAxisAttr(),
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ValueRange(packedInputs),
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tensor::EmptyOp::create(rewriter,
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concatOp.getLoc(),
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outputType.getShape(),
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outputType.getElementType())
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.getResult());
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auto newConcat = pim::PimConcatOp::create(
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rewriter,
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concatOp.getLoc(),
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concatOp.getOutput().getType(),
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concatOp.getAxisAttr(),
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ValueRange(packedInputs),
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tensor::EmptyOp::create(rewriter, concatOp.getLoc(), outputType.getShape(), outputType.getElementType())
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.getResult());
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rewriter.replaceOp(concatOp, newConcat.getOutput());
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return success();
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}
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@@ -1,7 +1,7 @@
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#pragma once
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/Dialect/Func/IR/FuncOps.h"
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#include "mlir/Dialect/Tensor/IR/Tensor.h"
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#include "mlir/IR/PatternMatch.h"
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#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
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@@ -1,15 +1,15 @@
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#include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/Interfaces/DestinationStyleOpInterface.h"
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#include "llvm/ADT/DenseMap.h"
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#include "llvm/ADT/STLExtras.h"
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#include "llvm/ADT/SmallPtrSet.h"
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#include "llvm/ADT/SmallVector.h"
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#include "llvm/ADT/STLExtras.h"
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#include <limits>
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#include "src/Accelerators/PIM/Dialect/Pim/Transforms/StaticMemoryCoalescing/StaticMemoryCoalescing.hpp"
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using namespace mlir;
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namespace onnx_mlir {
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@@ -29,9 +29,8 @@ static uint64_t getTypeSizeBytes(MemRefType type) {
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return static_cast<uint64_t>(type.getNumElements() * type.getElementTypeBitWidth() / 8);
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}
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static FailureOr<uint64_t> getLastUseInstruction(memref::AllocOp allocOp,
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Block& body,
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const DenseMap<Operation*, uint64_t>& opOrder) {
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static FailureOr<uint64_t>
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getLastUseInstruction(memref::AllocOp allocOp, Block& body, const DenseMap<Operation*, uint64_t>& opOrder) {
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uint64_t endInstruction = opOrder.lookup(allocOp);
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SmallPtrSet<Operation*, 16> visited;
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SmallVector<Value> pendingValues;
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@@ -45,10 +44,9 @@ static FailureOr<uint64_t> getLastUseInstruction(memref::AllocOp allocOp,
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if (!visited.insert(user).second)
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continue;
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if (isSupportedAliasOp(user)) {
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if (isSupportedAliasOp(user))
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for (Value result : user->getResults())
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pendingValues.push_back(result);
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}
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if (auto dpsOp = dyn_cast<DestinationStyleOpInterface>(user)) {
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for (OpResult result : user->getResults()) {
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@@ -2,7 +2,6 @@
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#include "mlir/Dialect/MemRef/IR/MemRef.h"
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#include "mlir/IR/PatternMatch.h"
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#include "mlir/IR/Operation.h"
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#include "llvm/ADT/SmallVector.h"
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+20
-19
@@ -45,9 +45,7 @@ struct CoalescingReportEntry {
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CoalescingReportRow row;
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};
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static std::string formatMemory(uint64_t bytes) {
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return formatReportMemory(bytes);
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}
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static std::string formatMemory(uint64_t bytes) { return formatReportMemory(bytes); }
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static SmallVector<int32_t> getBatchCoreIds(pim::PimCoreBatchOp coreBatchOp) {
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auto coreIdsAttr = coreBatchOp->getAttrOfType<DenseI32ArrayAttr>(onnx_mlir::kCoreIdsAttrName);
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@@ -58,9 +56,10 @@ static SmallVector<int32_t> getBatchCoreIds(pim::PimCoreBatchOp coreBatchOp) {
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static void printReportRow(raw_ostream& os, const CoalescingReportRow& row) {
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llvm::SmallVector<ReportField, 4> fields = {
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{"Number of candidates", std::to_string(row.numCandidates)},
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{"Skipped allocations", std::to_string(row.numSkipped)},
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{"Removed allocations", std::to_string(row.numRemoved)},
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{"Saved memory", formatMemory(row.savedBytes)}};
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{"Skipped allocations", std::to_string(row.numSkipped) },
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{"Removed allocations", std::to_string(row.numRemoved) },
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{"Saved memory", formatMemory(row.savedBytes) }
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};
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printReportFlatFields(os, fields);
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}
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@@ -87,10 +86,12 @@ static void emitReport(ArrayRef<CoalescingReportEntry> entries) {
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totalRow.savedBytes += entryTotal.savedBytes;
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}
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llvm::SmallVector<ReportField, 4> totalFields = {{"Number of candidates", std::to_string(totalRow.numCandidates)},
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{"Skipped allocations", std::to_string(totalRow.numSkipped)},
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{"Removed allocations", std::to_string(totalRow.numRemoved)},
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{"Saved memory", formatMemory(totalRow.savedBytes)}};
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llvm::SmallVector<ReportField, 4> totalFields = {
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{"Number of candidates", std::to_string(totalRow.numCandidates)},
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{"Skipped allocations", std::to_string(totalRow.numSkipped) },
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{"Removed allocations", std::to_string(totalRow.numRemoved) },
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{"Saved memory", formatMemory(totalRow.savedBytes) }
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};
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printReportTotalsBlock(os, totalFields);
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if (!entries.empty())
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os << "\n";
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@@ -127,15 +128,17 @@ static void emitReport(ArrayRef<CoalescingReportEntry> entries) {
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if (sortedEntries[index].kind == CoalescingReportEntry::Kind::Batch) {
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llvm::SmallVector<ReportField, 4> perCoreFields = {
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{"Number of candidates", std::to_string(sortedEntries[index].row.numCandidates)},
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{"Skipped allocations", std::to_string(sortedEntries[index].row.numSkipped)},
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{"Removed allocations", std::to_string(sortedEntries[index].row.numRemoved)},
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{"Saved memory", formatMemory(sortedEntries[index].row.savedBytes)}};
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{"Skipped allocations", std::to_string(sortedEntries[index].row.numSkipped) },
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{"Removed allocations", std::to_string(sortedEntries[index].row.numRemoved) },
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{"Saved memory", formatMemory(sortedEntries[index].row.savedBytes) }
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};
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CoalescingReportRow totalRow = getTotalRow(sortedEntries[index]);
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llvm::SmallVector<ReportField, 4> totalFields = {
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{"Number of candidates", std::to_string(totalRow.numCandidates)},
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{"Skipped allocations", std::to_string(totalRow.numSkipped)},
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{"Removed allocations", std::to_string(totalRow.numRemoved)},
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{"Saved memory", formatMemory(totalRow.savedBytes)}};
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{"Skipped allocations", std::to_string(totalRow.numSkipped) },
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{"Removed allocations", std::to_string(totalRow.numRemoved) },
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{"Saved memory", formatMemory(totalRow.savedBytes) }
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};
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printReportPerCoreAndTotalFields(os, perCoreFields, totalFields);
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}
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else {
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@@ -196,8 +199,6 @@ struct StaticMemoryCoalescingPass : PassWrapper<StaticMemoryCoalescingPass, Oper
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} // namespace
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std::unique_ptr<Pass> createPimStaticMemoryCoalescingPass() {
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return std::make_unique<StaticMemoryCoalescingPass>();
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}
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std::unique_ptr<Pass> createPimStaticMemoryCoalescingPass() { return std::make_unique<StaticMemoryCoalescingPass>(); }
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} // namespace onnx_mlir
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@@ -818,13 +818,14 @@ void generateReport(func::FuncOp funcOp, const std::string& name, size_t usedCpu
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}
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}
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llvm::SmallVector<ReportField, 6> totalFields = {{"Used cores", std::to_string(usedCpuCount)},
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{"Number of top-level compute ops", std::to_string(totalComputeOps)},
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{"Number of logical computes", std::to_string(totalLogicalComputes)},
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{"Number of top-level batch compute ops",
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std::to_string(totalBatchComputeOps)},
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{"Number of instructions", std::to_string(totalInstructionCount)},
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{"Number of used crossbars", std::to_string(totalWeightCount)}};
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llvm::SmallVector<ReportField, 6> totalFields = {
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{"Used cores", std::to_string(usedCpuCount) },
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{"Number of top-level compute ops", std::to_string(totalComputeOps) },
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{"Number of logical computes", std::to_string(totalLogicalComputes) },
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{"Number of top-level batch compute ops", std::to_string(totalBatchComputeOps) },
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{"Number of instructions", std::to_string(totalInstructionCount)},
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{"Number of used crossbars", std::to_string(totalWeightCount) }
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};
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printReportTotalsBlock(os, totalFields);
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if (!collectedData.empty())
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os << "\n";
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@@ -876,13 +877,15 @@ void generateReport(func::FuncOp funcOp, const std::string& name, size_t usedCpu
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llvm::SmallVector<ReportField, 3> perCoreFields = {
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{"Number of logical computes", std::to_string(perCoreLogicalComputeCount)},
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{"Number of instructions", std::to_string(perCoreInstructionCount)},
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{"Number of used crossbars", std::to_string(perCoreWeightCount)}};
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{"Number of instructions", std::to_string(perCoreInstructionCount) },
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{"Number of used crossbars", std::to_string(perCoreWeightCount) }
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};
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if (current.isRebatched) {
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llvm::SmallVector<ReportField, 3> totalEntryFields = {
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{"Number of logical computes", std::to_string(current.logicalComputeCount)},
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{"Number of instructions", std::to_string(totalEntryInstructionCount)},
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{"Number of used crossbars", std::to_string(current.weightCount)}};
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{"Number of instructions", std::to_string(totalEntryInstructionCount) },
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{"Number of used crossbars", std::to_string(current.weightCount) }
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};
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printReportPerCoreAndTotalFields(os, perCoreFields, totalEntryFields);
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}
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else {
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@@ -1003,6 +1006,23 @@ public:
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DenseMap<Value, Value> externalInputMap;
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DenseMap<Value, size_t> weightToIndex;
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};
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struct RemoteSendInfo {
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ChannelInfo channelInfo;
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ComputeInstance consumer;
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size_t inputIndex = 0;
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size_t consumerOrder = 0;
|
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size_t sourceOrder = 0;
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};
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struct RemoteReceiveEntry {
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ChannelInfo channelInfo;
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ComputeInstance consumer;
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size_t inputIndex = 0;
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||||
size_t sourceOrder = 0;
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};
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auto getRemoteSendPairKey = [](const ChannelInfo& channelInfo) {
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return (static_cast<uint64_t>(static_cast<uint32_t>(channelInfo.sourceCoreId)) << 32)
|
||||
| static_cast<uint32_t>(channelInfo.targetCoreId);
|
||||
};
|
||||
|
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auto getTaskInputs = [&](const ScheduledTask& task) {
|
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SmallVector<Value> inputs;
|
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@@ -1143,7 +1163,7 @@ public:
|
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}
|
||||
};
|
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|
||||
DenseMap<ComputeInstance, SmallVector<SmallVector<ChannelInfo>>> remoteSendsByTask;
|
||||
DenseMap<ComputeInstance, SmallVector<SmallVector<RemoteSendInfo>>> remoteSendsByTask;
|
||||
DenseMap<ComputeInstance, SmallVector<std::optional<ChannelInfo>>> remoteInputsByTask;
|
||||
DenseMap<size_t, SmallVector<Value>> cpuExternalInputs;
|
||||
DenseMap<size_t, SmallVector<Value>> cpuWeights;
|
||||
@@ -1176,7 +1196,7 @@ public:
|
||||
auto& perResultChannels = remoteSendsByTask[producerRef->instance];
|
||||
if (perResultChannels.empty())
|
||||
perResultChannels.resize(getTaskOutputTypes(producerIt->second).size());
|
||||
perResultChannels[producerRef->resultIndex].push_back(info);
|
||||
perResultChannels[producerRef->resultIndex].push_back({info, task.key, inputIndex, task.order, 0});
|
||||
}
|
||||
continue;
|
||||
}
|
||||
@@ -1201,6 +1221,79 @@ public:
|
||||
}
|
||||
}
|
||||
|
||||
DenseSet<uint64_t> pairsNeedingReceiveReorder;
|
||||
for (size_t cpu : orderedCpus) {
|
||||
DenseMap<uint64_t, size_t> nextSourceOrderByPair;
|
||||
DenseMap<uint64_t, size_t> lastConsumerOrderByPair;
|
||||
for (const ScheduledTask& task : tasksByCpu[cpu]) {
|
||||
auto sendsIt = remoteSendsByTask.find(task.key);
|
||||
if (sendsIt == remoteSendsByTask.end())
|
||||
continue;
|
||||
for (auto& sendInfos : sendsIt->second) {
|
||||
for (RemoteSendInfo& sendInfo : sendInfos) {
|
||||
uint64_t pairKey = getRemoteSendPairKey(sendInfo.channelInfo);
|
||||
sendInfo.sourceOrder = nextSourceOrderByPair[pairKey]++;
|
||||
auto [it, inserted] = lastConsumerOrderByPair.try_emplace(pairKey, sendInfo.consumerOrder);
|
||||
if (!inserted) {
|
||||
if (sendInfo.consumerOrder < it->second)
|
||||
pairsNeedingReceiveReorder.insert(pairKey);
|
||||
it->second = sendInfo.consumerOrder;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
DenseMap<uint64_t, SmallVector<RemoteSendInfo*>> reorderedSendsByPair;
|
||||
for (auto& taskSends : remoteSendsByTask) {
|
||||
for (auto& sendInfos : taskSends.second) {
|
||||
for (RemoteSendInfo& sendInfo : sendInfos) {
|
||||
uint64_t pairKey = getRemoteSendPairKey(sendInfo.channelInfo);
|
||||
if (pairsNeedingReceiveReorder.contains(pairKey))
|
||||
reorderedSendsByPair[pairKey].push_back(&sendInfo);
|
||||
}
|
||||
}
|
||||
}
|
||||
for (auto& pairSends : reorderedSendsByPair) {
|
||||
llvm::stable_sort(pairSends.second, [](const RemoteSendInfo* lhs, const RemoteSendInfo* rhs) {
|
||||
if (lhs->sourceOrder != rhs->sourceOrder)
|
||||
return lhs->sourceOrder < rhs->sourceOrder;
|
||||
return lhs->channelInfo.channelId < rhs->channelInfo.channelId;
|
||||
});
|
||||
for (RemoteSendInfo* sendInfo : pairSends.second) {
|
||||
int64_t channelId = nextChannelId++;
|
||||
sendInfo->channelInfo.channelId = channelId;
|
||||
auto remoteInputsIt = remoteInputsByTask.find(sendInfo->consumer);
|
||||
assert(remoteInputsIt != remoteInputsByTask.end() && "missing remote input for reordered send");
|
||||
assert(sendInfo->inputIndex < remoteInputsIt->second.size() && "remote input index out of range");
|
||||
assert(remoteInputsIt->second[sendInfo->inputIndex] && "missing reordered remote input channel");
|
||||
remoteInputsIt->second[sendInfo->inputIndex]->channelId = channelId;
|
||||
}
|
||||
}
|
||||
|
||||
DenseMap<size_t, DenseMap<uint64_t, SmallVector<RemoteReceiveEntry>>> receiveQueuesByCpu;
|
||||
for (auto& taskSends : remoteSendsByTask) {
|
||||
for (const auto& sendInfos : taskSends.second) {
|
||||
for (const RemoteSendInfo& sendInfo : sendInfos) {
|
||||
uint64_t pairKey = getRemoteSendPairKey(sendInfo.channelInfo);
|
||||
if (!pairsNeedingReceiveReorder.contains(pairKey))
|
||||
continue;
|
||||
size_t targetCpu = static_cast<size_t>(sendInfo.channelInfo.targetCoreId - 1);
|
||||
receiveQueuesByCpu[targetCpu][pairKey].push_back(
|
||||
{sendInfo.channelInfo, sendInfo.consumer, sendInfo.inputIndex, sendInfo.sourceOrder});
|
||||
}
|
||||
}
|
||||
}
|
||||
for (auto& cpuQueues : receiveQueuesByCpu) {
|
||||
for (auto& pairQueue : cpuQueues.second) {
|
||||
llvm::stable_sort(pairQueue.second, [](const RemoteReceiveEntry& lhs, const RemoteReceiveEntry& rhs) {
|
||||
if (lhs.sourceOrder != rhs.sourceOrder)
|
||||
return lhs.sourceOrder < rhs.sourceOrder;
|
||||
return lhs.channelInfo.channelId < rhs.channelInfo.channelId;
|
||||
});
|
||||
}
|
||||
}
|
||||
|
||||
auto returnOp = cast<func::ReturnOp>(func.getBody().front().getTerminator());
|
||||
IRRewriter rewriter(&getContext());
|
||||
DenseMap<size_t, CpuProgram> cpuPrograms;
|
||||
@@ -1255,6 +1348,59 @@ public:
|
||||
CpuProgram& program = cpuPrograms[cpu];
|
||||
IRRewriter cpuRewriter(&getContext());
|
||||
cpuRewriter.setInsertionPointToEnd(program.block);
|
||||
DenseMap<uint64_t, size_t> receiveQueueIndices;
|
||||
DenseMap<ComputeInstance, SmallVector<Value>> preReceivedInputsByTask;
|
||||
|
||||
auto lookupPreReceivedInput = [&](ComputeInstance consumer, size_t inputIndex) -> std::optional<Value> {
|
||||
auto inputsIt = preReceivedInputsByTask.find(consumer);
|
||||
if (inputsIt == preReceivedInputsByTask.end() || inputsIt->second.size() <= inputIndex)
|
||||
return std::nullopt;
|
||||
Value value = inputsIt->second[inputIndex];
|
||||
if (!value)
|
||||
return std::nullopt;
|
||||
return value;
|
||||
};
|
||||
|
||||
auto receiveThroughInput = [&](const ChannelInfo& requestedChannelInfo,
|
||||
ComputeInstance requestedConsumer,
|
||||
size_t requestedInputIndex) -> std::optional<Value> {
|
||||
uint64_t pairKey = getRemoteSendPairKey(requestedChannelInfo);
|
||||
auto cpuQueuesIt = receiveQueuesByCpu.find(cpu);
|
||||
if (cpuQueuesIt == receiveQueuesByCpu.end())
|
||||
return std::nullopt;
|
||||
auto queueIt = cpuQueuesIt->second.find(pairKey);
|
||||
if (queueIt == cpuQueuesIt->second.end())
|
||||
return std::nullopt;
|
||||
|
||||
auto& queue = queueIt->second;
|
||||
size_t& queueIndex = receiveQueueIndices[pairKey];
|
||||
while (queueIndex < queue.size()) {
|
||||
const RemoteReceiveEntry& entry = queue[queueIndex++];
|
||||
auto consumerTaskIt = taskByKey.find(entry.consumer);
|
||||
if (consumerTaskIt == taskByKey.end())
|
||||
return std::nullopt;
|
||||
SmallVector<Value> consumerInputs = getTaskInputs(consumerTaskIt->second);
|
||||
if (consumerInputs.size() <= entry.inputIndex)
|
||||
return std::nullopt;
|
||||
Type inputType = consumerInputs[entry.inputIndex].getType();
|
||||
auto receive =
|
||||
spatial::SpatChannelReceiveOp::create(cpuRewriter,
|
||||
loc,
|
||||
inputType,
|
||||
cpuRewriter.getI64IntegerAttr(entry.channelInfo.channelId),
|
||||
cpuRewriter.getI32IntegerAttr(entry.channelInfo.sourceCoreId),
|
||||
cpuRewriter.getI32IntegerAttr(entry.channelInfo.targetCoreId));
|
||||
|
||||
auto& receivedInputs = preReceivedInputsByTask[entry.consumer];
|
||||
if (receivedInputs.size() <= entry.inputIndex)
|
||||
receivedInputs.resize(entry.inputIndex + 1);
|
||||
receivedInputs[entry.inputIndex] = receive.getResult();
|
||||
|
||||
if (entry.consumer == requestedConsumer && entry.inputIndex == requestedInputIndex)
|
||||
return receive.getResult();
|
||||
}
|
||||
return std::nullopt;
|
||||
};
|
||||
|
||||
for (const ScheduledTask& task : tasksByCpu[cpu]) {
|
||||
SmallVector<Value> taskInputs = getTaskInputs(task);
|
||||
@@ -1284,6 +1430,24 @@ public:
|
||||
continue;
|
||||
}
|
||||
const ChannelInfo& channelInfo = *remoteInputsIt->second[inputIndex];
|
||||
uint64_t pairKey = getRemoteSendPairKey(channelInfo);
|
||||
if (pairsNeedingReceiveReorder.contains(pairKey)) {
|
||||
if (std::optional<Value> preReceived = lookupPreReceivedInput(task.key, inputIndex)) {
|
||||
resolvedInputs.push_back(*preReceived);
|
||||
continue;
|
||||
}
|
||||
std::optional<Value> received = receiveThroughInput(channelInfo, task.key, inputIndex);
|
||||
if (!received) {
|
||||
task.sourceOp->emitOpError("failed to materialize reordered remote receive")
|
||||
<< " consumerCpu=" << cpu << " consumerSlot=" << task.slot
|
||||
<< " sourceCoreId=" << channelInfo.sourceCoreId << " targetCoreId=" << channelInfo.targetCoreId
|
||||
<< " channelId=" << channelInfo.channelId;
|
||||
signalPassFailure();
|
||||
return;
|
||||
}
|
||||
resolvedInputs.push_back(*received);
|
||||
continue;
|
||||
}
|
||||
auto receive =
|
||||
spatial::SpatChannelReceiveOp::create(cpuRewriter,
|
||||
loc,
|
||||
@@ -1367,13 +1531,14 @@ public:
|
||||
if (sendInfos.empty())
|
||||
continue;
|
||||
Value producedValue = taskYieldValues[resultIndex];
|
||||
for (const ChannelInfo& sendInfo : sendInfos)
|
||||
for (const RemoteSendInfo& sendInfo : sendInfos) {
|
||||
spatial::SpatChannelSendOp::create(cpuRewriter,
|
||||
loc,
|
||||
cpuRewriter.getI64IntegerAttr(sendInfo.channelId),
|
||||
cpuRewriter.getI32IntegerAttr(sendInfo.sourceCoreId),
|
||||
cpuRewriter.getI32IntegerAttr(sendInfo.targetCoreId),
|
||||
cpuRewriter.getI64IntegerAttr(sendInfo.channelInfo.channelId),
|
||||
cpuRewriter.getI32IntegerAttr(sendInfo.channelInfo.sourceCoreId),
|
||||
cpuRewriter.getI32IntegerAttr(sendInfo.channelInfo.targetCoreId),
|
||||
producedValue);
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
@@ -1666,23 +1831,21 @@ private:
|
||||
IRRewriter rewriter(context);
|
||||
|
||||
rewriter.setInsertionPointAfter(producerOp);
|
||||
auto savedSendInsertPoint = rewriter.saveInsertionPoint();
|
||||
auto insertNew = [this, savedSendInsertPoint, context, loc, computeValueResults, producerCpu](size_t resultIndex,
|
||||
size_t targetCpu) {
|
||||
auto insertNew = [this, context, loc, computeValueResults, producerCpu](size_t resultIndex, size_t targetCpu) {
|
||||
auto channelId = nextChannelId++;
|
||||
LazyInsertComputeResult::ChannelInfo channelInfo {
|
||||
channelId, getPhysicalCoreId(producerCpu), getPhysicalCoreId(targetCpu)};
|
||||
auto insertVal = [&context, loc, computeValueResults, channelInfo, resultIndex, savedSendInsertPoint](
|
||||
mlir::IRRewriter::InsertPoint) {
|
||||
IRRewriter rewriter(context);
|
||||
rewriter.restoreInsertionPoint(savedSendInsertPoint);
|
||||
spatial::SpatChannelSendOp::create(rewriter,
|
||||
loc,
|
||||
rewriter.getI64IntegerAttr(channelInfo.channelId),
|
||||
rewriter.getI32IntegerAttr(channelInfo.sourceCoreId),
|
||||
rewriter.getI32IntegerAttr(channelInfo.targetCoreId),
|
||||
computeValueResults.getOuter(resultIndex));
|
||||
};
|
||||
auto insertVal =
|
||||
[&context, loc, computeValueResults, channelInfo, resultIndex](mlir::IRRewriter::InsertPoint insertPoint) {
|
||||
IRRewriter rewriter(context);
|
||||
rewriter.restoreInsertionPoint(insertPoint);
|
||||
spatial::SpatChannelSendOp::create(rewriter,
|
||||
loc,
|
||||
rewriter.getI64IntegerAttr(channelInfo.channelId),
|
||||
rewriter.getI32IntegerAttr(channelInfo.sourceCoreId),
|
||||
rewriter.getI32IntegerAttr(channelInfo.targetCoreId),
|
||||
computeValueResults.getOuter(resultIndex));
|
||||
};
|
||||
std::pair<LazyInsertComputeResult::ChannelInfo, std::function<void(mlir::IRRewriter::InsertPoint)>> ret {
|
||||
channelInfo, insertVal};
|
||||
return ret;
|
||||
|
||||
@@ -10,8 +10,6 @@
|
||||
#include "llvm/ADT/STLExtras.h"
|
||||
#include "llvm/ADT/SmallVector.h"
|
||||
|
||||
#include <tuple>
|
||||
|
||||
#include "RegularOpCompaction.hpp"
|
||||
#include "src/Accelerators/PIM/Conversion/SpatialToPim/TensorPackingPatterns.hpp"
|
||||
#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
|
||||
@@ -340,7 +338,18 @@ void compactScalarChannelRuns(func::FuncOp funcOp, int64_t& nextChannelId) {
|
||||
++runIt;
|
||||
}
|
||||
|
||||
if (run.size() > 1) {
|
||||
bool hasRepeatedEndpoint = false;
|
||||
for (size_t lhs = 0; lhs < run.size() && !hasRepeatedEndpoint; ++lhs) {
|
||||
for (size_t rhs = lhs + 1; rhs < run.size(); ++rhs) {
|
||||
if (run[lhs].getSourceCoreId() == run[rhs].getSourceCoreId()
|
||||
&& run[lhs].getTargetCoreId() == run[rhs].getTargetCoreId()) {
|
||||
hasRepeatedEndpoint = true;
|
||||
break;
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
if (run.size() > 1 && !hasRepeatedEndpoint) {
|
||||
struct ReceiveEntry {
|
||||
spatial::SpatChannelReceiveOp op;
|
||||
size_t originalIndex = 0;
|
||||
@@ -352,10 +361,6 @@ void compactScalarChannelRuns(func::FuncOp funcOp, int64_t& nextChannelId) {
|
||||
sortedEntries.reserve(run.size());
|
||||
for (auto [originalIndex, op] : llvm::enumerate(run))
|
||||
sortedEntries.push_back({op, originalIndex, op.getSourceCoreId(), op.getTargetCoreId(), op.getChannelId()});
|
||||
llvm::stable_sort(sortedEntries, [](const ReceiveEntry& lhs, const ReceiveEntry& rhs) {
|
||||
return std::tuple(lhs.sourceCoreId, lhs.targetCoreId, lhs.channelId)
|
||||
< std::tuple(rhs.sourceCoreId, rhs.targetCoreId, rhs.channelId);
|
||||
});
|
||||
|
||||
SmallVector<int64_t> channelIds;
|
||||
SmallVector<int32_t> sourceCoreIds;
|
||||
@@ -436,10 +441,6 @@ void compactScalarChannelRuns(func::FuncOp funcOp, int64_t& nextChannelId) {
|
||||
sortedEntries.reserve(run.size());
|
||||
for (auto op : run)
|
||||
sortedEntries.push_back({op, op.getSourceCoreId(), op.getTargetCoreId(), op.getChannelId()});
|
||||
llvm::stable_sort(sortedEntries, [](const SendEntry& lhs, const SendEntry& rhs) {
|
||||
return std::tuple(lhs.sourceCoreId, lhs.targetCoreId, lhs.channelId)
|
||||
< std::tuple(rhs.sourceCoreId, rhs.targetCoreId, rhs.channelId);
|
||||
});
|
||||
|
||||
SmallVector<int64_t> channelIds;
|
||||
SmallVector<int32_t> sourceCoreIds;
|
||||
|
||||
@@ -66,8 +66,10 @@ static Value buildSubviewChunk(const StaticSubviewInfo& info,
|
||||
return memref::SubViewOp::create(rewriter, loc, info.source, chunkOffsets, chunkSizes, chunkStrides);
|
||||
}
|
||||
|
||||
static SmallVector<Value>
|
||||
delinearizeIndexValue(Value linearIndex, ArrayRef<int64_t> shape, ArrayRef<int64_t> strides, PatternRewriter& rewriter) {
|
||||
static SmallVector<Value> delinearizeIndexValue(Value linearIndex,
|
||||
ArrayRef<int64_t> shape,
|
||||
ArrayRef<int64_t> strides,
|
||||
PatternRewriter& rewriter) {
|
||||
SmallVector<Value> indices;
|
||||
indices.reserve(shape.size());
|
||||
|
||||
@@ -112,7 +114,8 @@ static Value buildDynamicSubviewChunk(const StaticSubviewInfo& info,
|
||||
assert(info.strides[dim] == 1 && "loop-based subview rewrite requires unit strides");
|
||||
chunkOffsets.push_back(addDynamicOffset(info.offsets[dim], outerIndices[dim], rewriter));
|
||||
chunkSizes.push_back(rewriter.getIndexAttr(1));
|
||||
} else {
|
||||
}
|
||||
else {
|
||||
chunkOffsets.push_back(info.offsets[dim]);
|
||||
chunkSizes.push_back(rewriter.getIndexAttr(info.sizes.back()));
|
||||
}
|
||||
@@ -122,11 +125,8 @@ static Value buildDynamicSubviewChunk(const StaticSubviewInfo& info,
|
||||
return memref::SubViewOp::create(rewriter, loc, info.source, chunkOffsets, chunkSizes, chunkStrides);
|
||||
}
|
||||
|
||||
static Value buildContiguousChunk(Value source,
|
||||
ArrayRef<int64_t> copyShape,
|
||||
ArrayRef<Value> outerIndices,
|
||||
Location loc,
|
||||
PatternRewriter& rewriter) {
|
||||
static Value buildContiguousChunk(
|
||||
Value source, ArrayRef<int64_t> copyShape, ArrayRef<Value> outerIndices, Location loc, PatternRewriter& rewriter) {
|
||||
SmallVector<OpFoldResult> chunkOffsets;
|
||||
SmallVector<OpFoldResult> chunkSizes;
|
||||
SmallVector<OpFoldResult> chunkStrides;
|
||||
@@ -203,7 +203,8 @@ static LogicalResult rewriteSubviewCopyLikeOp(CopyOp copyOp,
|
||||
rewriter.setInsertionPointToStart(loop.getBody());
|
||||
|
||||
SmallVector<Value> outerIndices =
|
||||
outerShape.empty() ? SmallVector<Value> {} : delinearizeIndexValue(loop.getInductionVar(), outerShape, outerStrides, rewriter);
|
||||
outerShape.empty() ? SmallVector<Value> {}
|
||||
: delinearizeIndexValue(loop.getInductionVar(), outerShape, outerStrides, rewriter);
|
||||
Value chunkDst = splitDst ? buildDynamicSubviewChunk(*dstSubview, outerIndices, copyOp.getLoc(), rewriter)
|
||||
: buildContiguousChunk(dst, copyShape, outerIndices, copyOp.getLoc(), rewriter);
|
||||
Value chunkSrc = splitSrc ? buildDynamicSubviewChunk(*srcSubview, outerIndices, copyOp.getLoc(), rewriter)
|
||||
|
||||
@@ -6,10 +6,10 @@
|
||||
|
||||
#include "llvm/ADT/STLExtras.h"
|
||||
|
||||
#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
|
||||
#include "src/Accelerators/PIM/Common/PimCommon.hpp"
|
||||
#include "src/Accelerators/PIM/Dialect/Pim/PimOps.hpp"
|
||||
#include "src/Accelerators/PIM/Dialect/Spatial/SpatialOps.hpp"
|
||||
#include "src/Accelerators/PIM/Common/IR/SubviewUtils.hpp"
|
||||
|
||||
using namespace mlir;
|
||||
|
||||
|
||||
@@ -60,6 +60,7 @@ def main():
|
||||
ap.add_argument("--simulator-dir", default=None,
|
||||
help="Path to pim-simulator crate root (default: auto-detected relative to script).")
|
||||
ap.add_argument("--threshold", type=float, default=1e-3, help="Max allowed diff per output element.")
|
||||
ap.add_argument("--seed", type=int, default=0, help="RNG seed for generated validation inputs.")
|
||||
ap.add_argument("--crossbar-size", type=int, default=64)
|
||||
ap.add_argument("--crossbar-count", type=int, default=8)
|
||||
ap.add_argument("--core-count", type=int, default=None,
|
||||
@@ -117,6 +118,7 @@ def main():
|
||||
onnx_path, a.raptor_path, a.onnx_include_dir, simulator_dir,
|
||||
crossbar_size=a.crossbar_size, crossbar_count=a.crossbar_count, core_count=a.core_count,
|
||||
threshold=a.threshold,
|
||||
seed=a.seed,
|
||||
reporter=reporter,
|
||||
model_index=index,
|
||||
model_total=len(onnx_files),
|
||||
|
||||
@@ -268,7 +268,7 @@ def validate_outputs(sim_arrays, runner_out_dir, outputs_descriptor, threshold=1
|
||||
|
||||
def validate_network(network_onnx_path, raptor_path, onnx_include_dir,
|
||||
simulator_dir, crossbar_size=64, crossbar_count=8, core_count=None, threshold=1e-3,
|
||||
reporter=None, model_index=1, model_total=1, verbose=False):
|
||||
seed=0, reporter=None, model_index=1, model_total=1, verbose=False):
|
||||
network_onnx_path = Path(network_onnx_path).resolve()
|
||||
raptor_path = Path(raptor_path).resolve()
|
||||
onnx_include_dir = Path(onnx_include_dir).resolve()
|
||||
@@ -306,7 +306,7 @@ def validate_network(network_onnx_path, raptor_path, onnx_include_dir,
|
||||
|
||||
print_stage(reporter, model_index, model_total, network_onnx_path.name, "Generate Inputs")
|
||||
inputs_descriptor, outputs_descriptor = onnx_io(network_onnx_path)
|
||||
inputs_list, _inputs_dict = gen_random_inputs(inputs_descriptor)
|
||||
inputs_list, _inputs_dict = gen_random_inputs(inputs_descriptor, seed=seed)
|
||||
flags, _files = save_inputs_to_files(network_onnx_path, inputs_list, out_dir=workspace_dir / "inputs")
|
||||
print_info(reporter, f"Saved {len(inputs_list)} input file(s) to {workspace_dir / 'inputs'}")
|
||||
reporter.advance()
|
||||
|
||||
Reference in New Issue
Block a user