From f79109963afc1ccce394b13d56e85fa3debe36de Mon Sep 17 00:00:00 2001 From: Gorokhov Dmitriy Date: Wed, 21 Aug 2024 16:04:57 +0400 Subject: [PATCH 01/35] [CPU] Fix debug assertion in oneDNN Matmul Brgemm kernel (#26087) ### Details: - OneDNN Matmul Brgemm kernel contains wrong debug assertion which lead to functional failurs on systems with avx2_vnni support. ### Tickets: - [CVS-149880](https://jira.devtools.intel.com/browse/CVS-149880) --- src/plugins/intel_cpu/thirdparty/onednn | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/plugins/intel_cpu/thirdparty/onednn b/src/plugins/intel_cpu/thirdparty/onednn index 6b99866a4531e3..6237cb7cc6df35 160000 --- a/src/plugins/intel_cpu/thirdparty/onednn +++ b/src/plugins/intel_cpu/thirdparty/onednn @@ -1 +1 @@ -Subproject commit 6b99866a4531e38a74d1de36d5b366c54c5e6cc3 +Subproject commit 6237cb7cc6df3591bf0113fdc546fab724d4d55b From a07e2bc449a9cb4f0dc2a1e1f6dca97f3acd49d3 Mon Sep 17 00:00:00 2001 From: Alicja Miloszewska Date: Wed, 21 Aug 2024 15:34:06 +0200 Subject: [PATCH 02/35] [OV JS] Remove extra assets from tests (#25712) ### Details: - Remove testing models from repository - Add `setup.js` script that will be called once to download testing models - Before running the suite check if model is downloaded, add top describe to *.test.js files ### Notes: Currently there is no global setup file for native [node test runner](https://github.com/nodejs/node/issues/49732) as in other test runner e.g. [jest](https://jestjs.io/docs/configuration#globalsetup-string) ### Tickets: - [CVS-146345](https://jira.devtools.intel.com/browse/CVS-146345) --------- Co-authored-by: Vishniakov Nikolai --- src/bindings/js/node/.gitignore | 1 + src/bindings/js/node/package.json | 3 +- .../js/node/scripts/download_runtime.js | 18 +- src/bindings/js/node/tests/unit/basic.test.js | 481 +++++++++--------- .../js/node/tests/unit/compiled_model.test.js | 46 +- src/bindings/js/node/tests/unit/core.test.js | 85 ++-- .../js/node/tests/unit/infer_request.test.js | 469 ++++++++--------- src/bindings/js/node/tests/unit/model.test.js | 211 ++++---- .../js/node/tests/unit/partial_shape.test.js | 20 +- .../tests/unit/pre_post_processor.test.js | 244 ++++----- .../js/node/tests/unit/read_model.test.js | 137 ++--- src/bindings/js/node/tests/unit/setup.js | 9 + .../js/node/tests/unit/tensor.test.js | 397 ++++++++------- .../unit/test_models/test_model_fp32.bin | Bin 337592 -> 0 bytes .../unit/test_models/test_model_fp32.xml | 467 ----------------- src/bindings/js/node/tests/unit/utils.js | 60 ++- 16 files changed, 1147 insertions(+), 1501 deletions(-) create mode 100644 src/bindings/js/node/tests/unit/setup.js delete mode 100644 src/bindings/js/node/tests/unit/test_models/test_model_fp32.bin delete mode 100644 src/bindings/js/node/tests/unit/test_models/test_model_fp32.xml diff --git a/src/bindings/js/node/.gitignore b/src/bindings/js/node/.gitignore index 6bbca841fe2db9..38782682575826 100644 --- a/src/bindings/js/node/.gitignore +++ b/src/bindings/js/node/.gitignore @@ -3,6 +3,7 @@ dist build types ov_runtime +tests/unit/test_models *.exp diff --git a/src/bindings/js/node/package.json b/src/bindings/js/node/package.json index 43f0a264571c9e..a3544a6136cccf 100644 --- a/src/bindings/js/node/package.json +++ b/src/bindings/js/node/package.json @@ -18,7 +18,8 @@ "build": "npm run tsc", "prepare": "npm run build", "lint": "eslint .", - "test": "node --test ./tests/unit/*.test.js", + "test_setup": "node ./tests/unit/setup.js", + "test": "npm run test_setup && node --test ./tests/unit/*.test.js", "test:e2e": "mocha ./tests/e2e/electron-app.test.js", "tsc": "tsc", "postinstall": "npm run install_runtime", diff --git a/src/bindings/js/node/scripts/download_runtime.js b/src/bindings/js/node/scripts/download_runtime.js index f3aa3dce849c4d..775b4aa6c754c9 100644 --- a/src/bindings/js/node/scripts/download_runtime.js +++ b/src/bindings/js/node/scripts/download_runtime.js @@ -61,7 +61,7 @@ class RuntimeExistsError extends Error { async function downloadRuntime(destinationPath, config = { force: false, ignoreIfExists: true, proxy: null }) { const { version } = packageJson; const osInfo = await getOsInfo(); - const isRuntimeDirectoryExists = await checkIfDirectoryExists(destinationPath); + const isRuntimeDirectoryExists = await checkIfPathExists(destinationPath); if (isRuntimeDirectoryExists && !config.force) { if (config.ignoreIfExists) { @@ -87,7 +87,7 @@ async function downloadRuntime(destinationPath, config = { force: false, ignoreI await fs.mkdir(tempDirectoryPath); console.log('Downloading OpenVINO runtime archive...'); - await downloadFile(runtimeArchiveUrl, filename, tempDirectoryPath, config.proxy); + await downloadFile(runtimeArchiveUrl, tempDirectoryPath, filename, config.proxy); console.log('OpenVINO runtime archive downloaded.'); await removeDirectory(destinationPath); @@ -139,16 +139,16 @@ async function getOsInfo() { } /** - * Check if directory exists. + * Check if path exists. * * @async - * @function checkIfDirectoryExists - * @param {string} directoryPath - The directory path. + * @function checkIfPathExists + * @param {string} path - The path to directory or file. * @returns {Promise} */ -async function checkIfDirectoryExists(directoryPath) { +async function checkIfPathExists(path) { try { - await fs.access(directoryPath); + await fs.access(path); return true; } catch (error) { if (error.code === codeENOENT) { @@ -210,7 +210,7 @@ async function removeDirectory(path) { * @param {string} [proxy=null] - (Optional) The proxy URL. * @returns {Promise} */ -function downloadFile(url, filename, destination, proxy = null) { +function downloadFile(url, destination, filename, proxy = null) { const timeout = 5000; const fullPath = path.resolve(destination, filename); const file = createWriteStream(fullPath); @@ -281,4 +281,4 @@ function unarchive(tarFilePath, dest) { }); } -module.exports = { downloadRuntime }; +module.exports = { downloadRuntime, downloadFile, checkIfPathExists }; diff --git a/src/bindings/js/node/tests/unit/basic.test.js b/src/bindings/js/node/tests/unit/basic.test.js index e7447b3c863655..4b4913fb52b7d2 100644 --- a/src/bindings/js/node/tests/unit/basic.test.js +++ b/src/bindings/js/node/tests/unit/basic.test.js @@ -4,78 +4,83 @@ const { addon: ov } = require('../..'); const assert = require('assert'); -const { describe, it } = require('node:test'); -const { getModelPath } = require('./utils.js'); +const { describe, it, before } = require('node:test'); +const { testModels, getModelPath, isModelAvailable } = require('./utils.js'); -const testXml = getModelPath().xml; -const core = new ov.Core(); -const model = core.readModelSync(testXml); -const compiledModel = core.compileModelSync(model, 'CPU'); -const modelLike = [[model], - [compiledModel]]; +describe('ov basic tests.', () => { -it('Core.getAvailableDevices()', () => { - const devices = core.getAvailableDevices(); - - assert.ok(devices.includes('CPU')); -}); - -describe('Core.getVersions()', () => { - - it('getVersions(validDeviceName: string)', () => { - const deviceVersion = core.getVersions('CPU'); - assert.strictEqual(typeof deviceVersion, 'object'); - assert.strictEqual(typeof deviceVersion.CPU, 'object'); - assert.strictEqual(typeof deviceVersion.CPU.buildNumber, 'string'); - assert.strictEqual(typeof deviceVersion.CPU.description, 'string'); + before(async () => { + await isModelAvailable(testModels.testModelFP32); }); - it('getVersions() throws if no arguments are passed into the function', () => { - assert.throws( - () => core.getVersions(), - { message: 'getVersions() method expects 1 argument of string type.' } - ); - }); + const testXml = getModelPath().xml; + const core = new ov.Core(); + const model = core.readModelSync(testXml); + const compiledModel = core.compileModelSync(model, 'CPU'); + const modelLike = [[model], + [compiledModel]]; - it('getVersions() throws if non string coercable arguments are passed into the function', () => { - assert.throws( - () => core.getVersions({ deviceName: 'CPU' }), - { message: 'The argument in getVersions() method must be a string or convertible to a string.' } - ); - }); + it('Core.getAvailableDevices()', () => { + const devices = core.getAvailableDevices(); -}); + assert.ok(devices.includes('CPU')); + }); + describe('Core.getVersions()', () => { -it('CompiledModel type', () => { - assert.ok(compiledModel instanceof ov.CompiledModel); -}); + it('getVersions(validDeviceName: string)', () => { + const deviceVersion = core.getVersions('CPU'); + assert.strictEqual(typeof deviceVersion, 'object'); + assert.strictEqual(typeof deviceVersion.CPU, 'object'); + assert.strictEqual(typeof deviceVersion.CPU.buildNumber, 'string'); + assert.strictEqual(typeof deviceVersion.CPU.description, 'string'); + }); -it('compileModel.createInferRequest()', () => { - const ir = compiledModel.createInferRequest(); - assert.ok(ir instanceof ov.InferRequest); -}); + it('getVersions() throws if no arguments are passed into the function', () => { + assert.throws( + () => core.getVersions(), + { message: 'getVersions() method expects 1 argument of string type.' } + ); + }); -describe('Core.compileModelSync()', () => { - const tput = { 'PERFORMANCE_HINT': 'THROUGHPUT' }; + it('getVersions() throws if non string coercable arguments are passed into the function', () => { + assert.throws( + () => core.getVersions({ deviceName: 'CPU' }), + { message: 'The argument in getVersions() method must be a string or convertible to a string.' } + ); + }); - it('compileModelSync(model:Model, deviceName: string, config: {}) ', () => { - const cm = core.compileModelSync(model, 'CPU', tput); - assert.deepStrictEqual(cm.output(0).shape, [1, 10]); }); - it('compileModelSync(model:model_path, deviceName: string, config: {}) ', () => { - const cm = core.compileModelSync(testXml, 'CPU', tput); - assert.equal(cm.inputs.length, 1); + it('CompiledModel type', () => { + assert.ok(compiledModel instanceof ov.CompiledModel); }); - it('compileModelSync(model:model_path, deviceName: string) ', () => { - const cm = core.compileModelSync(testXml, 'CPU'); - assert.deepStrictEqual(cm.output(0).shape, [1, 10]); + it('compileModel.createInferRequest()', () => { + const ir = compiledModel.createInferRequest(); + assert.ok(ir instanceof ov.InferRequest); }); - it('compileModelSync(model, device, config) throws when config is a string', () => { - const expectedMsg = ("'compileModelSync' method called with incorrect parameters.\n" + + describe('Core.compileModelSync()', () => { + const tput = { 'PERFORMANCE_HINT': 'THROUGHPUT' }; + + it('compileModelSync(model:Model, deviceName: string, config: {}) ', () => { + const cm = core.compileModelSync(model, 'CPU', tput); + assert.deepStrictEqual(cm.output(0).shape, [1, 10]); + }); + + it('compileModelSync(model:model_path, deviceName: string, config: {}) ', () => { + const cm = core.compileModelSync(testXml, 'CPU', tput); + assert.equal(cm.inputs.length, 1); + }); + + it('compileModelSync(model:model_path, deviceName: string) ', () => { + const cm = core.compileModelSync(testXml, 'CPU'); + assert.deepStrictEqual(cm.output(0).shape, [1, 10]); + }); + + it('compileModelSync(model, device, config) throws when config is a string', () => { + const expectedMsg = ('\'compileModelSync\' method called with incorrect parameters.\n' + 'Provided signature: (object, string, string) \n' + 'Allowed signatures:\n' + '- (string, string)\n' + @@ -83,21 +88,21 @@ describe('Core.compileModelSync()', () => { '- (string, string, object)\n' + '- (Model, string, object)\n').replace(/[()]/g, '\\$&'); - assert.throws( - () => core.compileModelSync(model, 'CPU', 'string'), - new RegExp(expectedMsg), - ); - }); + assert.throws( + () => core.compileModelSync(model, 'CPU', 'string'), + new RegExp(expectedMsg), + ); + }); - it('compileModelSync(model, device, config) throws when config value is not a string', () => { - assert.throws( - () => core.compileModelSync(model, 'CPU', { 'PERFORMANCE_HINT': tput }), - /Cannot convert to ov::Any/ - ); - }); + it('compileModelSync(model, device, config) throws when config value is not a string', () => { + assert.throws( + () => core.compileModelSync(model, 'CPU', { 'PERFORMANCE_HINT': tput }), + /Cannot convert to ov::Any/ + ); + }); - it('compileModelSync(model) throws if the number of arguments is invalid', () => { - const expectedMsg = ("'compileModelSync' method called with incorrect parameters.\n" + + it('compileModelSync(model) throws if the number of arguments is invalid', () => { + const expectedMsg = ('\'compileModelSync\' method called with incorrect parameters.\n' + 'Provided signature: (object) \n' + 'Allowed signatures:\n' + '- (string, string)\n' + @@ -105,228 +110,230 @@ describe('Core.compileModelSync()', () => { '- (string, string, object)\n' + '- (Model, string, object)\n').replace(/[()]/g, '\\$&'); - assert.throws( - () => core.compileModelSync(model), - new RegExp(expectedMsg), - ); - }); -}); - -describe('Core.compileModel()', () => { - const tput = { 'PERFORMANCE_HINT': 'THROUGHPUT' }; - - it('compileModel(model:Model, deviceName: string, config: {}) ', () => { - core.compileModel(model, 'CPU', tput).then(cm => { - assert.deepStrictEqual(cm.output(0).shape, [1, 10]); + assert.throws( + () => core.compileModelSync(model), + new RegExp(expectedMsg), + ); }); - }); - it('compileModel(model:model_path, deviceName: string, config: {}) ', () => { - core.compileModel(testXml, 'CPU', tput).then(cm => { - assert.equal(cm.inputs.length, 1); - }); + describe('Core.compileModel()', () => { + const tput = { 'PERFORMANCE_HINT': 'THROUGHPUT' }; - }); + it('compileModel(model:Model, deviceName: string, config: {}) ', () => { + core.compileModel(model, 'CPU', tput).then(cm => { + assert.deepStrictEqual(cm.output(0).shape, [1, 10]); + }); - it('compileModel(model:model_path, deviceName: string) ', () => { - core.compileModel(testXml, 'CPU').then(cm => { - assert.deepStrictEqual(cm.output(0).shape, [1, 10]); }); - }); - - it('compileModel(model, device, config) throws when config isn\'t an object', () => { - assert.throws( - () => core.compileModel(model, 'CPU', 'string').then(), - "Argument #3 must be an Object." - ); - }); - - it('compileModel(model, device, config) throws when config value is not a string', () => { - assert.throws( - () => core.compileModel(model, 'CPU', { 'PERFORMANCE_HINT': tput }).then(), - /Cannot convert to ov::Any/ - ); - }); - - it('compileModel(model) throws if the number of arguments is invalid', () => { - assert.throws( - () => core.compileModel(model).then(), - /Invalid number of arguments/ - ); - }); - -}); + it('compileModel(model:model_path, deviceName: string, config: {}) ', () => { + core.compileModel(testXml, 'CPU', tput).then(cm => { + assert.equal(cm.inputs.length, 1); + }); -describe('Output class', () => { + }); - it('Output type', () => { - assert.ok(model.output() instanceof ov.Output); - }); + it('compileModel(model:model_path, deviceName: string) ', () => { + core.compileModel(testXml, 'CPU').then(cm => { + assert.deepStrictEqual(cm.output(0).shape, [1, 10]); + }); - it('ConstOutput type', () => { - assert.ok(compiledModel.output() instanceof ov.ConstOutput); - }); + }); - modelLike.forEach(([obj]) => { - it('Output getters and properties', () => { - assert.strictEqual(obj.outputs.length, 1); - // tests for an obj with one output - assert.strictEqual(obj.output().toString(), 'fc_out'); - assert.strictEqual(obj.output(0).toString(), 'fc_out'); - assert.strictEqual(obj.output('fc_out').toString(), 'fc_out'); - assert.deepStrictEqual(obj.output(0).shape, [1, 10]); - assert.deepStrictEqual(obj.output(0).getShape(), [1, 10]); - assert.strictEqual(obj.output().getAnyName(), 'fc_out'); - assert.strictEqual(obj.output().anyName, 'fc_out'); + it('compileModel(model, device, config) throws when config isn\'t an object', () => { + assert.throws( + () => core.compileModel(model, 'CPU', 'string').then(), + 'Argument #3 must be an Object.' + ); }); - }); -}); + it('compileModel(model, device, config) throws when config value is not a string', () => { + assert.throws( + () => core.compileModel(model, 'CPU', { 'PERFORMANCE_HINT': tput }).then(), + /Cannot convert to ov::Any/ + ); + }); -describe('Input class for ov::Input', () => { + it('compileModel(model) throws if the number of arguments is invalid', () => { + assert.throws( + () => core.compileModel(model).then(), + /Invalid number of arguments/ + ); + }); - it('Output type', () => { - assert.ok(model.input() instanceof ov.Output); }); - it('ConstOutput type', () => { - assert.ok(compiledModel.input() instanceof ov.ConstOutput); - }); + describe('Output class', () => { - modelLike.forEach(([obj]) => { - it('input() is typeof object', () => { - assert.strictEqual(typeof obj.input(), 'object'); + it('Output type', () => { + assert.ok(model.output() instanceof ov.Output); }); - it('inputs property', () => { - assert.strictEqual(obj.inputs.length, 1); + it('ConstOutput type', () => { + assert.ok(compiledModel.output() instanceof ov.ConstOutput); }); - it('input().toString()', () => { - assert.strictEqual(obj.input().toString(), 'data'); + modelLike.forEach(([obj]) => { + it('Output getters and properties', () => { + assert.strictEqual(obj.outputs.length, 1); + // tests for an obj with one output + assert.strictEqual(obj.output().toString(), 'fc_out'); + assert.strictEqual(obj.output(0).toString(), 'fc_out'); + assert.strictEqual(obj.output('fc_out').toString(), 'fc_out'); + assert.deepStrictEqual(obj.output(0).shape, [1, 10]); + assert.deepStrictEqual(obj.output(0).getShape(), [1, 10]); + assert.strictEqual(obj.output().getAnyName(), 'fc_out'); + assert.strictEqual(obj.output().anyName, 'fc_out'); + }); }); - it('input(idx: number).ToString() method', () => { - assert.strictEqual(obj.input(0).toString(), 'data'); - }); + }); - it('input(tensorName: string).ToString() method', () => { - assert.strictEqual(obj.input('data').toString(), 'data'); - }); + describe('Input class for ov::Input', () => { - it('input().getAnyName() and anyName', () => { - assert.strictEqual(obj.input().getAnyName(), 'data'); - assert.strictEqual(obj.input().anyName, 'data'); + it('Output type', () => { + assert.ok(model.input() instanceof ov.Output); }); - it('input(idx).shape property with dimensions', () => { - assert.deepStrictEqual(obj.input(0).shape, [1, 3, 32, 32]); - assert.deepStrictEqual(obj.input(0).getShape(), [1, 3, 32, 32]); + it('ConstOutput type', () => { + assert.ok(compiledModel.input() instanceof ov.ConstOutput); }); - }); -}); + modelLike.forEach(([obj]) => { + it('input() is typeof object', () => { + assert.strictEqual(typeof obj.input(), 'object'); + }); -describe('Test exportModel()/importModel()', () => { - const userStream = compiledModel.exportModelSync(); - const epsilon = 0.5; - const tensor = Float32Array.from({ length: 3072 }, () => (Math.random() + epsilon)); - const inferRequest = compiledModel.createInferRequest(); - const res1 = inferRequest.infer([tensor]); + it('inputs property', () => { + assert.strictEqual(obj.inputs.length, 1); + }); - it('Test importModelSync(stream, device)', () => { - const newCompiled = core.importModelSync(userStream, 'CPU'); - const newInferRequest = newCompiled.createInferRequest(); - const res2 = newInferRequest.infer([tensor]); + it('input().toString()', () => { + assert.strictEqual(obj.input().toString(), 'data'); + }); - assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]); - }); + it('input(idx: number).ToString() method', () => { + assert.strictEqual(obj.input(0).toString(), 'data'); + }); - it('Test importModelSync(stream, device, config)', () => { - const newCompiled = core.importModelSync(userStream, 'CPU', { 'NUM_STREAMS': 1 }); - const newInferRequest = newCompiled.createInferRequest(); - const res2 = newInferRequest.infer([tensor]); + it('input(tensorName: string).ToString() method', () => { + assert.strictEqual(obj.input('data').toString(), 'data'); + }); - assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]); - }); + it('input().getAnyName() and anyName', () => { + assert.strictEqual(obj.input().getAnyName(), 'data'); + assert.strictEqual(obj.input().anyName, 'data'); + }); - it('Test importModelSync(stream, device) throws', () => { - assert.throws( - () => core.importModelSync(epsilon, 'CPU'), - /The first argument must be of type Buffer./ - ); - }); + it('input(idx).shape property with dimensions', () => { + assert.deepStrictEqual(obj.input(0).shape, [1, 3, 32, 32]); + assert.deepStrictEqual(obj.input(0).getShape(), [1, 3, 32, 32]); + }); + }); - it('Test importModelSync(stream, device) throws', () => { - assert.throws( - () => core.importModelSync(userStream, tensor), - /The second argument must be of type String./ - ); - }); - it('Test importModelSync(stream, device, config: tensor) throws', () => { - assert.throws( - () => core.importModelSync(userStream, 'CPU', tensor), - /NotFound: Unsupported property 0 by CPU plugin./ - ); }); - it('Test importModelSync(stream, device, config: string) throws', () => { - const testString = 'test'; - assert.throws( - () => core.importModelSync(userStream, 'CPU', testString), - /Passed Napi::Value must be an object./ - ); - }); + describe('Test exportModel()/importModel()', () => { + const userStream = compiledModel.exportModelSync(); + const epsilon = 0.5; + const tensor = Float32Array.from({ length: 3072 }, () => (Math.random() + epsilon)); + const inferRequest = compiledModel.createInferRequest(); + const res1 = inferRequest.infer([tensor]); - it('Test importModelSync(stream, device, config: unsupported property) \ - throws', () => { - const tmpDir = '/tmp'; - assert.throws( - () => core.importModelSync(userStream, 'CPU', { 'CACHE_DIR': tmpDir }), - /Unsupported property CACHE_DIR by CPU plugin./ - ); - }); + it('Test importModelSync(stream, device)', () => { + const newCompiled = core.importModelSync(userStream, 'CPU'); + const newInferRequest = newCompiled.createInferRequest(); + const res2 = newInferRequest.infer([tensor]); - it('Test importModel(stream, device)', () => { - core.importModel(userStream, 'CPU').then(newCompiled => { + assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]); + }); + + it('Test importModelSync(stream, device, config)', () => { + const newCompiled = core.importModelSync(userStream, 'CPU', { 'NUM_STREAMS': 1 }); const newInferRequest = newCompiled.createInferRequest(); const res2 = newInferRequest.infer([tensor]); + assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]); }); - }); - it('Test importModel(stream, device, config)', () => { - core.importModel(userStream, 'CPU', { 'NUM_STREAMS': 1 }).then( - newCompiled => { + it('Test importModelSync(stream, device) throws', () => { + assert.throws( + () => core.importModelSync(epsilon, 'CPU'), + /The first argument must be of type Buffer./ + ); + }); + + it('Test importModelSync(stream, device) throws', () => { + assert.throws( + () => core.importModelSync(userStream, tensor), + /The second argument must be of type String./ + ); + }); + it('Test importModelSync(stream, device, config: tensor) throws', () => { + assert.throws( + () => core.importModelSync(userStream, 'CPU', tensor), + /NotFound: Unsupported property 0 by CPU plugin./ + ); + }); + + it('Test importModelSync(stream, device, config: string) throws', () => { + const testString = 'test'; + assert.throws( + () => core.importModelSync(userStream, 'CPU', testString), + /Passed Napi::Value must be an object./ + ); + }); + + it('Test importModelSync(stream, device, config: unsupported property) \ + throws', () => { + const tmpDir = '/tmp'; + assert.throws( + () => core.importModelSync(userStream, 'CPU', { 'CACHE_DIR': tmpDir }), + /Unsupported property CACHE_DIR by CPU plugin./ + ); + }); + + it('Test importModel(stream, device)', () => { + core.importModel(userStream, 'CPU').then(newCompiled => { const newInferRequest = newCompiled.createInferRequest(); const res2 = newInferRequest.infer([tensor]); - assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]); }); - }); + }); - it('Test importModel(stream, device) throws', () => { - assert.throws( - () => core.importModel(epsilon, 'CPU').then(), - /'importModel' method called with incorrect parameters./ - ); - }); + it('Test importModel(stream, device, config)', () => { + core.importModel(userStream, 'CPU', { 'NUM_STREAMS': 1 }).then( + newCompiled => { + const newInferRequest = newCompiled.createInferRequest(); + const res2 = newInferRequest.infer([tensor]); - it('Test importModel(stream, device) throws', () => { - assert.throws( - () => core.importModel(userStream, tensor).then(), - /'importModel' method called with incorrect parameters./ - ); - }); + assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]); + }); + }); + + it('Test importModel(stream, device) throws', () => { + assert.throws( + () => core.importModel(epsilon, 'CPU').then(), + /'importModel' method called with incorrect parameters./ + ); + }); + + it('Test importModel(stream, device) throws', () => { + assert.throws( + () => core.importModel(userStream, tensor).then(), + /'importModel' method called with incorrect parameters./ + ); + }); + + it('Test importModel(stream, device, config: string) throws', () => { + const testString = 'test'; + assert.throws( + () => core.importModel(userStream, 'CPU', testString).then(), + /'importModel' method called with incorrect parameters./ + ); + }); - it('Test importModel(stream, device, config: string) throws', () => { - const testString = 'test'; - assert.throws( - () => core.importModel(userStream, 'CPU', testString).then(), - /'importModel' method called with incorrect parameters./ - ); }); }); diff --git a/src/bindings/js/node/tests/unit/compiled_model.test.js b/src/bindings/js/node/tests/unit/compiled_model.test.js index 6b33296131b04d..2d1169b0783e2e 100644 --- a/src/bindings/js/node/tests/unit/compiled_model.test.js +++ b/src/bindings/js/node/tests/unit/compiled_model.test.js @@ -4,17 +4,21 @@ const { addon: ov } = require('../..'); const assert = require('assert'); -const { describe, it } = require('node:test'); -const { getModelPath } = require('./utils.js'); +const { describe, it, before } = require('node:test'); +const { testModels, getModelPath, isModelAvailable } = require('./utils.js'); -const testXml = getModelPath().xml; -const core = new ov.Core(); -const properties = { - "AUTO_BATCH_TIMEOUT": '1' -}; -const compiledModel = core.compileModelSync(testXml, 'BATCH:CPU', properties); +describe('ov.CompiledModel tests', () => { -describe('setProperty() / getProperty()', () => { + before(async () => { + await isModelAvailable(testModels.testModelFP32); + }); + + const testXml = getModelPath().xml; + const core = new ov.Core(); + const properties = { + 'AUTO_BATCH_TIMEOUT': '1', + }; + const compiledModel = core.compileModelSync(testXml, 'BATCH:CPU', properties); describe('getProperty()', () => { it('returns the value of property from compiled model', () => { @@ -26,7 +30,7 @@ describe('setProperty() / getProperty()', () => { /'getProperty' method called with incorrect parameters/ ); }); - it('throws an error when called with property name that does not exists', ()=>{ + it('throws an error when called with property name that does not exists', () => { assert.throws( () => compiledModel.getProperty('PROPERTY_THAT_DOES_NOT_EXIST') ); @@ -35,10 +39,10 @@ describe('setProperty() / getProperty()', () => { describe('setProperty()', () => { it('sets a properties for compiled model', () => { - properties["AUTO_BATCH_TIMEOUT"] = '1000'; + properties['AUTO_BATCH_TIMEOUT'] = '1000'; assert.doesNotThrow(() => compiledModel.setProperty(properties)); }); - + it('throws an error when called without an object argument', () => { assert.throws( () => compiledModel.setProperty(), @@ -47,30 +51,30 @@ describe('setProperty() / getProperty()', () => { }); it('throws an error when called with wrong argument', () => { assert.throws( - () => compiledModel.setProperty(123), - /'setProperty' method called with incorrect parameters/ + () => compiledModel.setProperty(123), + /'setProperty' method called with incorrect parameters/ ); }); - + it('throws an error when called with multiple arguments', () => { assert.throws( - () => compiledModel.setProperty({"PERFORMANCE_HINT": "THROUGHPUT"}, {"NUM_STREAMS": "AUTO"}), + () => compiledModel.setProperty({'PERFORMANCE_HINT': 'THROUGHPUT'}, {'NUM_STREAMS': 'AUTO'}), /'setProperty' method called with incorrect parameters/ ); }); - + it('returns the set property of the compiled model', () => { - properties["AUTO_BATCH_TIMEOUT"] = '123'; + properties['AUTO_BATCH_TIMEOUT'] = '123'; compiledModel.setProperty(properties); assert.strictEqual(compiledModel.getProperty('AUTO_BATCH_TIMEOUT'), 123); }); - + it('retains the last set property when set multiple times', () => { - compiledModel.setProperty({"AUTO_BATCH_TIMEOUT": '321'}); + compiledModel.setProperty({'AUTO_BATCH_TIMEOUT': '321'}); compiledModel.setProperty({'AUTO_BATCH_TIMEOUT': '132'}); assert.strictEqual(compiledModel.getProperty('AUTO_BATCH_TIMEOUT'), 132); }); - + it('allows to pass empty object', () => { assert.doesNotThrow(() => compiledModel.setProperty({})); }); diff --git a/src/bindings/js/node/tests/unit/core.test.js b/src/bindings/js/node/tests/unit/core.test.js index 2c70428b66b7c1..69d8720d97a380 100644 --- a/src/bindings/js/node/tests/unit/core.test.js +++ b/src/bindings/js/node/tests/unit/core.test.js @@ -6,64 +6,67 @@ const { addon: ov } = require('../..'); const assert = require('assert'); const { describe, it } = require('node:test'); -const core = new ov.Core(); +describe('ov.Core tests', () => { -it('Core.setProperty()', () => { - const tmpDir = '/tmp'; + const core = new ov.Core(); - core.setProperty({ 'CACHE_DIR': tmpDir }); + it('Core.setProperty()', () => { + const tmpDir = '/tmp'; - const cacheDir = core.getProperty('CACHE_DIR'); + core.setProperty({ 'CACHE_DIR': tmpDir }); - assert.equal(cacheDir, tmpDir); -}); + const cacheDir = core.getProperty('CACHE_DIR'); -it('Core.setProperty(\'CPU\')', () => { - const tmpDir = '/tmp'; + assert.equal(cacheDir, tmpDir); + }); - core.setProperty('CPU', { 'CACHE_DIR': tmpDir }); + it('Core.setProperty(\'CPU\')', () => { + const tmpDir = '/tmp'; - const cacheDir = core.getProperty('CPU', 'CACHE_DIR'); + core.setProperty('CPU', { 'CACHE_DIR': tmpDir }); - assert.equal(cacheDir, tmpDir); -}); + const cacheDir = core.getProperty('CPU', 'CACHE_DIR'); -it('Core.getProperty(\'CPU\', \'SUPPORTED_PROPERTIES\') is Array', () => { - const supportedPropertiesArray = core.getProperty('CPU', 'SUPPORTED_PROPERTIES'); + assert.equal(cacheDir, tmpDir); + }); - assert.ok(Array.isArray(supportedPropertiesArray)); -}); + it('Core.getProperty(\'CPU\', \'SUPPORTED_PROPERTIES\') is Array', () => { + const supportedPropertiesArray = core.getProperty('CPU', 'SUPPORTED_PROPERTIES'); -it('Core.setProperty(\'CPU\', { \'NUM_STREAMS\': 5 })', () => { - const streams = 5; + assert.ok(Array.isArray(supportedPropertiesArray)); + }); - core.setProperty('CPU', { 'NUM_STREAMS': streams }); - const result = core.getProperty('CPU', 'NUM_STREAMS'); + it('Core.setProperty(\'CPU\', { \'NUM_STREAMS\': 5 })', () => { + const streams = 5; - assert.equal(result, streams); -}); + core.setProperty('CPU', { 'NUM_STREAMS': streams }); + const result = core.getProperty('CPU', 'NUM_STREAMS'); -it('Core.setProperty(\'CPU\', { \'INFERENCE_NUM_THREADS\': 3 })', () => { - const threads = 3; + assert.equal(result, streams); + }); - core.setProperty('CPU', { 'INFERENCE_NUM_THREADS': threads }); - const result = core.getProperty('CPU', 'INFERENCE_NUM_THREADS'); + it('Core.setProperty(\'CPU\', { \'INFERENCE_NUM_THREADS\': 3 })', () => { + const threads = 3; - assert.equal(result, threads); -}); + core.setProperty('CPU', { 'INFERENCE_NUM_THREADS': threads }); + const result = core.getProperty('CPU', 'INFERENCE_NUM_THREADS'); -it('Core.addExtension() with empty parameters', () => { - assert.throws( - () => core.addExtension(), - /addExtension method applies one argument of string type/ - ); -}); + assert.equal(result, threads); + }); + + it('Core.addExtension() with empty parameters', () => { + assert.throws( + () => core.addExtension(), + /addExtension method applies one argument of string type/ + ); + }); -it('Core.addExtension(\'not_exists\') with non-existed library', () => { - const notExistsExt = 'not_exists'; + it('Core.addExtension(\'not_exists\') with non-existed library', () => { + const notExistsExt = 'not_exists'; - assert.throws( - () => core.addExtension(notExistsExt), - /Cannot load library 'not_exists'/ - ); + assert.throws( + () => core.addExtension(notExistsExt), + /Cannot load library 'not_exists'/ + ); + }); }); diff --git a/src/bindings/js/node/tests/unit/infer_request.test.js b/src/bindings/js/node/tests/unit/infer_request.test.js index bdc54fc4f4d086..8dc16cd195430a 100644 --- a/src/bindings/js/node/tests/unit/infer_request.test.js +++ b/src/bindings/js/node/tests/unit/infer_request.test.js @@ -4,277 +4,286 @@ const { addon: ov } = require('../..'); const assert = require('assert'); -const { describe, it } = require('node:test'); -const { getModelPath } = require('./utils.js'); +const { describe, it, before } = require('node:test'); +const { testModels, isModelAvailable, getModelPath } = require('./utils.js'); const epsilon = 0.5; // To avoid very small numbers const testXml = getModelPath().xml; -const core = new ov.Core(); -const model = core.readModelSync(testXml); -const compiledModel = core.compileModelSync(model, 'CPU'); - -const inferRequest = compiledModel.createInferRequest(); -const inferRequestAsync = compiledModel.createInferRequest(); - -const tensorData = Float32Array.from({ length: 3072 }, () => (Math.random() + epsilon)); -const tensor = new ov.Tensor( - ov.element.f32, - [1, 3, 32, 32], - tensorData, -); -const resTensor = new ov.Tensor( - ov.element.f32, - [1, 10], - tensorData.slice(-10), -); - -const tensorLike = [[tensor], - [tensorData]]; - -describe('InferRequest', () => { - - tensorLike.forEach(([tl]) => { - const result = inferRequest.infer({ data: tl }); - const label = tl instanceof Float32Array ? 'TypedArray[]' : 'Tensor[]'; - it(`Test infer(inputData: { [inputName: string]: ${label} })`, () => { - assert.deepStrictEqual(Object.keys(result), ['fc_out']); - assert.deepStrictEqual(result['fc_out'].data.length, 10); - }); - }); - tensorLike.forEach(([tl]) => { - const result = inferRequest.infer([tl]); - const label = tl instanceof Float32Array ? 'TypedArray[]' : 'Tensor[]'; - it(`Test infer(inputData: ${label})`, () => { - assert.deepStrictEqual(Object.keys(result), ['fc_out']); - assert.deepStrictEqual(result['fc_out'].data.length, 10); - }); +describe('ov.InferRequest tests', () => { + before(async () => { + await isModelAvailable(testModels.testModelFP32); }); - it('Test infer(TypedArray) throws', () => { - assert.throws( - () => inferRequest.infer(tensorData), - {message: /TypedArray cannot be passed directly into infer\(\) method./}); - }); + const core = new ov.Core(); + const model = core.readModelSync(testXml); + const compiledModel = core.compileModelSync(model, 'CPU'); - const buffer = new ArrayBuffer(tensorData.length); - const inputMessagePairs = [ - ['string', 'Cannot create a tensor from the passed Napi::Value.'], - [tensorData.slice(-10), 'Memory allocated using shape and element::type mismatch passed data\'s size'], - [new Float32Array(buffer, 4), 'TypedArray.byteOffset has to be equal to zero.'], - [{}, /Invalid argument/], // Test for object that is not Tensor - ]; + const inferRequest = compiledModel.createInferRequest(); + const inferRequestAsync = compiledModel.createInferRequest(); + + const tensorData = Float32Array.from({ length: 3072 }, () => (Math.random() + epsilon)); + const tensor = new ov.Tensor( + ov.element.f32, + [1, 3, 32, 32], + tensorData, + ); + const resTensor = new ov.Tensor( + ov.element.f32, + [1, 10], + tensorData.slice(-10), + ); + + const tensorLike = [[tensor], + [tensorData]]; + + describe('InferRequest', () => { + + tensorLike.forEach(([tl]) => { + const result = inferRequest.infer({ data: tl }); + const label = tl instanceof Float32Array ? 'TypedArray[]' : 'Tensor[]'; + it(`Test infer(inputData: { [inputName: string]: ${label} })`, () => { + assert.deepStrictEqual(Object.keys(result), ['fc_out']); + assert.deepStrictEqual(result['fc_out'].data.length, 10); + }); + }); + + tensorLike.forEach(([tl]) => { + const result = inferRequest.infer([tl]); + const label = tl instanceof Float32Array ? 'TypedArray[]' : 'Tensor[]'; + it(`Test infer(inputData: ${label})`, () => { + assert.deepStrictEqual(Object.keys(result), ['fc_out']); + assert.deepStrictEqual(result['fc_out'].data.length, 10); + }); - inputMessagePairs.forEach( ([tl, msg]) => { - it(`Test infer([data]) throws ${msg}`, () => { - assert.throws( - () => inferRequest.infer([tl]), - {message: new RegExp(msg)}); }); - it(`Test infer({ data: tl}) throws ${msg}`, () => { + + it('Test infer(TypedArray) throws', () => { assert.throws( - () => inferRequest.infer({data: tl}), - {message: new RegExp(msg)}); + () => inferRequest.infer(tensorData), + {message: /TypedArray cannot be passed directly into infer\(\) method./}); }); - }); - it('Test inferAsync(inputData: { [inputName: string]: Tensor })', () => { - inferRequestAsync.inferAsync({ data: tensor }).then(result => { - assert.ok(result['fc_out'] instanceof ov.Tensor); - assert.deepStrictEqual(Object.keys(result), ['fc_out']); - assert.deepStrictEqual(result['fc_out'].data.length, 10);} - ); - }); + const buffer = new ArrayBuffer(tensorData.length); + const inputMessagePairs = [ + ['string', 'Cannot create a tensor from the passed Napi::Value.'], + [tensorData.slice(-10), 'Memory allocated using shape and element::type mismatch passed data\'s size'], + [new Float32Array(buffer, 4), 'TypedArray.byteOffset has to be equal to zero.'], + [{}, /Invalid argument/], // Test for object that is not Tensor + ]; + + inputMessagePairs.forEach( ([tl, msg]) => { + it(`Test infer([data]) throws ${msg}`, () => { + assert.throws( + () => inferRequest.infer([tl]), + {message: new RegExp(msg)}); + }); + it(`Test infer({ data: tl}) throws ${msg}`, () => { + assert.throws( + () => inferRequest.infer({data: tl}), + {message: new RegExp(msg)}); + }); + }); - it('Test inferAsync(inputData: Tensor[])', () => { - inferRequestAsync.inferAsync([ tensor ]).then(result => { - assert.ok(result['fc_out'] instanceof ov.Tensor); - assert.deepStrictEqual(Object.keys(result), ['fc_out']); - assert.deepStrictEqual(result['fc_out'].data.length, 10); + it('Test inferAsync(inputData: { [inputName: string]: Tensor })', () => { + inferRequestAsync.inferAsync({ data: tensor }).then(result => { + assert.ok(result['fc_out'] instanceof ov.Tensor); + assert.deepStrictEqual(Object.keys(result), ['fc_out']); + assert.deepStrictEqual(result['fc_out'].data.length, 10);} + ); }); - }); - it('Test inferAsync([data]) throws: Cannot create a tensor from the passed Napi::Value.', () => { - assert.throws( - () => inferRequestAsync.inferAsync(['string']).then(), - /Cannot create a tensor from the passed Napi::Value./ - ); - }); + it('Test inferAsync(inputData: Tensor[])', () => { + inferRequestAsync.inferAsync([ tensor ]).then(result => { + assert.ok(result['fc_out'] instanceof ov.Tensor); + assert.deepStrictEqual(Object.keys(result), ['fc_out']); + assert.deepStrictEqual(result['fc_out'].data.length, 10); + }); + }); - it('Test inferAsync({ data: "string"}) throws: Cannot create a tensor from the passed Napi::Value.', () => { - assert.throws( - () => inferRequestAsync.inferAsync({data: 'string'}).then(), - /Cannot create a tensor from the passed Napi::Value./ - ); - }); + it('Test inferAsync([data]) throws: Cannot create a tensor from the passed Napi::Value.', () => { + assert.throws( + () => inferRequestAsync.inferAsync(['string']).then(), + /Cannot create a tensor from the passed Napi::Value./ + ); + }); - it('Test setInputTensor(tensor)', () => { - inferRequest.setInputTensor(tensor); - const t1 = inferRequest.getInputTensor(); - assert.deepStrictEqual(tensor.data[0], t1.data[0]); - }); + it('Test inferAsync({ data: "string"}) throws: Cannot create a tensor from the passed Napi::Value.', () => { + assert.throws( + () => inferRequestAsync.inferAsync({data: 'string'}).then(), + /Cannot create a tensor from the passed Napi::Value./ + ); + }); - it('Test setInputTensor(object) throws when passed object is not a Tensor.', () => { - assert.throws( - () => inferRequest.setInputTensor({}), - {message: /Argument #[0-9]+ must be a Tensor./} - ); - }); + it('Test setInputTensor(tensor)', () => { + inferRequest.setInputTensor(tensor); + const t1 = inferRequest.getInputTensor(); + assert.deepStrictEqual(tensor.data[0], t1.data[0]); + }); - it('Test setInputTensor(idx, tensor)', () => { - inferRequest.setInputTensor(0, tensor); - const t1 = inferRequest.getInputTensor(); - assert.deepStrictEqual(tensor.data[0], t1.data[0]); - }); + it('Test setInputTensor(object) throws when passed object is not a Tensor.', () => { + assert.throws( + () => inferRequest.setInputTensor({}), + {message: /Argument #[0-9]+ must be a Tensor./} + ); + }); - it('Test setInputTensor(idx, tensor) throws', () => { - const testIdx = 10; - assert.throws ( - () => inferRequest.setInputTensor(testIdx, tensor), - {message: /Input port for index [0-9]+ was not found!/} - ); - }); + it('Test setInputTensor(idx, tensor)', () => { + inferRequest.setInputTensor(0, tensor); + const t1 = inferRequest.getInputTensor(); + assert.deepStrictEqual(tensor.data[0], t1.data[0]); + }); - it('Test setInputTensor(idx, object) throws when passed object is not a Tensor.', () => { - assert.throws( - () => inferRequest.setInputTensor(0, {}), - {message: /Argument #[0-9]+ must be a Tensor./} - ); - }); + it('Test setInputTensor(idx, tensor) throws', () => { + const testIdx = 10; + assert.throws ( + () => inferRequest.setInputTensor(testIdx, tensor), + {message: /Input port for index [0-9]+ was not found!/} + ); + }); - it('Test setInputTensor(tensor, tensor) throws', () => { - assert.throws( - () => inferRequest.setInputTensor(resTensor, tensor), - {message: / invalid argument./} - ); - }); + it('Test setInputTensor(idx, object) throws when passed object is not a Tensor.', () => { + assert.throws( + () => inferRequest.setInputTensor(0, {}), + {message: /Argument #[0-9]+ must be a Tensor./} + ); + }); - it('Test setOutputTensor(tensor)', () => { - inferRequest.setOutputTensor(resTensor); - const res2 = inferRequest.getOutputTensor(); - assert.deepStrictEqual(resTensor.data[0], res2.data[0]); - }); + it('Test setInputTensor(tensor, tensor) throws', () => { + assert.throws( + () => inferRequest.setInputTensor(resTensor, tensor), + {message: / invalid argument./} + ); + }); - it('Test setOutputTensor(object) throws when passed object is not a Tensor.', () => { - assert.throws( - () => inferRequest.setOutputTensor({}), - {message: /Argument #[0-9]+ must be a Tensor./} - ); - }); + it('Test setOutputTensor(tensor)', () => { + inferRequest.setOutputTensor(resTensor); + const res2 = inferRequest.getOutputTensor(); + assert.deepStrictEqual(resTensor.data[0], res2.data[0]); + }); - it('Test setOutputTensor(idx, tensor) throws', () => { - const testIdx = 10; - assert.throws ( - () => inferRequest.setOutputTensor(testIdx, tensor), - {message: /Output port for index [0-9]+ was not found!/} - ); - }); + it('Test setOutputTensor(object) throws when passed object is not a Tensor.', () => { + assert.throws( + () => inferRequest.setOutputTensor({}), + {message: /Argument #[0-9]+ must be a Tensor./} + ); + }); - it('Test setOutputTensor(idx, tensor)', () => { - inferRequest.setOutputTensor(0, resTensor); - const res2 = inferRequest.getOutputTensor(); - assert.deepStrictEqual(resTensor.data[0], res2.data[0]); - }); + it('Test setOutputTensor(idx, tensor) throws', () => { + const testIdx = 10; + assert.throws ( + () => inferRequest.setOutputTensor(testIdx, tensor), + {message: /Output port for index [0-9]+ was not found!/} + ); + }); - it('Test setOutputTensor(idx, tensor) throws when passed object is not a Tensor.', () => { - assert.throws( - () => inferRequest.setOutputTensor(0, {}), - {message: /Argument #[0-9]+ must be a Tensor./} - ); - }); + it('Test setOutputTensor(idx, tensor)', () => { + inferRequest.setOutputTensor(0, resTensor); + const res2 = inferRequest.getOutputTensor(); + assert.deepStrictEqual(resTensor.data[0], res2.data[0]); + }); - it('Test setOutputTensor() - pass two tensors', () => { - assert.throws( - () => inferRequest.setOutputTensor(resTensor, tensor), - {message: / invalid argument./}); - }); + it('Test setOutputTensor(idx, tensor) throws when passed object is not a Tensor.', () => { + assert.throws( + () => inferRequest.setOutputTensor(0, {}), + {message: /Argument #[0-9]+ must be a Tensor./} + ); + }); - it('Test setTensor(string, tensor)', () => { - inferRequest.setTensor('fc_out', resTensor); - const res2 = inferRequest.getTensor('fc_out'); - assert.ok(res2 instanceof ov.Tensor); - assert.deepStrictEqual(resTensor.data[0], res2.data[0]); - }); + it('Test setOutputTensor() - pass two tensors', () => { + assert.throws( + () => inferRequest.setOutputTensor(resTensor, tensor), + {message: / invalid argument./}); + }); - it('Test setTensor(string, object) - throws', () => { - const testName = 'testName'; - assert.throws( - () => inferRequest.setTensor(testName, tensor), - {message: /Port for tensor name testName was not found./}); - }); + it('Test setTensor(string, tensor)', () => { + inferRequest.setTensor('fc_out', resTensor); + const res2 = inferRequest.getTensor('fc_out'); + assert.ok(res2 instanceof ov.Tensor); + assert.deepStrictEqual(resTensor.data[0], res2.data[0]); + }); - it('Test setTensor(string, object) - throws', () => { - assert.throws( - () => inferRequest.setTensor('fc_out', {}), - {message: /Argument #[0-9]+ must be a Tensor./}); - }); + it('Test setTensor(string, object) - throws', () => { + const testName = 'testName'; + assert.throws( + () => inferRequest.setTensor(testName, tensor), + {message: /Port for tensor name testName was not found./}); + }); - it('Test setTensor(string, tensor) - pass one arg', () => { - assert.throws( - () => inferRequest.setTensor('fc_out'), - {message: / invalid argument./} - ); - }); + it('Test setTensor(string, object) - throws', () => { + assert.throws( + () => inferRequest.setTensor('fc_out', {}), + {message: /Argument #[0-9]+ must be a Tensor./}); + }); - it('Test setTensor(string, tensor) - pass args in wrong order', () => { - assert.throws( - () => inferRequest.setTensor(resTensor, 'fc_out'), - {message: / invalid argument./} - ); - }); + it('Test setTensor(string, tensor) - pass one arg', () => { + assert.throws( + () => inferRequest.setTensor('fc_out'), + {message: / invalid argument./} + ); + }); - it('Test setTensor(string, tensor) - pass number as first arg', () => { - assert.throws( - () => inferRequest.setTensor(123, 'fc_out'), - {message: / invalid argument/} - ); - }); + it('Test setTensor(string, tensor) - pass args in wrong order', () => { + assert.throws( + () => inferRequest.setTensor(resTensor, 'fc_out'), + {message: / invalid argument./} + ); + }); - const irGetters = compiledModel.createInferRequest(); - irGetters.setInputTensor(tensor); - irGetters.infer(); + it('Test setTensor(string, tensor) - pass number as first arg', () => { + assert.throws( + () => inferRequest.setTensor(123, 'fc_out'), + {message: / invalid argument/} + ); + }); - it('Test getTensor(tensorName)', () => { - const t1 = irGetters.getTensor('data'); - assert.ok(t1 instanceof ov.Tensor); - assert.deepStrictEqual(tensor.data[0], t1.data[0]); - }); + const irGetters = compiledModel.createInferRequest(); + irGetters.setInputTensor(tensor); + irGetters.infer(); - it('Test getTensor(Output)', () => { - const input = irGetters.getCompiledModel().input(); - const t1 = irGetters.getTensor(input); - assert.ok(t1 instanceof ov.Tensor); - assert.deepStrictEqual(tensor.data[0], t1.data[0]); - }); + it('Test getTensor(tensorName)', () => { + const t1 = irGetters.getTensor('data'); + assert.ok(t1 instanceof ov.Tensor); + assert.deepStrictEqual(tensor.data[0], t1.data[0]); + }); - it('Test getInputTensor()', () => { - const t1 = irGetters.getInputTensor(); - assert.ok(t1 instanceof ov.Tensor); - assert.deepStrictEqual(tensor.data[0], t1.data[0]); - }); + it('Test getTensor(Output)', () => { + const input = irGetters.getCompiledModel().input(); + const t1 = irGetters.getTensor(input); + assert.ok(t1 instanceof ov.Tensor); + assert.deepStrictEqual(tensor.data[0], t1.data[0]); + }); - it('Test getInputTensor(idx)', () => { - const t1 = irGetters.getInputTensor(0); - assert.ok(t1 instanceof ov.Tensor); - assert.deepStrictEqual(tensor.data[0], t1.data[0]); - }); + it('Test getInputTensor()', () => { + const t1 = irGetters.getInputTensor(); + assert.ok(t1 instanceof ov.Tensor); + assert.deepStrictEqual(tensor.data[0], t1.data[0]); + }); - it('Test getOutputTensor(idx?)', () => { - const res1 = irGetters.getOutputTensor(); - const res2 = irGetters.getOutputTensor(0); - assert.ok(res1 instanceof ov.Tensor); - assert.ok(res2 instanceof ov.Tensor); - assert.deepStrictEqual(res1.data[0], res2.data[0]); - }); + it('Test getInputTensor(idx)', () => { + const t1 = irGetters.getInputTensor(0); + assert.ok(t1 instanceof ov.Tensor); + assert.deepStrictEqual(tensor.data[0], t1.data[0]); + }); + + it('Test getOutputTensor(idx?)', () => { + const res1 = irGetters.getOutputTensor(); + const res2 = irGetters.getOutputTensor(0); + assert.ok(res1 instanceof ov.Tensor); + assert.ok(res2 instanceof ov.Tensor); + assert.deepStrictEqual(res1.data[0], res2.data[0]); + }); - it('Test getCompiledModel()', () => { - const ir = compiledModel.createInferRequest(); - const cm = ir.getCompiledModel(); - assert.ok(cm instanceof ov.CompiledModel); - const ir2 = cm.createInferRequest(); - const res2 = ir2.infer([tensorData]); - const res1 = ir.infer([tensorData]); - assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]); + it('Test getCompiledModel()', () => { + const ir = compiledModel.createInferRequest(); + const cm = ir.getCompiledModel(); + assert.ok(cm instanceof ov.CompiledModel); + const ir2 = cm.createInferRequest(); + const res2 = ir2.infer([tensorData]); + const res1 = ir.infer([tensorData]); + assert.deepStrictEqual(res1['fc_out'].data[0], res2['fc_out'].data[0]); + }); }); + }); diff --git a/src/bindings/js/node/tests/unit/model.test.js b/src/bindings/js/node/tests/unit/model.test.js index 7728f13a25dce9..e374c0b3ab3cad 100644 --- a/src/bindings/js/node/tests/unit/model.test.js +++ b/src/bindings/js/node/tests/unit/model.test.js @@ -4,45 +4,51 @@ const { addon: ov } = require('../..'); const assert = require('assert'); -const { describe, it } = require('node:test'); -const { getModelPath } = require('./utils.js'); -const testXml = getModelPath().xml; -const core = new ov.Core(); -const model = core.readModelSync(testXml); -const clonedModel = model.clone(); - -describe('Node.js Model.isDynamic()', () => { - it('should return a boolean value indicating if the model is dynamic', () => { - const result = model.isDynamic(); - assert.strictEqual( - typeof result, - 'boolean', - 'isDynamic() should return a boolean value' - ); - }); +const { describe, it, before } = require('node:test'); +const { testModels, getModelPath, isModelAvailable } = require('./utils.js'); + +describe('ov.Model tests', () => { - it('should not accept any arguments', () => { - assert.throws( - () => { - model.isDynamic('unexpected argument'); - }, - /^Error: isDynamic\(\) does not accept any arguments\.$/, - 'Expected isDynamic to throw an error when called with arguments' - ); + before(async () => { + await isModelAvailable(testModels.testModelFP32); }); - it('returns false for a static model', () => { - const expectedStatus = false; - assert.strictEqual( - model.isDynamic(), - expectedStatus, - 'Expected isDynamic to return false for a static model' - ); + const testXml = getModelPath().xml; + const core = new ov.Core(); + const model = core.readModelSync(testXml); + const clonedModel = model.clone(); + + describe('Model.isDynamic()', () => { + it('should return a boolean value indicating if the model is dynamic', () => { + const result = model.isDynamic(); + assert.strictEqual( + typeof result, + 'boolean', + 'isDynamic() should return a boolean value' + ); + }); + + it('should not accept any arguments', () => { + assert.throws( + () => { + model.isDynamic('unexpected argument'); + }, + /^Error: isDynamic\(\) does not accept any arguments\.$/, + 'Expected isDynamic to throw an error when called with arguments' + ); + }); + + it('returns false for a static model', () => { + const expectedStatus = false; + assert.strictEqual( + model.isDynamic(), + expectedStatus, + 'Expected isDynamic to return false for a static model' + ); + }); }); -}); -describe('Node.js getFriendlyName() / setFriendlyName()', () => { - describe('getFriendlyName()', () => { + describe('Model.getFriendlyName()', () => { it('returns the unique name of the model if no friendly name is set', () => { const expectedName = 'test_model'; assert.strictEqual(model.getFriendlyName(), expectedName); @@ -54,7 +60,7 @@ describe('Node.js getFriendlyName() / setFriendlyName()', () => { ); }); }); - describe('setFriendlyName()', () => { + describe('Model.setFriendlyName()', () => { it('sets a friendly name for the model', () => { assert.doesNotThrow(() => model.setFriendlyName('MyFriendlyName')); }); @@ -94,84 +100,85 @@ describe('Node.js getFriendlyName() / setFriendlyName()', () => { assert.strictEqual(model.getFriendlyName(), 'Model1'); }); }); -}); -describe('Model.getOutputSize()', () => { + describe('Model.getOutputSize()', () => { - it('should return a number indicating number of outputs for the model', () => { - const result = model.getOutputSize(); - assert.strictEqual(typeof result, 'number', 'getOutputSize() should return a number'); - }); + it('should return a number indicating number of outputs for the model', () => { + const result = model.getOutputSize(); + assert.strictEqual(typeof result, 'number', 'getOutputSize() should return a number'); + }); - it('should not accept any arguments', () => { - assert.throws(() => { - model.getOutputSize('unexpected argument'); - }, /^Error: getOutputSize\(\) does not accept any arguments\.$/, 'Expected getOutputSize to throw an error when called with arguments'); - }); + it('should not accept any arguments', () => { + assert.throws(() => { + model.getOutputSize('unexpected argument'); + }, /^Error: getOutputSize\(\) does not accept any arguments\.$/, 'Expected getOutputSize to throw an error when called with arguments'); + }); - it('should return 1 for the default model', () => { - assert.strictEqual(model.getOutputSize(), 1, 'Expected getOutputSize to return 1 for the default model'); + it('should return 1 for the default model', () => { + assert.strictEqual(model.getOutputSize(), 1, 'Expected getOutputSize to return 1 for the default model'); + }); }); -}); -describe('Model.getOutputElementType()', () => { - it('should return a string for the element type ', () => { - const result = model.getOutputElementType(0); - assert.strictEqual(typeof result, 'string', - 'getOutputElementType() should return a string'); - }); + describe('Model.getOutputElementType()', () => { + it('should return a string for the element type ', () => { + const result = model.getOutputElementType(0); + assert.strictEqual(typeof result, 'string', + 'getOutputElementType() should return a string'); + }); - it('should accept a single integer argument', () => { - assert.throws(() => { - model.getOutputElementType(); - }, /'getOutputElementType' method called with incorrect parameters/, - 'Should throw when called without arguments'); - - assert.throws(() => { - model.getOutputElementType('unexpected argument'); - }, /'getOutputElementType' method called with incorrect parameters/, - 'Should throw on non-number argument'); - - assert.throws(() => { - model.getOutputElementType(0, 1); - }, /'getOutputElementType' method called with incorrect parameters/, - 'Should throw on multiple arguments'); - - assert.throws(() => { - model.getOutputElementType(3.14); - }, /'getOutputElementType' method called with incorrect parameters/, - 'Should throw on non-integer number'); - }); + it('should accept a single integer argument', () => { + assert.throws(() => { + model.getOutputElementType(); + }, /'getOutputElementType' method called with incorrect parameters/, + 'Should throw when called without arguments'); + + assert.throws(() => { + model.getOutputElementType('unexpected argument'); + }, /'getOutputElementType' method called with incorrect parameters/, + 'Should throw on non-number argument'); + + assert.throws(() => { + model.getOutputElementType(0, 1); + }, /'getOutputElementType' method called with incorrect parameters/, + 'Should throw on multiple arguments'); + + assert.throws(() => { + model.getOutputElementType(3.14); + }, /'getOutputElementType' method called with incorrect parameters/, + 'Should throw on non-integer number'); + }); - it('should return a valid element type for the default model', () => { - const elementType = model.getOutputElementType(0); - assert.ok(typeof elementType === 'string' && elementType.length > 0, - `Expected a non-empty string, got ${elementType}`); - }); + it('should return a valid element type for the default model', () => { + const elementType = model.getOutputElementType(0); + assert.ok(typeof elementType === 'string' && elementType.length > 0, + `Expected a non-empty string, got ${elementType}`); + }); - it('should throw an error for out-of-range index', () => { - const outputSize = model.getOutputSize(); - assert.throws( - () => { model.getOutputElementType(outputSize); }, - /^Error: /, - 'Should throw for out-of-range index' - ); + it('should throw an error for out-of-range index', () => { + const outputSize = model.getOutputSize(); + assert.throws( + () => { model.getOutputElementType(outputSize); }, + /^Error: /, + 'Should throw for out-of-range index' + ); + }); }); -}); -describe('Model.clone()', () => { - it('should return an object of type model', () => { - assert.ok(clonedModel instanceof ov.Model, 'clone() should return a model'); - }); + describe('Model.clone()', () => { + it('should return an object of type model', () => { + assert.ok(clonedModel instanceof ov.Model, 'clone() should return a model'); + }); - it('should return a model that is a clone of the calling model', () => { - assert.deepStrictEqual(clonedModel, model, "Cloned Model should be exactly equal to the calling model"); - }); - - it('should not accept any arguments', () => { - assert.throws( - () => model.clone("Unexpected argument").then(), - /'clone' method called with incorrect parameters./ - ); + it('should return a model that is a clone of the calling model', () => { + assert.deepStrictEqual(clonedModel, model, 'Cloned Model should be exactly equal to the calling model'); + }); + + it('should not accept any arguments', () => { + assert.throws( + () => model.clone('Unexpected argument').then(), + /'clone' method called with incorrect parameters./ + ); + }); }); + }); diff --git a/src/bindings/js/node/tests/unit/partial_shape.test.js b/src/bindings/js/node/tests/unit/partial_shape.test.js index 98f7d372ce8191..e46af099f03e27 100644 --- a/src/bindings/js/node/tests/unit/partial_shape.test.js +++ b/src/bindings/js/node/tests/unit/partial_shape.test.js @@ -9,46 +9,46 @@ const { describe, it } = require('node:test'); const staticShape = '1, 3, 224, 224'; const dynamicShape = '?, -1, 1..3, 224'; -describe('PartialShape', () => { - it('Allows create empty shape', () => { +describe('ov.PartialShape tests', () => { + it('allows create empty shape', () => { const partialShape = new ov.PartialShape(); assert.strictEqual(partialShape.toString(), '[]'); }); - it('Should detect static shape', () => { + it('should detect static shape', () => { const partialShape = new ov.PartialShape(staticShape); assert.ok(partialShape.isStatic()); }); - it('Should detect dynamic shape', () => { + it('should detect dynamic shape', () => { const partialShape = new ov.PartialShape(dynamicShape); assert.strictEqual(partialShape.isStatic(), false); }); - it('Should return shape as string for static shape', () => { + it('should return shape as string for static shape', () => { const partialShape = new ov.PartialShape(staticShape); assert.strictEqual(partialShape.toString(), '[1,3,224,224]'); }); - it('Should return shape as string for dynamic shape', () => { + it('should return shape as string for dynamic shape', () => { const partialShape = new ov.PartialShape(dynamicShape); assert.strictEqual(partialShape.toString(), '[?,?,1..3,224]'); }); - it('Should return array with dimensions for dynamic shape', () => { + it('should return array with dimensions for dynamic shape', () => { const partialShape = new ov.PartialShape(staticShape); - assert.deepStrictEqual(partialShape.getDimensions(), [1,3,224,224]); + assert.deepStrictEqual(partialShape.getDimensions(), [1, 3, 224, 224]); }); - it('Should return array with dimensions for dynamic shape', () => { + it('should return array with dimensions for dynamic shape', () => { const partialShape = new ov.PartialShape(dynamicShape); - assert.deepStrictEqual(partialShape.getDimensions(), [-1,-1,[1,3],224]); + assert.deepStrictEqual(partialShape.getDimensions(), [-1, -1, [1, 3], 224]); }); }); diff --git a/src/bindings/js/node/tests/unit/pre_post_processor.test.js b/src/bindings/js/node/tests/unit/pre_post_processor.test.js index a4570ce5fdd6a4..f3068f52581732 100644 --- a/src/bindings/js/node/tests/unit/pre_post_processor.test.js +++ b/src/bindings/js/node/tests/unit/pre_post_processor.test.js @@ -4,161 +4,167 @@ const { addon: ov } = require('../..'); const assert = require('assert'); -const { describe, it } = require('node:test'); -const { getModelPath } = require('./utils.js'); +const { describe, it, before } = require('node:test'); +const { testModels, getModelPath, isModelAvailable } = require('./utils.js'); -const testXml = getModelPath().xml; -const core = new ov.Core(); -const model = core.readModelSync(testXml); +describe('ov.preprocess.PrePostProcessor tests', () => { -describe('PrePostProcess', () => { - - it('input() ', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input()); + before(async () => { + await isModelAvailable(testModels.testModelFP32); }); - it('input(size_t input_index)', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0)); - }); + const testXml = getModelPath().xml; + const core = new ov.Core(); + const model = core.readModelSync(testXml); - it('input(const std::string& tensor_name)', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input('data')); - }); + describe('PrePostProcess', () => { - it('output() ', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).output()); - }); + it('input() ', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input()); + }); - it('output(size_t output_index)', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).output(0)); - }); + it('input(size_t input_index)', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0)); + }); - it('output(const std::string& tensor_name)', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).output('fc_out')); - }); + it('input(const std::string& tensor_name)', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input('data')); + }); -}); + it('output() ', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).output()); + }); -describe('InputInfo', () => { - it('tensor()', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor()); - }); + it('output(size_t output_index)', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).output(0)); + }); - it('preprocess()', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0).preprocess()); - }); + it('output(const std::string& tensor_name)', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).output('fc_out')); + }); - it('model()', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0).model()); }); - it('tensor(param) throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor(0), + describe('InputInfo', () => { + it('tensor()', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor()); + }); + + it('preprocess()', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0).preprocess()); + }); + + it('model()', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0).model()); + }); + + it('tensor(param) throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor(0), /Function does not take any parameters./); - }); + }); - it('preprocess(param) throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).preprocess(0), + it('preprocess(param) throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).preprocess(0), /Function does not take any parameters./); - }); + }); - it('model(param) throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).model(0), + it('model(param) throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).model(0), /Function does not take any parameters./); - }); + }); - it('tensor().setElementType()', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType(ov.element.u8)); - }); + it('tensor().setElementType()', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType(ov.element.u8)); + }); - it('tensor().setElementType() throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType(), + it('tensor().setElementType() throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType(), /Wrong number of parameters./); - }); + }); - it('tensor().setElementType() throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType('invalidType'), + it('tensor().setElementType() throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor().setElementType('invalidType'), /Cannot create ov::element::Type/); - }); + }); - it('tensor().SetShape()', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor().setShape([1, 10])); - }); + it('tensor().SetShape()', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor().setShape([1, 10])); + }); - it('tensor().SetShape() throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor().setShape(), + it('tensor().SetShape() throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor().setShape(), /Wrong number of parameters./); - }); + }); - it('tensor().setLayout()', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor().setLayout('NHWC')); - }); + it('tensor().setLayout()', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor().setLayout('NHWC')); + }); - it('tensor().setLayout() throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).tensor().setLayout(), + it('tensor().setLayout() throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).tensor().setLayout(), /Wrong number of parameters./); - }); + }); - it('preprocess().resize()', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize( - ov.preprocess.resizeAlgorithm.RESIZE_LINEAR, - )); - }); + it('preprocess().resize()', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize( + ov.preprocess.resizeAlgorithm.RESIZE_LINEAR, + )); + }); - it('preprocess().resize() throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize( - ov.preprocess.resizeAlgorithm.RESIZE_LINEAR, 'extraArg', - ), + it('preprocess().resize() throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize( + ov.preprocess.resizeAlgorithm.RESIZE_LINEAR, 'extraArg', + ), /Wrong number of parameters./); - }); + }); - it('preprocess().resize() no arg throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize(), + it('preprocess().resize() no arg throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).preprocess().resize(), /Wrong number of parameters./); - }); + }); - it('model().setLayout()', () => { - assert.doesNotThrow(() => - new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('NCHW')); - }); + it('model().setLayout()', () => { + assert.doesNotThrow(() => + new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('NCHW')); + }); - it('model().setLayout() throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('NCHW', 'extraArg'), + it('model().setLayout() throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('NCHW', 'extraArg'), /Wrong number of parameters./); - }); + }); - it('model().setLayout() throws', () => { - assert.throws(() => - new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('invalidLayout') - ); - }); + it('model().setLayout() throws', () => { + assert.throws(() => + new ov.preprocess.PrePostProcessor(model).input(0).model().setLayout('invalidLayout') + ); + }); -}); + }); -describe('OutputInfo', () => { + describe('OutputInfo', () => { it('tensor()', () => { assert.doesNotThrow(() => new ov.preprocess.PrePostProcessor(model).output(0).tensor()); @@ -167,7 +173,7 @@ describe('OutputInfo', () => { it('tensor(param) throws', () => { assert.throws(() => new ov.preprocess.PrePostProcessor(model).output(0).tensor(0), - /Function does not take any parameters./); + /Function does not take any parameters./); }); it('tensor().setElementType()', () => { @@ -178,13 +184,13 @@ describe('OutputInfo', () => { it('tensor().setElementType() throws', () => { assert.throws(() => new ov.preprocess.PrePostProcessor(model).output(0).tensor().setElementType(), - /Wrong number of parameters./); + /Wrong number of parameters./); }); it('tensor().setElementType() throws', () => { assert.throws(() => new ov.preprocess.PrePostProcessor(model).output(0).tensor().setElementType('invalidType'), - /Cannot create ov::element::Type/); + /Cannot create ov::element::Type/); }); it('tensor().setLayout()', () => { @@ -195,7 +201,9 @@ describe('OutputInfo', () => { it('tensor().setLayout() throws', () => { assert.throws(() => new ov.preprocess.PrePostProcessor(model).output(0).tensor().setLayout(), - /Wrong number of parameters./); + /Wrong number of parameters./); }); }); + +}); diff --git a/src/bindings/js/node/tests/unit/read_model.test.js b/src/bindings/js/node/tests/unit/read_model.test.js index dbc9fc2e876b7d..47fc584fe54e9e 100644 --- a/src/bindings/js/node/tests/unit/read_model.test.js +++ b/src/bindings/js/node/tests/unit/read_model.test.js @@ -5,81 +5,90 @@ const fs = require('node:fs'); const { addon: ov } = require('../..'); const assert = require('assert'); -const { describe, it } = require('node:test'); -const { getModelPath } = require('./utils.js'); +const { describe, it, before } = require('node:test'); +const { testModels, isModelAvailable, getModelPath } = require('./utils.js'); const { xml: modelPath, bin: weightsPath } = getModelPath(); -const modelFile = fs.readFileSync(modelPath); -const modelStr = fs.readFileSync(modelPath, 'utf8'); -const weightsFile = fs.readFileSync(weightsPath); -const weightsTensor = new ov.Tensor(ov.element.u8, [weightsFile.buffer.byteLength], new Uint8Array(weightsFile.buffer)); - -const core = new ov.Core(); - -describe('Core.readModeSync', () => { - it('readModeSync(xmlPath) ', () => { - const model = core.readModelSync(modelPath); - assert.ok(model instanceof ov.Model); - assert.equal(model.inputs.length, 1); - }); - it('readModeSync(xmlPath, weightsPath) ', () => { - const model = core.readModelSync(modelPath, weightsPath); - assert.ok(model instanceof ov.Model); - assert.equal(model.inputs.length, 1); - }); +describe('Tests for reading model.', () => { - it('readModeSync throws', () => { - assert.throws( - () => core.readModelSync(core), - /'readModelSync' method called with incorrect parameters./, - ) + before(async () => { + await isModelAvailable(testModels.testModelFP32); }); - it('readModelSync(modelString, weightsTensor) ', () => { - const model = core.readModelSync( - modelStr, - weightsTensor, - ); - assert.ok(model instanceof ov.Model); - assert.equal(model.inputs.length, 1); - }); + const modelFile = fs.readFileSync(modelPath); + const modelStr = fs.readFileSync(modelPath, 'utf8'); + const weightsFile = fs.readFileSync(weightsPath); + const weightsTensor = new ov.Tensor(ov.element.u8, [weightsFile.buffer.byteLength], new Uint8Array(weightsFile.buffer)); - it('readModelSync(modelUint8ArrayBuffer, weightsUint8ArrayBuffer) ', () => { - const model = core.readModelSync( - new Uint8Array(modelFile.buffer), - new Uint8Array(weightsFile.buffer), - ); - assert.ok(model instanceof ov.Model); - assert.equal(model.inputs.length, 1); - }); -}); + const core = new ov.Core(); -describe('Core.readModel', () => { - it('readModel(xmlPath) ', async () => { - const model = await core.readModel(modelPath); - assert.equal(model.inputs.length, 1); - }); + describe('Core.readModeSync', () => { + it('readModeSync(xmlPath) ', () => { + const model = core.readModelSync(modelPath); + assert.ok(model instanceof ov.Model); + assert.equal(model.inputs.length, 1); + }); - it('readModel(xmlPath, weightsPath) ', async () => { - const model = await core.readModel(modelPath, weightsPath); - assert.equal(model.inputs.length, 1); - }); + it('readModeSync(xmlPath, weightsPath) ', () => { + const model = core.readModelSync(modelPath, weightsPath); + assert.ok(model instanceof ov.Model); + assert.equal(model.inputs.length, 1); + }); - it('readModel(modelString, weightsTensor) ', async () => { - const model = await core.readModel( - modelStr, - weightsTensor, - ); - assert.ok(model instanceof ov.Model); - assert.equal(model.inputs.length, 1); + it('readModeSync throws', () => { + assert.throws( + () => core.readModelSync(core), + /'readModelSync' method called with incorrect parameters./, + ); + }); + + it('readModelSync(modelString, weightsTensor) ', () => { + const model = core.readModelSync( + modelStr, + weightsTensor, + ); + assert.ok(model instanceof ov.Model); + assert.equal(model.inputs.length, 1); + }); + + it('readModelSync(modelUint8ArrayBuffer, weightsUint8ArrayBuffer) ', () => { + const model = core.readModelSync( + new Uint8Array(modelFile.buffer), + new Uint8Array(weightsFile.buffer), + ); + assert.ok(model instanceof ov.Model); + assert.equal(model.inputs.length, 1); + }); }); - it('readModel(modelUint8ArrayBuffer, weightsUint8ArrayBuffer) ', async () => { - const model = await core.readModel( - new Uint8Array(modelFile.buffer), - new Uint8Array(weightsFile.buffer), - ); - assert.equal(model.inputs.length, 1); + describe('Core.readModel', () => { + it('readModel(xmlPath) ', async () => { + const model = await core.readModel(modelPath); + assert.equal(model.inputs.length, 1); + }); + + it('readModel(xmlPath, weightsPath) ', async () => { + const model = await core.readModel(modelPath, weightsPath); + assert.equal(model.inputs.length, 1); + }); + + it('readModel(modelString, weightsTensor) ', async () => { + const model = await core.readModel( + modelStr, + weightsTensor, + ); + assert.ok(model instanceof ov.Model); + assert.equal(model.inputs.length, 1); + }); + + it('readModel(modelUint8ArrayBuffer, weightsUint8ArrayBuffer) ', async () => { + const model = await core.readModel( + new Uint8Array(modelFile.buffer), + new Uint8Array(weightsFile.buffer), + ); + assert.equal(model.inputs.length, 1); + }); }); + }); diff --git a/src/bindings/js/node/tests/unit/setup.js b/src/bindings/js/node/tests/unit/setup.js new file mode 100644 index 00000000000000..b4885d24157abe --- /dev/null +++ b/src/bindings/js/node/tests/unit/setup.js @@ -0,0 +1,9 @@ +const { testModels, downloadTestModel } = require('./utils.js'); + +if (require.main === module) { + main(); +} + +async function main() { + await downloadTestModel(testModels.testModelFP32); +} diff --git a/src/bindings/js/node/tests/unit/tensor.test.js b/src/bindings/js/node/tests/unit/tensor.test.js index 79e0a66a9cdfe7..45d974f54cd6c0 100644 --- a/src/bindings/js/node/tests/unit/tensor.test.js +++ b/src/bindings/js/node/tests/unit/tensor.test.js @@ -7,243 +7,246 @@ const assert = require('assert'); const { test, describe, it } = require('node:test'); const getRandomBigInt = require('random-bigint'); -const shape = [1, 3, 224, 224]; -const elemNum = 1 * 3 * 224 * 224; -const data = Float32Array.from({ length: elemNum }, () => Math.random() ); -const params = [ - [ov.element.i8, 'i8', Int8Array.from({ length: elemNum }, () => Math.random() )], - [ov.element.u8, 'u8', Uint8Array.from({ length: elemNum }, () => Math.random() )], - [ov.element.i16, 'i16', Int16Array.from({ length: elemNum }, () => Math.random() )], - [ov.element.u16, 'u16', Uint16Array.from({ length: elemNum }, () => Math.random() )], - [ov.element.i32, 'i32', Int32Array.from({ length: elemNum }, () => Math.random() )], - [ov.element.u32, 'u32', Uint32Array.from({ length: elemNum }, () => Math.random() )], - [ov.element.f32, 'f32', Float32Array.from({ length: elemNum }, () => Math.random() )], - [ov.element.f64, 'f64', Float64Array.from({ length: elemNum }, () => Math.random() )], - [ov.element.i64, 'i64', BigInt64Array.from({ length: elemNum }, () => getRandomBigInt(10) )], - [ov.element.u64, 'u64', BigUint64Array.from({ length: elemNum }, () => getRandomBigInt(10) )], -]; - -test('Test for number of arguments in tensor', () => { - assert.throws( () => new ov.Tensor(ov.element.f32, shape, data, params), - {message: /Invalid number of arguments for Tensor constructor./}); -}); - -describe('Tensor without data parameters', () => { - it('Tensor should have array with zeros and numbers of elements according to the shape', () => { - const tensor = new ov.Tensor(ov.element.f32, shape); - assert.strictEqual(tensor.data.length, elemNum); +describe('ov.Tensor tests', () => { + + const shape = [1, 3, 224, 224]; + const elemNum = 1 * 3 * 224 * 224; + const data = Float32Array.from({ length: elemNum }, () => Math.random() ); + const params = [ + [ov.element.i8, 'i8', Int8Array.from({ length: elemNum }, () => Math.random() )], + [ov.element.u8, 'u8', Uint8Array.from({ length: elemNum }, () => Math.random() )], + [ov.element.i16, 'i16', Int16Array.from({ length: elemNum }, () => Math.random() )], + [ov.element.u16, 'u16', Uint16Array.from({ length: elemNum }, () => Math.random() )], + [ov.element.i32, 'i32', Int32Array.from({ length: elemNum }, () => Math.random() )], + [ov.element.u32, 'u32', Uint32Array.from({ length: elemNum }, () => Math.random() )], + [ov.element.f32, 'f32', Float32Array.from({ length: elemNum }, () => Math.random() )], + [ov.element.f64, 'f64', Float64Array.from({ length: elemNum }, () => Math.random() )], + [ov.element.i64, 'i64', BigInt64Array.from({ length: elemNum }, () => getRandomBigInt(10) )], + [ov.element.u64, 'u64', BigUint64Array.from({ length: elemNum }, () => getRandomBigInt(10) )], + ]; + + test('Test for number of arguments in tensor', () => { + assert.throws( () => new ov.Tensor(ov.element.f32, shape, data, params), + {message: /Invalid number of arguments for Tensor constructor./}); + }); + + describe('Tensor without data parameters', () => { + it('Tensor should have array with zeros and numbers of elements according to the shape', () => { + const tensor = new ov.Tensor(ov.element.f32, shape); + assert.strictEqual(tensor.data.length, elemNum); + }); }); -}); -describe('Tensor data', () => { + describe('Tensor data', () => { - params.forEach(([type, stringType, data]) => { - it(`Set tensor data with ${stringType} element type`, () => { - const tensor = new ov.Tensor(type, shape, data); - assert.deepStrictEqual(tensor.data, data); + params.forEach(([type, stringType, data]) => { + it(`set tensor data with ${stringType} element type`, () => { + const tensor = new ov.Tensor(type, shape, data); + assert.deepStrictEqual(tensor.data, data); + }); }); - }); - it('Create string tensor', () => { - const str_arr = ['text', 'more text', 'even more text']; - const tensor = new ov.Tensor(str_arr); - assert.deepStrictEqual(tensor.data, str_arr); - }); + it('create string tensor', () => { + const str_arr = ['text', 'more text', 'even more text']; + const tensor = new ov.Tensor(str_arr); + assert.deepStrictEqual(tensor.data, str_arr); + }); - it('Create string tensor', () => { - const str_arr = ['text', 'more text', 'even more text']; - const tensor = new ov.Tensor(str_arr); - assert.deepStrictEqual(tensor.data, str_arr); - }); + it('create string tensor', () => { + const str_arr = ['text', 'more text', 'even more text']; + const tensor = new ov.Tensor(str_arr); + assert.deepStrictEqual(tensor.data, str_arr); + }); - it('String tensor - passed array does not contain string elements', () => { - const str_arr = ['text', true]; - assert.throws(() => { new ov.Tensor(str_arr);}, - /The array passed to create string tensor must contain only strings./ - ); - }); + it('string tensor - passed array does not contain string elements', () => { + const str_arr = ['text', true]; + assert.throws(() => { new ov.Tensor(str_arr);}, + /The array passed to create string tensor must contain only strings./ + ); + }); - it('Set string tensor data', () => { - const str_arr = ['H', 'e', 'l', 'l', 'o']; - const tensor = new ov.Tensor(ov.element.string, [1, 1, 1, 5]); - tensor.data = str_arr; - assert.deepStrictEqual(tensor.data, str_arr); - }); + it('set string tensor data', () => { + const str_arr = ['H', 'e', 'l', 'l', 'o']; + const tensor = new ov.Tensor(ov.element.string, [1, 1, 1, 5]); + tensor.data = str_arr; + assert.deepStrictEqual(tensor.data, str_arr); + }); - it('Test tensor getData()', () => { - const tensor = new ov.Tensor(ov.element.f32, shape, data); - assert.deepStrictEqual(tensor.getData(), data); - }); + it('test tensor getData()', () => { + const tensor = new ov.Tensor(ov.element.f32, shape, data); + assert.deepStrictEqual(tensor.getData(), data); + }); - it('Test tensor.data setter - different element type throws', () => { - const float64_data = Float64Array.from([1, 2, 3] ); - const tensor = new ov.Tensor(ov.element.f32, [1, 3]); - assert.throws(() => { - tensor.data = float64_data;}, - /Passed array must have the same size as the Tensor!/ - ); - }); + it('test tensor.data setter - different element type throws', () => { + const float64_data = Float64Array.from([1, 2, 3] ); + const tensor = new ov.Tensor(ov.element.f32, [1, 3]); + assert.throws(() => { + tensor.data = float64_data;}, + /Passed array must have the same size as the Tensor!/ + ); + }); - it('Test tensor.data setter - different element length throws', () => { - const float64_data = Float64Array.from([1, 2, 3] ); - const tensor = new ov.Tensor(ov.element.f64, [1, 2]); - assert.throws(() => { - tensor.data = float64_data;}, - /Passed array must have the same size as the Tensor!/ - ); - }); + it('test tensor.data setter - different element length throws', () => { + const float64_data = Float64Array.from([1, 2, 3] ); + const tensor = new ov.Tensor(ov.element.f64, [1, 2]); + assert.throws(() => { + tensor.data = float64_data;}, + /Passed array must have the same size as the Tensor!/ + ); + }); - it('Test tensor.data setter', () => { - const testString = 'test'; - const tensor = new ov.Tensor(ov.element.f64, [1, 2]); - assert.throws(() => { - tensor.data = testString;}, - /Passed argument must be TypedArray, or Array if the tensor type is string./ - ); - }); + it('test tensor.data setter', () => { + const testString = 'test'; + const tensor = new ov.Tensor(ov.element.f64, [1, 2]); + assert.throws(() => { + tensor.data = testString;}, + /Passed argument must be TypedArray, or Array if the tensor type is string./ + ); + }); - it('Test tensor.data setter', () => { - const tensor = new ov.Tensor(ov.element.f32, shape); - tensor.data = data; - assert.deepStrictEqual(tensor.getData(), data); - }); + it('test tensor.data setter', () => { + const tensor = new ov.Tensor(ov.element.f32, shape); + tensor.data = data; + assert.deepStrictEqual(tensor.getData(), data); + }); - it('Set tensor data with Float32Array created from ArrayBuffer', () => { - const size = elemNum * 4; - const buffer = new ArrayBuffer(size); - const view = new Float32Array(buffer); - view.set(data); - const tensor = new ov.Tensor(ov.element.f32, shape, view); - assert.deepStrictEqual(tensor.data, data); - }); + it('set tensor data with Float32Array created from ArrayBuffer', () => { + const size = elemNum * 4; + const buffer = new ArrayBuffer(size); + const view = new Float32Array(buffer); + view.set(data); + const tensor = new ov.Tensor(ov.element.f32, shape, view); + assert.deepStrictEqual(tensor.data, data); + }); - it('Set tensor data with too big Float32Array', () => { - const size = elemNum * 8; - const buffer = new ArrayBuffer(size); - const view = new Float32Array(buffer); - view.set(data); - assert.throws( () => new ov.Tensor(ov.element.f32, shape, view), - {message: /Memory allocated using shape and element::type mismatch/}); - }); + it('set tensor data with too big Float32Array', () => { + const size = elemNum * 8; + const buffer = new ArrayBuffer(size); + const view = new Float32Array(buffer); + view.set(data); + assert.throws( () => new ov.Tensor(ov.element.f32, shape, view), + {message: /Memory allocated using shape and element::type mismatch/}); + }); - it('Third argument of a tensor cannot be an ArrayBuffer', () => { - assert.throws( - () => new ov.Tensor(ov.element.f32, shape, new ArrayBuffer(1234)), - {message: /Third argument of a tensor must be TypedArray./}); - }); + it('third argument of a tensor cannot be an ArrayBuffer', () => { + assert.throws( + () => new ov.Tensor(ov.element.f32, shape, new ArrayBuffer(1234)), + {message: /Third argument of a tensor must be TypedArray./}); + }); - it('Third argument of a tensor cannot be an array object', () => { - assert.throws( - () => new ov.Tensor(ov.element.f32, shape, [1, 2, 3, 4]), - {message: /Third argument of a tensor must be TypedArray./}); + it('third argument of a tensor cannot be an array object', () => { + assert.throws( + () => new ov.Tensor(ov.element.f32, shape, [1, 2, 3, 4]), + {message: /Third argument of a tensor must be TypedArray./}); + }); }); -}); -describe('Tensor shape', () => { + describe('Tensor shape', () => { - it('ov::Shape from an array object', () => { - const tensor = new ov.Tensor(ov.element.f32, [1, 3, 224, 224], data); - assert.deepStrictEqual(tensor.getShape(), [1, 3, 224, 224]); - }); + it('ov::Shape from an array object', () => { + const tensor = new ov.Tensor(ov.element.f32, [1, 3, 224, 224], data); + assert.deepStrictEqual(tensor.getShape(), [1, 3, 224, 224]); + }); - it('ov::Shape from an array object with floating point numbers', () => { - const tensor = + it('ov::Shape from an array object with floating point numbers', () => { + const tensor = new ov.Tensor(ov.element.f32, [1, 3.0, 224.8, 224.4], data); - assert.deepStrictEqual(tensor.getShape(), [1, 3, 224, 224]); - }); + assert.deepStrictEqual(tensor.getShape(), [1, 3, 224, 224]); + }); - it('Array argument to create ov::Shape can only contain numbers', () => { - assert.throws( - () => new ov.Tensor(ov.element.f32, ['1', 3, 224, 224], data), - {message: /Passed array must contain only numbers/}); - }); + it('array argument to create ov::Shape can only contain numbers', () => { + assert.throws( + () => new ov.Tensor(ov.element.f32, ['1', 3, 224, 224], data), + {message: /Passed array must contain only numbers/}); + }); - it('ov::Shape from TypedArray -> Int32Array', () => { - const shp = Int32Array.from([1, 224, 224, 3]); - const tensor = new ov.Tensor(ov.element.f32, shp, data); - assert.deepStrictEqual(tensor.getShape(), [1, 224, 224, 3]); - }); + it('ov::Shape from TypedArray -> Int32Array', () => { + const shp = Int32Array.from([1, 224, 224, 3]); + const tensor = new ov.Tensor(ov.element.f32, shp, data); + assert.deepStrictEqual(tensor.getShape(), [1, 224, 224, 3]); + }); - it('Cannot create ov::Shape from Float32Array', () => { - const shape = Float32Array.from([1, 224, 224, 3]); - assert.throws( - () => new ov.Tensor(ov.element.f32, shape, data), - /Passed argument must be an Int32Array or a Uint32Array./ - ); - }); + it('cannot create ov::Shape from Float32Array', () => { + const shape = Float32Array.from([1, 224, 224, 3]); + assert.throws( + () => new ov.Tensor(ov.element.f32, shape, data), + /Passed argument must be an Int32Array or a Uint32Array./ + ); + }); - it('Cannot create ov::Shape from ArrayBuffer', () => { - const shape = Int32Array.from([1, 224, 224, 3]); - assert.throws( - () => new ov.Tensor(ov.element.f32, shape.buffer, data), - /Passed argument must be of type Array or TypedArray./ - ); - }); + it('cannot create ov::Shape from ArrayBuffer', () => { + const shape = Int32Array.from([1, 224, 224, 3]); + assert.throws( + () => new ov.Tensor(ov.element.f32, shape.buffer, data), + /Passed argument must be of type Array or TypedArray./ + ); + }); - it('getShape() method does not accept parameters', () => { - const tensor = new ov.Tensor(ov.element.f32, [1, 3, 224, 224], data); - assert.throws( - () => tensor.getShape(1, 2, 3), - { message: 'No parameters are allowed for the getShape() method.'} - ); + it('getShape() method does not accept parameters', () => { + const tensor = new ov.Tensor(ov.element.f32, [1, 3, 224, 224], data); + assert.throws( + () => tensor.getShape(1, 2, 3), + { message: 'No parameters are allowed for the getShape() method.'} + ); + }); }); -}); -describe('Tensor element type', () => { - params.forEach(([elemType, val]) => { - it(`Comparison of ov.element.${elemType} to string ${val}`, () => { - assert.strictEqual(elemType, val); + describe('Tensor element type', () => { + params.forEach(([elemType, val]) => { + it(`comparison of ov.element.${elemType} to string ${val}`, () => { + assert.strictEqual(elemType, val); + }); }); - }); - params.forEach(([elemType, , data]) => { - it(`Comparison of ov.element ${elemType} got from Tensor object`, () => { - const tensor = new ov.Tensor(elemType, shape, data); - assert.strictEqual(tensor.getElementType(), elemType); + params.forEach(([elemType, , data]) => { + it(`comparison of ov.element ${elemType} got from Tensor object`, () => { + const tensor = new ov.Tensor(elemType, shape, data); + assert.strictEqual(tensor.getElementType(), elemType); + }); }); }); -}); -describe('Tensor getSize', () => { + describe('Tensor getSize', () => { - it('getSize returns the correct total number of elements', () => { - const tensor = new ov.Tensor(ov.element.f32, shape, data); - const expectedSize = shape.reduce((acc, dim) => acc * dim, 1); - assert.strictEqual(tensor.getSize(), expectedSize); - }); + it('getSize returns the correct total number of elements', () => { + const tensor = new ov.Tensor(ov.element.f32, shape, data); + const expectedSize = shape.reduce((acc, dim) => acc * dim, 1); + assert.strictEqual(tensor.getSize(), expectedSize); + }); - it('getSize should throw an error if arguments are provided', () => { - const tensor = new ov.Tensor(ov.element.f32, shape, data); - assert.throws( - () => tensor.getSize(1), - { message: 'getSize() does not accept any arguments.' } - ); + it('getSize should throw an error if arguments are provided', () => { + const tensor = new ov.Tensor(ov.element.f32, shape, data); + assert.throws( + () => tensor.getSize(1), + { message: 'getSize() does not accept any arguments.' } + ); + }); }); -}); -describe('Tensor getSize for various shapes', () => { + describe('Tensor getSize for various shapes', () => { - it('calculates size correctly for a common image data shape [3, 224, 224]', () => { - const shape = [3, 224, 224]; - const expectedSize = 3*224*224; - const tensorData = new Float32Array(expectedSize).fill(0); - const tensor = new ov.Tensor(ov.element.f32, shape, tensorData); - assert.strictEqual(tensor.getSize(), expectedSize); - }); + it('calculates size correctly for a common image data shape [3, 224, 224]', () => { + const shape = [3, 224, 224]; + const expectedSize = 3*224*224; + const tensorData = new Float32Array(expectedSize).fill(0); + const tensor = new ov.Tensor(ov.element.f32, shape, tensorData); + assert.strictEqual(tensor.getSize(), expectedSize); + }); - it('calculates size correctly for a scalar wrapped in a tensor [1]', () => { - const shape = [1]; - const expectedSize = 1; - const tensorData = new Float32Array(expectedSize).fill(0); - const tensor = new ov.Tensor(ov.element.f32, shape, tensorData); - assert.strictEqual(tensor.getSize(), expectedSize); - }); + it('calculates size correctly for a scalar wrapped in a tensor [1]', () => { + const shape = [1]; + const expectedSize = 1; + const tensorData = new Float32Array(expectedSize).fill(0); + const tensor = new ov.Tensor(ov.element.f32, shape, tensorData); + assert.strictEqual(tensor.getSize(), expectedSize); + }); - it('calculates size correctly for a vector [10]', () => { - const shape = [10]; - const expectedSize = 10; - const tensorData = new Float32Array(expectedSize).fill(0); - const tensor = new ov.Tensor(ov.element.f32, shape, tensorData); - assert.strictEqual(tensor.getSize(), expectedSize); + it('calculates size correctly for a vector [10]', () => { + const shape = [10]; + const expectedSize = 10; + const tensorData = new Float32Array(expectedSize).fill(0); + const tensor = new ov.Tensor(ov.element.f32, shape, tensorData); + assert.strictEqual(tensor.getSize(), expectedSize); + }); }); }); diff --git 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5184 - - - 10 - 5184 - - - - - 1 - 10 - - - - - - - - 1 - 10 - - - - - - - 1 - 10 - - - 1 - 10 - - - - - 1 - 10 - - - - - - - - 1 - 10 - - - - - 1 - 10 - - - - - - - 1 - 10 - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - diff --git a/src/bindings/js/node/tests/unit/utils.js b/src/bindings/js/node/tests/unit/utils.js index ebe126d6c74129..cf541a332b103f 100644 --- a/src/bindings/js/node/tests/unit/utils.js +++ b/src/bindings/js/node/tests/unit/utils.js @@ -3,15 +3,67 @@ // SPDX-License-Identifier: Apache-2.0 const path = require('path'); +const fs = require('node:fs/promises'); +const { downloadFile, checkIfPathExists } = require('../../scripts/download_runtime'); -module.exports = { getModelPath }; +const modelDir = 'tests/unit/test_models/'; +const testModels = { + testModelFP32: { + xml: 'test_model_fp32.xml', + bin: 'test_model_fp32.bin', + xmlURL: + 'https://raw.githubusercontent.com/openvinotoolkit/testdata/master/models/test_model/test_model_fp32.xml', + binURL: + 'https://media.githubusercontent.com/media/openvinotoolkit/testdata/master/models/test_model/test_model_fp32.bin', + }, +}; + +module.exports = { + getModelPath, + downloadTestModel, + isModelAvailable, + testModels, +}; function getModelPath(isFP16=false) { - const basePath = 'tests/unit/test_models/'; const modelName = `test_model_fp${isFP16 ? 16 : 32}`; return { - xml: path.join(basePath, `${modelName}.xml`), - bin: path.join(basePath, `${modelName}.bin`), + xml: path.join(modelDir, `${modelName}.xml`), + bin: path.join(modelDir, `${modelName}.bin`), }; } + +async function downloadTestModel(model) { + const modelsDir = './tests/unit/test_models'; + try { + const ifModelsDirectoryExists = await checkIfPathExists(modelsDir); + if (!ifModelsDirectoryExists) { + await fs.mkdir(modelDir); + } + + const modelPath = path.join(modelsDir, model.xml); + const modelExists = await checkIfPathExists(modelPath); + if (modelExists) return; + + const { env } = process; + const proxyUrl = env.http_proxy || env.HTTP_PROXY || env.npm_config_proxy; + + await downloadFile(model.xmlURL, modelsDir, model.xml, proxyUrl); + await downloadFile(model.binURL, modelsDir, model.bin, proxyUrl); + } catch(error) { + console.error(`Failed to download the model: ${error}.`); + throw error; + } +} + +async function isModelAvailable(model) { + const baseArtifactsDir = './tests/unit/test_models'; + const modelPath = path.join(baseArtifactsDir, model.xml); + const modelExists = await checkIfPathExists(modelPath); + if (modelExists) return; + + console.log('\n\nTestModel cannot be found.\nPlease run `npm run test_setup`.\n\n'); + process.exit(1); + +} From 3afad8df203a9354b314e32c79d088e358a043b8 Mon Sep 17 00:00:00 2001 From: Aleksandr Voron Date: Wed, 21 Aug 2024 15:52:32 +0200 Subject: [PATCH 03/35] [CPU][ARM] Upgrade ACL to 24.08 (#26137) - [x] Performance check ### Tickets: - [CVS-150178](https://jira.devtools.intel.com/browse/CVS-150178) --- src/plugins/intel_cpu/thirdparty/ComputeLibrary | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/plugins/intel_cpu/thirdparty/ComputeLibrary b/src/plugins/intel_cpu/thirdparty/ComputeLibrary index c5dd7753d0475f..f1929dc994d8e5 160000 --- a/src/plugins/intel_cpu/thirdparty/ComputeLibrary +++ b/src/plugins/intel_cpu/thirdparty/ComputeLibrary @@ -1 +1 @@ -Subproject commit c5dd7753d0475ffec0f192f3181fe67a1d761680 +Subproject commit f1929dc994d8e5afae5c77ca66446344119a8592 From 6f899d293a10bc100c8fbc39fc0009d31601a51d Mon Sep 17 00:00:00 2001 From: Haiqi Pan Date: Wed, 21 Aug 2024 22:37:31 +0800 Subject: [PATCH 04/35] Move INFERENCE_PRECISION_HINT test to optional conformance for meta-plugin (#25598) ### Details: - *Move INFERENCE_PRECISION_HINT test to optional conformance for meta-plugin* ### Tickets: - *147702* --------- Co-authored-by: Chen Peter --- .../src/ov_plugin/properties.cpp | 11 ++++++----- .../shared/include/base/ov_behavior_test_utils.hpp | 6 ++++++ .../github/skip_configs/CPU/expected_failures_API.csv | 2 +- 3 files changed, 13 insertions(+), 6 deletions(-) diff --git a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_plugin/properties.cpp b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_plugin/properties.cpp index 5ed1e4269d69c4..594f4c68b61dc9 100644 --- a/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_plugin/properties.cpp +++ b/src/tests/functional/plugin/conformance/test_runner/api_conformance_runner/src/ov_plugin/properties.cpp @@ -88,11 +88,12 @@ INSTANTIATE_TEST_SUITE_P(ov_plugin_mandatory, OVCheckChangePropComplieModleGetPr ::testing::Values(ov::AnyMap({}))), OVCheckChangePropComplieModleGetPropTests_DEVICE_ID::getTestCaseName); -INSTANTIATE_TEST_SUITE_P(ov_plugin_mandatory, OVCheckChangePropComplieModleGetPropTests_InferencePrecision, - ::testing::Combine( - ::testing::Values(ov::test::utils::target_device), - ::testing::Values(ov::AnyMap({}))), - OVCheckChangePropComplieModleGetPropTests_InferencePrecision::getTestCaseName); +/* Add prefix mandatory_ to suffix (getTestCaseName) of HW plugin test cases */ +INSTANTIATE_TEST_SUITE_P( + ov_plugin, + OVCheckChangePropComplieModleGetPropTests_InferencePrecision, + ::testing::Combine(::testing::Values(ov::test::utils::target_device), ::testing::Values(ov::AnyMap({}))), + MARK_MANDATORY_FOR_HW_DEVICE(OVCheckChangePropComplieModleGetPropTests_InferencePrecision::getTestCaseName)); INSTANTIATE_TEST_SUITE_P(ov_plugin, OVCheckMetricsPropsTests_ModelDependceProps, ::testing::Combine( diff --git a/src/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp b/src/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp index 9de110a82b3032..686a3c797ba917 100644 --- a/src/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp +++ b/src/tests/functional/plugin/shared/include/base/ov_behavior_test_utils.hpp @@ -30,6 +30,12 @@ #include "common_test_utils/subgraph_builders/concat_with_params.hpp" #include "common_test_utils/subgraph_builders/split_concat.hpp" +#define MARK_MANDATORY_FOR_HW_DEVICE(GET_TEST_NAME) \ + [&](const testing::TestParamInfo& info) { \ + std::string name = GET_TEST_NAME(info); \ + return sw_plugin_in_target_device(ov::test::utils::target_device) ? "" : "mandatory_" + name; \ + } + namespace ov { namespace test { namespace behavior { diff --git a/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_API.csv b/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_API.csv index 52b257b6251142..ba96df69cf0887 100644 --- a/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_API.csv +++ b/src/tests/test_utils/functional_test_utils/layer_tests_summary/github/skip_configs/CPU/expected_failures_API.csv @@ -1,5 +1,5 @@ Test Name,Fix Priority -ov_plugin_mandatory/OVCheckChangePropComplieModleGetPropTests_InferencePrecision.ChangeCorrectProperties/target_device=CPU_,1.0 +ov_plugin/OVCheckChangePropComplieModleGetPropTests_InferencePrecision.ChangeCorrectProperties/mandatory_target_device=CPU_,1.0 ov_compiled_model_mandatory/OVClassCompiledModelGetPropertyTest_MODEL_PRIORITY.GetMetricNoThrow/3,1.0 ov_compiled_model_mandatory/OVClassCompiledModelGetPropertyTest_MODEL_PRIORITY.GetMetricNoThrow/2,1.0 ov_compiled_model_mandatory/OVClassCompiledModelGetPropertyTest_MODEL_PRIORITY.GetMetricNoThrow/1,1.0 From 407f0bcf25454f90a0c7a05ecaafe224e6ffc71c Mon Sep 17 00:00:00 2001 From: Andrei Kashchikhin Date: Wed, 21 Aug 2024 16:29:27 +0100 Subject: [PATCH 05/35] [CI] [GHA] Do not checkout latest oneDNN on U22 in nightly (#26150) ### Details: - GPU tests are only enabled for U20. Checking out the latest oneDNN via https://github.com/openvinotoolkit/openvino/blob/0b8eb87e0efa9030be47d661fe94b3c8beb655fd/.github/workflows/job_build_linux.yml#L71 breaks the build on U22: [like this one](https://github.com/openvinotoolkit/openvino/actions/runs/10481070002/job/29030046664) and prevents other nightly tests from executing. - This PR limits the checkout of the latest oneDNN to U20 only. This will unblock the nightly tests that run on U22 for now. - Later, it might be better to separate the builds for GPU/other test jobs. ### Tickets: - *149805* --- .github/workflows/job_build_linux.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/job_build_linux.yml b/.github/workflows/job_build_linux.yml index 8fd35f48172a85..564cd8e58c38a7 100644 --- a/.github/workflows/job_build_linux.yml +++ b/.github/workflows/job_build_linux.yml @@ -69,7 +69,7 @@ jobs: # Ticket: 139627 - name: Checkout the latest OneDNN for GPU in nightly - if: ${{ inputs.event-name == 'schedule' }} + if: ${{ inputs.event-name == 'schedule' && inputs.os == 'ubuntu_20_04' }} # GPU tests are enabled only on U20 working-directory: ${{ env.OPENVINO_REPO }}/src/plugins/intel_gpu/thirdparty/onednn_gpu run: | git fetch origin From 9d09d0a8fbf356cf3b6b11d4ca39485d82f45e79 Mon Sep 17 00:00:00 2001 From: Andrew Kwangwoong Park Date: Thu, 22 Aug 2024 01:39:30 +0900 Subject: [PATCH 06/35] [GPU] Fix segfault in layer tests for onnx_tests.test_lstm.TestLSTM (#26159) ### Details: - This issue occurs after https://github.com/openvinotoolkit/openvino/pull/26032 - Fix segfault in layer tests for onnx_tests.test_lstm.TestLSTM ### Tickets: - [CVS-150221](https://jira.devtools.intel.com/browse/CVS-150221) --- src/plugins/intel_gpu/src/graph/include/reshape_inst.h | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/plugins/intel_gpu/src/graph/include/reshape_inst.h b/src/plugins/intel_gpu/src/graph/include/reshape_inst.h index d5a60138a50777..a94f48a6ec92dd 100644 --- a/src/plugins/intel_gpu/src/graph/include/reshape_inst.h +++ b/src/plugins/intel_gpu/src/graph/include/reshape_inst.h @@ -44,7 +44,7 @@ struct typed_program_node : public typed_program_node_base { return false; // oneDNN supports padded input of outer axis only for buffer fusing on static shape - if (!has_outer_padding_offset() && get_users().front()->get_preferred_impl_type() == impl_types::onednn) + if (!has_outer_padding_offset() && get_users().size() == 1 && get_users().front()->get_preferred_impl_type() == impl_types::onednn) return false; // TODO: If user is RoPE or MVN and dynamic padding exists, ouput padding propagation is not supported in the base mode From 9ef7e23da022f20e128d6b2265aeb4cdf22f382c Mon Sep 17 00:00:00 2001 From: ge0rdi Date: Wed, 21 Aug 2024 18:42:33 +0200 Subject: [PATCH 07/35] [NPU] Fix ze_loader dependency (#26157) ### Details: Commit ad4eb09 introduced dynamic loading of ze_loader so that it is loaded only when really needed. Recent changes in `ZeroRemoteTensor` caused NPU plugin binary to import from ze_loader directly. This commit should fix that and the NPU binary should have no longer dependency on ze_loader. ### Tickets: - None --- src/plugins/intel_npu/src/backend/src/zero_remote_tensor.cpp | 1 + 1 file changed, 1 insertion(+) diff --git a/src/plugins/intel_npu/src/backend/src/zero_remote_tensor.cpp b/src/plugins/intel_npu/src/backend/src/zero_remote_tensor.cpp index d27ee74d9e5d1e..4ac1d75fe57f10 100644 --- a/src/plugins/intel_npu/src/backend/src/zero_remote_tensor.cpp +++ b/src/plugins/intel_npu/src/backend/src/zero_remote_tensor.cpp @@ -7,6 +7,7 @@ #include #include "intel_npu/al/config/common.hpp" +#include "intel_npu/utils/zero/zero_api.hpp" #include "openvino/core/type/element_iterator.hpp" #include "zero_utils.hpp" From 50ffcbc7e96a46ff7799a6330ad63230bdd525e7 Mon Sep 17 00:00:00 2001 From: Ilya Lavrenov Date: Wed, 21 Aug 2024 23:45:05 +0400 Subject: [PATCH 08/35] Fixed pattern for patching TBB config files (#26167) ### Details: - To fix issues with tokenizers build - See https://github.com/openvinotoolkit/openvino_tokenizers/pull/229 --- src/bindings/python/wheel/setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/bindings/python/wheel/setup.py b/src/bindings/python/wheel/setup.py index d91523659a0a78..f88d9bd3da7592 100644 --- a/src/bindings/python/wheel/setup.py +++ b/src/bindings/python/wheel/setup.py @@ -487,7 +487,7 @@ def copy_package_data(self, src_dirs): tbb_replacements = { # change the path where the TBBConfig.cmake is installed (/lib/cmake/TBB -> openvino/cmake) - r"(\{CMAKE_CURRENT_LIST_FILE\})": r"\1/fake_dir", + r"(_IMPORT_PREFIX \"\$\{CMAKE_CURRENT_LIST_FILE\})": r"\1/fake_dir", # change the path where the libraries are installed (/lib -> openvino/libs) r"(\{_IMPORT_PREFIX\})\/(.*)\/(.+\.[lib|dylib|so|dll])": rf"\1/{WHEEL_LIBS_INSTALL_DIR}/\3", # change the path where the include files are installed (/include -> openvino/include) From 28950f660e0344984ab553ada973eec4465d20ff Mon Sep 17 00:00:00 2001 From: Roman Kazantsev Date: Thu, 22 Aug 2024 00:26:56 +0400 Subject: [PATCH 09/35] [Common FE] Document get_input_by_reference better (#26165) **Details:** Document get_input_by_reference better **Ticket:** TBD Signed-off-by: Kazantsev, Roman --- .../common/include/openvino/frontend/node_context.hpp | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/src/frontends/common/include/openvino/frontend/node_context.hpp b/src/frontends/common/include/openvino/frontend/node_context.hpp index 98243fab0e9c63..c184d59eb32ab0 100644 --- a/src/frontends/common/include/openvino/frontend/node_context.hpp +++ b/src/frontends/common/include/openvino/frontend/node_context.hpp @@ -48,7 +48,12 @@ class FRONTEND_API NodeContext { FRONT_END_NOT_IMPLEMENTED(get_input); } - /// \brief Returns the input by reference. The reference value can be changed by consuming operation + /// \brief Returns output of Variable node (or Variable value). + /// Variable is a special node that stores a value represented with a sub-graph. + /// Variable has a concrete value at each conversion step. + /// The current (consuming) operation node can change its value + /// so consumers of this Variable will have a new value at next conversion steps. + /// See ov::frontend::Variable class for more details. virtual Output get_input_by_reference(int idx) const { FRONT_END_NOT_IMPLEMENTED(get_input_by_reference); } From 711f06026f7ba35f4c2482f0e3d398d1be51ab12 Mon Sep 17 00:00:00 2001 From: Roman Kazantsev Date: Thu, 22 Aug 2024 01:39:35 +0400 Subject: [PATCH 10/35] Revert "Temporarily remove TF layer tests from required on ARM" (#26169) Reverts openvinotoolkit/openvino#26158 Issue with TBB on ARM was fixed. --- .github/workflows/linux_arm64.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/linux_arm64.yml b/.github/workflows/linux_arm64.yml index 225fd632ee81c3..d38eda93e7d2b8 100644 --- a/.github/workflows/linux_arm64.yml +++ b/.github/workflows/linux_arm64.yml @@ -434,7 +434,7 @@ jobs: Overall_Status: name: ci/gha_overall_status_linux_arm64 needs: [Smart_CI, Build, Debian_Packages, Samples, ONNX_Runtime, CXX_Unit_Tests, Python_Unit_Tests, CPU_Functional_Tests, - TensorFlow_Models_Tests, PyTorch_Models_Tests, Openvino_tokenizers] + TensorFlow_Models_Tests, PyTorch_Models_Tests, Openvino_tokenizers, TensorFlow_Layer_Tests] if: ${{ always() }} runs-on: ubuntu-latest steps: From 1335be030909df842b92144ef99da680bbdcbe55 Mon Sep 17 00:00:00 2001 From: Steve Yoo Date: Thu, 22 Aug 2024 07:02:41 +0900 Subject: [PATCH 11/35] [GPU] Enable fc 4d for MatMul (#24642) ### Details: - *Enable fc 4d for MatMul to calculate 4x2* ### Tickets: - *132334* --- .../intel_gpu/src/graph/fully_connected.cpp | 4 +- .../test_cases/fully_connected_gpu_test.cpp | 37 +++++++++++++++++++ 2 files changed, 40 insertions(+), 1 deletion(-) diff --git a/src/plugins/intel_gpu/src/graph/fully_connected.cpp b/src/plugins/intel_gpu/src/graph/fully_connected.cpp index cc6d849bcfabe0..b09416585e3fed 100644 --- a/src/plugins/intel_gpu/src/graph/fully_connected.cpp +++ b/src/plugins/intel_gpu/src/graph/fully_connected.cpp @@ -117,7 +117,7 @@ layout fully_connected_inst::calc_output_layout(fully_connected_node const& node feature = std::max({input_layout.spatial(0), input_layout.spatial(1), input_layout.spatial(2)}); } - if (desc->input_size > 3) { + if (desc->input_size > 4) { input_layout.set_partial_shape(reshape_to_2d(input_pshape, feature)); } if (weights_pshape.size() != 2) { @@ -127,6 +127,8 @@ layout fully_connected_inst::calc_output_layout(fully_connected_node const& node auto output_size = tensor(input_layout.batch(), weights_layout.batch(), 1, 1); if (desc->input_size == 3) { output_size = tensor(input_layout.batch(), input_layout.feature(), 1, weights_layout.batch()); + } else if (desc->input_size == 4) { + output_size = tensor(input_layout.batch(), input_layout.feature(), weights_layout.batch(), input_layout.spatial(1)); } format output_format = get_preferred_format(node, impl_param); diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp index fa3e375cbecfc5..3f0b43a861873b 100644 --- a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp +++ b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp @@ -371,6 +371,43 @@ TEST(fully_connected_gpu, no_biases_fc_i32) { } } +TEST(fully_connected_gpu, no_biases_4d_input) { + // Input : 1x256x256x384 + // Output : 1x256x256x1536 + // Weights: 1536x384x1x1 + + const int32_t input_b = 1, input_f = 256, input_y = 256, input_x = 384, // size of the whole input buffer + weight_b = 1536, weight_f = 384, weight_y = 1, weight_x = 1; // size of the whole weights buffer + + auto& engine = get_test_engine(); + + auto input_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { input_b, input_f, input_x, input_y } }); + auto weights_prim = engine.allocate_memory({ data_types::f32, format::bfyx, { weight_b, weight_f, weight_x, weight_y } }); + + std::vector input_data(input_b * input_f * input_y * input_x, 0); + std::vector weights_data(weight_b * weight_f * weight_y * weight_x, 0); + + set_values(input_prim, std::move(input_data)); + set_values(weights_prim, std::move(weights_data)); + + auto input = input_layout("input", input_prim->get_layout()); + auto w_data = data("weights", weights_prim); + auto fc = fully_connected("fc_prim", input_info("input"), "weights", "", 4, 2); + topology topology; + topology.add(input); + topology.add(w_data); + topology.add(fc); + + network network(engine, topology, get_test_default_config(engine)); + network.set_input_data("input", input_prim); + + auto outputs = network.execute(); + ASSERT_EQ(outputs.begin()->second.get_layout().batch(), input_b); + ASSERT_EQ(outputs.begin()->second.get_layout().feature(), input_f); + ASSERT_EQ(outputs.begin()->second.get_layout().spatial(1), input_y); + ASSERT_EQ(outputs.begin()->second.get_layout().spatial(0), weight_b); +} + TEST(fully_connected_gpu, xb_f32_batch_1) { // Input : 3x1 // Output : 4x1 From abbf944fe17249dac6fcdec0e75c84cf306186ef Mon Sep 17 00:00:00 2001 From: Roman Kazantsev Date: Thu, 22 Aug 2024 03:13:27 +0400 Subject: [PATCH 12/35] [TF FE] Stabilize L2Loss layer tests on all platforms (#26151) **Details:** Stabilize L2Loss layer tests on all platforms **Ticket:** 104863 --------- Signed-off-by: Kazantsev, Roman --- .../tensorflow_tests/test_tf_L2Loss.py | 40 +++++++++++-------- 1 file changed, 24 insertions(+), 16 deletions(-) diff --git a/tests/layer_tests/tensorflow_tests/test_tf_L2Loss.py b/tests/layer_tests/tensorflow_tests/test_tf_L2Loss.py index e43808fbb688a6..ddd14eb2ae23ea 100644 --- a/tests/layer_tests/tensorflow_tests/test_tf_L2Loss.py +++ b/tests/layer_tests/tensorflow_tests/test_tf_L2Loss.py @@ -1,38 +1,46 @@ # Copyright (C) 2018-2024 Intel Corporation # SPDX-License-Identifier: Apache-2.0 +import numpy as np import pytest import tensorflow as tf from common.tf_layer_test_class import CommonTFLayerTest +rng = np.random.default_rng(233453) + class TestL2Loss(CommonTFLayerTest): - def create_l2_loss_net(self, input_shape): + def _prepare_input(self, inputs_info): + assert 'input:0' in inputs_info, "Test error: inputs_info must contain `input`" + input_shape = inputs_info['input:0'] + inputs_data = {} + inputs_data['input:0'] = rng.uniform(-2.0, 2.0, input_shape).astype(self.input_type) + return inputs_data + + def create_l2_loss_net(self, input_shape, input_type): + self.input_type = input_type tf.compat.v1.reset_default_graph() # Create the graph and model with tf.compat.v1.Session() as sess: - input = tf.compat.v1.placeholder(tf.float32, input_shape, 'input') + input = tf.compat.v1.placeholder(input_type, input_shape, 'input') tf.raw_ops.L2Loss(t=input, name='l2_loss') tf.compat.v1.global_variables_initializer() - tf_net = sess.graph_def return tf_net, None - test_data_basic = [ - dict(input_shape=[1, 2]), - dict(input_shape=[2, 3, 4]), - ] - - @pytest.mark.parametrize("params", test_data_basic) + @pytest.mark.parametrize("input_shape", [[], [2], [1, 2], [2, 3, 4]]) + @pytest.mark.parametrize("input_type", [np.float16, np.float32, np.float64]) @pytest.mark.precommit @pytest.mark.nightly - def test_l2_loss_basic(self, params, ie_device, precision, ir_version, temp_dir, + def test_l2_loss_basic(self, input_shape, input_type, + ie_device, precision, ir_version, temp_dir, use_legacy_frontend): - if ie_device == 'GPU': - pytest.xfail('104863') - if use_legacy_frontend: - pytest.skip("L2Loss is not supported by legacy FE.") - self._test(*self.create_l2_loss_net(**params), + custom_eps = None + if input_type == np.float16: + custom_eps = 3 * 1e-3 + if ie_device == 'GPU' and input_shape == []: + pytest.skip("150321: Accessing out-of-range dimension on GPU") + self._test(*self.create_l2_loss_net(input_shape, input_type), ie_device, precision, ir_version, temp_dir=temp_dir, - use_legacy_frontend=use_legacy_frontend) + use_legacy_frontend=use_legacy_frontend, custom_eps=custom_eps) From 486fd0b48c2fd38696b9847d715e3c445335e24a Mon Sep 17 00:00:00 2001 From: Zhiyuan Tan <66934674+BHbean@users.noreply.github.com> Date: Thu, 22 Aug 2024 14:59:20 +0800 Subject: [PATCH 13/35] [DOCS] [GSoC] add docs about how to build OV on RISC-V devices (#26056) ### Details: - *add `build_riscv64.md` file giving instructions on how to build OV on RISC-V devices* ### Tickets: - *N/A* --- docs/dev/build.md | 1 + docs/dev/build_riscv64.md | 131 ++++++++++++++++++++++++++++++++++++++ 2 files changed, 132 insertions(+) create mode 100644 docs/dev/build_riscv64.md diff --git a/docs/dev/build.md b/docs/dev/build.md index c70ef73f527fc4..5aca51f0632654 100644 --- a/docs/dev/build.md +++ b/docs/dev/build.md @@ -22,6 +22,7 @@ The articles below provide the basic informations about the process of building * [Raspbian Stretch](./build_raspbian.md) * [Web Assembly](./build_webassembly.md) * [Docker Image](https://github.com/openvinotoolkit/docker_ci/tree/master/dockerfiles/ubuntu18/build_custom) +* [Linux RISC-V](./build_riscv64.md) > **NOTE**: For the details on how to build static OpenVINO, refer to [Building static OpenVINO libraries](static_libaries.md) diff --git a/docs/dev/build_riscv64.md b/docs/dev/build_riscv64.md new file mode 100644 index 00000000000000..75d3782004f4dc --- /dev/null +++ b/docs/dev/build_riscv64.md @@ -0,0 +1,131 @@ +# Cross compile OpenVINO™ Runtime for RISCV64 systems +This guide shows how to build OpenVINO Runtime for 64-bit RISC-V devices. Due to limited resources, cross compilation is used now for building OpenVINO targeting RISC-V development boards. + +Cross compilation was tested on the following hosts: +- Ubuntu 22.04 (64-bit), x64 + +The software was validated on the following devices: +- [Lichee Pi 4A](https://wiki.sipeed.com/hardware/en/lichee/th1520/lp4a.html) with RVV 0.7.1 +- [Banana Pi BPI-F3](https://www.banana-pi.org/en/banana-pi-sbcs/175.html) with RVV 1.0 + + +## Software requirements + +- [CMake](https://cmake.org/download/) 3.13 or higher +- GCC 7.5 or higher (for non-RVV) / [xuantie-gnu-toolchain](https://github.com/XUANTIE-RV/xuantie-gnu-toolchain) (for RVV) +- Python 3.10 for OpenVINO Runtime Python API + +## How to build +0. Prerequisite: +- For target with RVV - build `xuantie-gnu-toolchain` and `qemu`: + ```sh + git clone https://github.com/XUANTIE-RV/xuantie-gnu-toolchain.git + cd xuantie-gnu-toolchain + ./configure --prefix= + make linux build-qemu -j$(nproc) + ``` +- For target without RVV - build `riscv-gnu-toolchain`: + ```sh + git clone https://github.com/riscv-collab/riscv-gnu-toolchain.git + cd riscv-gnu-toolchain + ./configure --prefix=/opt/riscv + make linux build-qemu -j$(nproc) + ``` + > **NOTE**: The `build-qemu` target is optional, as it is used to build the `qemu` simulator. However, it is recommended to build the `qemu` simulator, since it is much more convenient to validate the software on your host than on your devices. More information can be seen [here](https://github.com/riscv-collab/riscv-gnu-toolchain). + +1. Clone OpenVINO repository and init submodules: + ```sh + git clone --recursive https://github.com/openvinotoolkit/openvino.git + cd openvino + ``` + +2. Install build dependencies using the `install_build_dependencies.sh` script in the + project root folder. + ```sh + sudo ./install_build_dependencies.sh + ``` + +3. Create a build folder: + ```sh + mkdir build && cd build + ``` + +4. To cross compile OpenVINO Runtime for RISC-V devices, run `cmake` with specified `CMAKE_TOOLCHAIN_FILE` and `RISCV_TOOLCHAIN_ROOT`. +- For target with RVV: + ```sh + cmake .. \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_INSTALL_PREFIX= \ + -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/ \ + -DRISCV_TOOLCHAIN_ROOT= + ``` + > **NOTE**: To build OpenVINO Runtime for different versions of RVV, you just need to specify corresponding toolchain files. For exmaple, you can replace `` with `riscv64-071-thead-gnu.toolchain.cmake` for RVV 0.7.1 and `riscv64-100-thead-gnu.toolchain.cmake` for RVV 1.0 respectively. +- For target without RVV: + ```sh + cmake .. \ + -DCMAKE_BUILD_TYPE=Release \ + -DCMAKE_INSTALL_PREFIX= \ + -DCMAKE_TOOLCHAIN_FILE=../cmake/toolchains/riscv64-gnu.toolchain.cmake \ + -DRISCV_TOOLCHAIN_ROOT=/opt/riscv + ``` + > **NOTE**: The `riscv-gnu-toolchain` is build as there are essential files used for cross compilation under `/opt/riscv/sysroot`. The latest stable versions of Clang or GCC both support compiling source code into RISC-V instructions, so it is acceptable to choose your preferable compilers by specifying `-DCMAKE_C_COMPILER` and `CMAKE_CXX_COMPILER`. But remember to add the key `-DCMAKE_SYSROOT=/opt/riscv/sysroot`, otherwise many fundamental headers and libs could not be found during cross compilation. + + Then run `make` to build the project: + ```sh + make install -j$(nproc) + ``` + +### (Optional) Build the OpenVINO Runtime Python API +To enable cross-compilation with python, the library `libpython3-dev:riscv64` should be on the host machine. + +When installing packages using the utilities `apt` or `apt-get` the packages are downloaded from apt software repositories. On Ubuntu the apt software repositories are defined in the `/etc/apt/sources.list` file or in separate files under the `/etc/apt/sources.list.d/` directory. Host machine contains host-specific repositories (for example, x86-x64) in these files. + +1. Add riscv64 repositories to download riscv64-specific packages: + ```sh + echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy main >> riscv64-sources.list + echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy universe >> riscv64-sources.list + echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy-updates main >> riscv64-sources.list + echo deb [arch=riscv64] http://ports.ubuntu.com/ubuntu-ports/ jammy-security main >> riscv64-sources.list + mv riscv64-sources.list /etc/apt/sources.list.d/ + dpkg --add-architecture riscv64 + apt-get update -o Dir::Etc::sourcelist=/etc/apt/sources.list.d/riscv64-sources.list + ``` + +2. Install `libpython3-dev:riscv64` using `apt-get`: + ```sh + apt-get install -y --no-install-recommends libpython3-dev:riscv64 + ``` + Create symbolink to allow python to find `riscv64-linux-gnu/python3.10/pyconfig.h` in `/usr/include/python3.10/` (this header is initially stored in `/usr/include/riscv64-linux-gnu/`) + ```sh + ln -s /usr/include/riscv64-linux-gnu/ /usr/include/python3.10/ + ``` + +3. Add the keys `-DENABLE_PYTHON=ON -DENABLE_WHEEL=ON` to cmake command during OpenVINO build. + +> **Note**: Currently only Python 3.10 on Ubuntu 22.04 is verified. So the target device must have Python 3.10 in this case. + +### RISC-V Emulation software +In order to test applications without hardware one can use emulation software. The command line example to launch executable file with riscv64 emulation: +```sh +/bin/qemu-riscv64 -cpu= +``` + +For example, to emulate RVV 0.7.1: +```sh +/bin/qemu-riscv64 -cpu rv64,x-v=true,vext_spec=v0.7.1 +``` + +Or to emulate RVV 1.0: +```sh +/bin/qemu-riscv64 -cpu rv64,x-v=true,vext_spec=v1.0 +``` + +> **Note**: If you are using official `qemu` instead of modified version by Xuantie, you should specify the CPU model with `-cpu rv64,v=true,vext_spec=v1.0` (for `qemu` version greater than `8.0`). + +## See also + + * [OpenVINO README](../../README.md) + * [OpenVINO Developer Documentation](index.md) + * [OpenVINO Get Started](./get_started.md) + * [How to build OpenVINO](build.md) + From 45a6f33e630ff4c15b96fb8ee50d7ef489a563a1 Mon Sep 17 00:00:00 2001 From: Przemyslaw Wysocki Date: Thu, 22 Aug 2024 11:05:47 +0200 Subject: [PATCH 14/35] Allow `numpy==1.26` in MO for Python 3.12 enablement (#26118) ### Details: - `numpy==1.27` is the first version to support Python 3.12 - kaldi, mxnet and caffee will most likely fail with Python 3.12, TBD how to handle it - cc @culhatsker ### Tickets: - CVS-150017 --- tools/mo/requirements_caffe.txt | 2 +- tools/mo/requirements_kaldi.txt | 2 +- tools/mo/requirements_mxnet.txt | 2 +- tools/mo/requirements_onnx.txt | 2 +- tools/mo/requirements_tf.txt | 2 +- tools/mo/requirements_tf2.txt | 2 +- 6 files changed, 6 insertions(+), 6 deletions(-) diff --git a/tools/mo/requirements_caffe.txt b/tools/mo/requirements_caffe.txt index a8932f1873daaf..2806576890500c 100644 --- a/tools/mo/requirements_caffe.txt +++ b/tools/mo/requirements_caffe.txt @@ -1,5 +1,5 @@ -c ../constraints.txt -numpy>=1.16.6,<1.26 +numpy>=1.16.6,<1.27 networkx protobuf defusedxml diff --git a/tools/mo/requirements_kaldi.txt b/tools/mo/requirements_kaldi.txt index b31121df7bfa84..1365904679bd5f 100644 --- a/tools/mo/requirements_kaldi.txt +++ b/tools/mo/requirements_kaldi.txt @@ -1,5 +1,5 @@ -c ../constraints.txt -numpy>=1.16.6,<1.26 +numpy>=1.16.6,<1.27 networkx defusedxml requests diff --git a/tools/mo/requirements_mxnet.txt b/tools/mo/requirements_mxnet.txt index 8b6561f761b00d..bb4ec290ed5912 100644 --- a/tools/mo/requirements_mxnet.txt +++ b/tools/mo/requirements_mxnet.txt @@ -1,5 +1,5 @@ -c ../constraints.txt -numpy>=1.16.6,<1.24 +numpy>=1.16.6,<1.27 mxnet networkx defusedxml diff --git a/tools/mo/requirements_onnx.txt b/tools/mo/requirements_onnx.txt index f1f52bb03c25fa..28484f314a9d60 100644 --- a/tools/mo/requirements_onnx.txt +++ b/tools/mo/requirements_onnx.txt @@ -1,5 +1,5 @@ -c ../constraints.txt -numpy>=1.16.6,<1.26 +numpy>=1.16.6,<1.27 onnx networkx defusedxml diff --git a/tools/mo/requirements_tf.txt b/tools/mo/requirements_tf.txt index f83e3ee69f4321..abf7c3a5f6400d 100644 --- a/tools/mo/requirements_tf.txt +++ b/tools/mo/requirements_tf.txt @@ -1,7 +1,7 @@ -c ../constraints.txt h5py tensorflow>=1.15.5,<2.17.0 -numpy>=1.16.6,<1.26 +numpy>=1.16.6,<1.27 networkx defusedxml requests diff --git a/tools/mo/requirements_tf2.txt b/tools/mo/requirements_tf2.txt index af80835e3728fe..be055b38cdefa7 100644 --- a/tools/mo/requirements_tf2.txt +++ b/tools/mo/requirements_tf2.txt @@ -1,7 +1,7 @@ -c ../constraints.txt h5py tensorflow>=2.5,<2.17.0 -numpy>=1.16.6,<1.26 +numpy>=1.16.6,<1.27 networkx defusedxml requests From e26f63e397bb4b00540c698b3c7186e25999c36c Mon Sep 17 00:00:00 2001 From: Piotr Kowalczyk Date: Thu, 22 Aug 2024 11:12:31 +0200 Subject: [PATCH 15/35] [GPU]: Added initial support for bitwise shifts with unittests. (#26046) ### Details: - Added support for bitwise shifts in gpu plugin - updated tests ### Tickets: - *CVS-149424* --- .../intel_gpu/plugin/primitives_list.hpp | 4 +- .../include/intel_gpu/primitives/eltwise.hpp | 4 + src/plugins/intel_gpu/src/graph/eltwise.cpp | 16 ++- .../intel_gpu/src/graph/impls/cpu/eltwise.cpp | 8 ++ .../intel_gpu/src/graph/impls/ocl/eltwise.cpp | 8 ++ .../impls/ocl/kernel_selector_helper.cpp | 4 + .../graph/impls/ocl/kernel_selector_helper.h | 5 + .../src/kernel_selector/common_types.h | 2 + .../kernels/eltwise/eltwise_kernel_base.cpp | 8 ++ .../kernels/eltwise/eltwise_kernel_ref.cpp | 2 + .../intel_gpu/src/plugin/ops/constant.cpp | 8 +- .../intel_gpu/src/plugin/ops/eltwise.cpp | 12 +++ .../single_layer_tests/eltwise.cpp | 22 +++++ .../unit/test_cases/eltwise_gpu_test.cpp | 97 +++++++++++++++---- .../include/common_test_utils/test_enums.hpp | 4 +- .../src/node_builders/eltwise.cpp | 6 ++ .../common_test_utils/src/test_enums.cpp | 6 ++ 17 files changed, 192 insertions(+), 24 deletions(-) diff --git a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp index e61542a0c50dfc..408f3dfb7cf2d4 100644 --- a/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp +++ b/src/plugins/intel_gpu/include/intel_gpu/plugin/primitives_list.hpp @@ -265,8 +265,10 @@ REGISTER_FACTORY(v12, ScatterElementsUpdate); REGISTER_FACTORY(v13, Multinomial); REGISTER_FACTORY(v13, ScaledDotProductAttention); -// ------------------------------ Supported v14 ops ----------------------------- // +// ------------------------------ Supported v15 ops ----------------------------- // REGISTER_FACTORY(v15, ROIAlignRotated); +REGISTER_FACTORY(v15, BitwiseRightShift); +REGISTER_FACTORY(v15, BitwiseLeftShift); // --------------------------- Supported internal ops --------------------------- // REGISTER_FACTORY(internal, NonMaxSuppressionIEInternal); diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/eltwise.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/eltwise.hpp index 81612a8c660558..7913435324e89d 100644 --- a/src/plugins/intel_gpu/include/intel_gpu/primitives/eltwise.hpp +++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/eltwise.hpp @@ -54,6 +54,10 @@ enum class eltwise_mode : int32_t { is_inf, /// @brief Eltwise is nan. is_nan, + /// @brief Eltwise bitwise right shift. + right_shift, + /// @brief Eltwise bitwise left shift. + left_shift, }; /// @brief Performs elementwise operations (sum, subtract, max or product) on two input primitives diff --git a/src/plugins/intel_gpu/src/graph/eltwise.cpp b/src/plugins/intel_gpu/src/graph/eltwise.cpp index c55d84e73c6293..0791a44014a079 100644 --- a/src/plugins/intel_gpu/src/graph/eltwise.cpp +++ b/src/plugins/intel_gpu/src/graph/eltwise.cpp @@ -79,7 +79,9 @@ layout eltwise_inst::calc_output_layout(eltwise_node const& node, kernel_impl_pa eltwise_mode::floor_mod, eltwise_mode::logic_and, eltwise_mode::logic_or, - eltwise_mode::logic_xor}; + eltwise_mode::logic_xor, + eltwise_mode::right_shift, + eltwise_mode::left_shift}; if (std::find(eltwise_int_modes.begin(), eltwise_int_modes.end(), mode) == eltwise_int_modes.end()) CLDNN_ERROR_MESSAGE(desc->id, "Requested eltwise mode is not supported for integer types."); } @@ -177,7 +179,9 @@ std::vector eltwise_inst::calc_output_layouts(eltwise_node const& /*node eltwise_mode::floor_mod, eltwise_mode::logic_and, eltwise_mode::logic_or, - eltwise_mode::logic_xor}; + eltwise_mode::logic_xor, + eltwise_mode::right_shift, + eltwise_mode::left_shift}; OPENVINO_ASSERT((std::find(eltwise_int_modes.begin(), eltwise_int_modes.end(), mode) != eltwise_int_modes.end()), desc->id + "Requested eltwise mode is not supported for integer types."); @@ -305,6 +309,12 @@ std::string eltwise_inst::to_string(eltwise_node const& node) { case eltwise_mode::is_nan: str_mode = "is_nan"; break; + case eltwise_mode::right_shift: + str_mode = "right_shift"; + break; + case eltwise_mode::left_shift: + str_mode = "left_shift"; + break; default: str_mode = "not supported mode"; break; @@ -428,6 +438,8 @@ void eltwise_inst::check_inputs_count(eltwise_node const& node) { case eltwise_mode::squared_diff: case eltwise_mode::pow: case eltwise_mode::floor_mod: + case eltwise_mode::right_shift: + case eltwise_mode::left_shift: OPENVINO_ASSERT(inputs_number == 2, "Node id: ", node.id(), ". Invalid eltwise inputs number (should be equal to 2). Actual: ", inputs_number); break; diff --git a/src/plugins/intel_gpu/src/graph/impls/cpu/eltwise.cpp b/src/plugins/intel_gpu/src/graph/impls/cpu/eltwise.cpp index eb10f340d2656b..8b95cfc626fa44 100644 --- a/src/plugins/intel_gpu/src/graph/impls/cpu/eltwise.cpp +++ b/src/plugins/intel_gpu/src/graph/impls/cpu/eltwise.cpp @@ -29,6 +29,8 @@ #include "openvino/op/is_finite.hpp" #include "openvino/op/is_inf.hpp" #include "openvino/op/is_nan.hpp" +#include "openvino/op/bitwise_right_shift.hpp" +#include "openvino/op/bitwise_left_shift.hpp" namespace cldnn { namespace cpu { @@ -166,6 +168,12 @@ struct eltwise_impl : public typed_primitive_impl { case eltwise_mode::is_nan: op = std::make_shared(); break; + case eltwise_mode::right_shift: + op = std::make_shared(); + break; + case eltwise_mode::left_shift: + op = std::make_shared(); + break; default: OPENVINO_THROW("[GPU] Couldn't create eltwise operation: unsupported eltwise operation (", static_cast(mode), ")"); } diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/eltwise.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/eltwise.cpp index 1b4a63a1c240ad..0e85aa7e294d21 100644 --- a/src/plugins/intel_gpu/src/graph/impls/ocl/eltwise.cpp +++ b/src/plugins/intel_gpu/src/graph/impls/ocl/eltwise.cpp @@ -171,6 +171,7 @@ attach_eltwise_impl::attach_eltwise_impl() { data_types::i8, data_types::u8, data_types::i32, + data_types::u32, data_types::i64 }; @@ -193,12 +194,14 @@ attach_eltwise_impl::attach_eltwise_impl() { std::make_tuple(data_types::f16, format::yxfb), std::make_tuple(data_types::i8, format::yxfb), std::make_tuple(data_types::u8, format::yxfb), + std::make_tuple(data_types::u32, format::yxfb), std::make_tuple(data_types::i32, format::yxfb), std::make_tuple(data_types::i64, format::yxfb), std::make_tuple(data_types::f32, format::bfyx), std::make_tuple(data_types::f16, format::bfyx), std::make_tuple(data_types::u8, format::bfyx), + std::make_tuple(data_types::u32, format::bfyx), std::make_tuple(data_types::i8, format::bfyx), std::make_tuple(data_types::i32, format::bfyx), std::make_tuple(data_types::i64, format::bfyx), @@ -207,6 +210,7 @@ attach_eltwise_impl::attach_eltwise_impl() { std::make_tuple(data_types::f16, format::byxf), std::make_tuple(data_types::i8, format::byxf), std::make_tuple(data_types::u8, format::byxf), + std::make_tuple(data_types::u32, format::byxf), std::make_tuple(data_types::i32, format::byxf), std::make_tuple(data_types::i64, format::byxf), @@ -219,6 +223,7 @@ attach_eltwise_impl::attach_eltwise_impl() { std::make_tuple(data_types::f16, format::bfzyx), std::make_tuple(data_types::i8, format::bfzyx), std::make_tuple(data_types::u8, format::bfzyx), + std::make_tuple(data_types::u32, format::bfzyx), std::make_tuple(data_types::i32, format::bfzyx), std::make_tuple(data_types::i64, format::bfzyx), @@ -226,6 +231,7 @@ attach_eltwise_impl::attach_eltwise_impl() { std::make_tuple(data_types::f16, format::bfwzyx), std::make_tuple(data_types::i8, format::bfwzyx), std::make_tuple(data_types::u8, format::bfwzyx), + std::make_tuple(data_types::u32, format::bfwzyx), std::make_tuple(data_types::i32, format::bfwzyx), std::make_tuple(data_types::i64, format::bfwzyx), @@ -233,6 +239,7 @@ attach_eltwise_impl::attach_eltwise_impl() { std::make_tuple(data_types::f16, format::bfuwzyx), std::make_tuple(data_types::i8, format::bfuwzyx), std::make_tuple(data_types::u8, format::bfuwzyx), + std::make_tuple(data_types::u32, format::bfuwzyx), std::make_tuple(data_types::i32, format::bfuwzyx), std::make_tuple(data_types::i64, format::bfuwzyx), @@ -240,6 +247,7 @@ attach_eltwise_impl::attach_eltwise_impl() { std::make_tuple(data_types::f16, format::bfvuwzyx), std::make_tuple(data_types::i8, format::bfvuwzyx), std::make_tuple(data_types::u8, format::bfvuwzyx), + std::make_tuple(data_types::u32, format::bfvuwzyx), std::make_tuple(data_types::i32, format::bfvuwzyx), std::make_tuple(data_types::i64, format::bfvuwzyx), diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp index 2ebcebd3b0b48f..1e6d06755a7a86 100644 --- a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp +++ b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.cpp @@ -173,6 +173,8 @@ kernel_selector::data_type to_data_type(data_types dt) { return kernel_selector::data_type::UINT8; case cldnn::data_types::i32: return kernel_selector::data_type::INT32; + case cldnn::data_types::u32: + return kernel_selector::data_type::UINT32; case cldnn::data_types::i64: return kernel_selector::data_type::INT64; case cldnn::data_types::f16: @@ -198,6 +200,8 @@ data_types from_data_type(kernel_selector::data_type dt) { return cldnn::data_types::u8; case kernel_selector::data_type::INT32: return cldnn::data_types::i32; + case kernel_selector::data_type::UINT32: + return cldnn::data_types::u32; case kernel_selector::data_type::INT64: return cldnn::data_types::i64; case kernel_selector::data_type::F16: diff --git a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.h b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.h index e17fb1c7045c17..59a7e8e9627b14 100644 --- a/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.h +++ b/src/plugins/intel_gpu/src/graph/impls/ocl/kernel_selector_helper.h @@ -200,7 +200,12 @@ inline kernel_selector::eltwise_mode convert_to_eltwise_mode(eltwise_mode mode) return kernel_selector::eltwise_mode::IS_INF; case eltwise_mode::is_nan: return kernel_selector::eltwise_mode::IS_NAN; + case eltwise_mode::right_shift: + return kernel_selector::eltwise_mode::RIGHT_SHIFT; + case eltwise_mode::left_shift: + return kernel_selector::eltwise_mode::LEFT_SHIFT; default: + OPENVINO_ASSERT(false, "Unsupported eltwise mode!"); return kernel_selector::eltwise_mode::ADD; } } diff --git a/src/plugins/intel_gpu/src/kernel_selector/common_types.h b/src/plugins/intel_gpu/src/kernel_selector/common_types.h index bf96ff16aa4c8f..12e685e0b091f6 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/common_types.h +++ b/src/plugins/intel_gpu/src/kernel_selector/common_types.h @@ -304,6 +304,8 @@ enum class EltwiseMode { IS_FINITE, IS_INF, IS_NAN, + RIGHT_SHIFT, + LEFT_SHIFT, }; //////////////////////////////////////////////////////////////////////////////////////////////////////////////////////// diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_base.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_base.cpp index 94b6ac46e9362f..da46e89002e2c4 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_base.cpp +++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_base.cpp @@ -49,6 +49,8 @@ uint32_t GetNumberOfInputs(EltwiseMode m) { case EltwiseMode::LOGIC_XOR: case EltwiseMode::SQUARED_DIFF: case EltwiseMode::FLOOR_MOD: + case EltwiseMode::RIGHT_SHIFT: + case EltwiseMode::LEFT_SHIFT: return 2; case EltwiseMode::SQRT: case EltwiseMode::RSQRT: @@ -313,6 +315,12 @@ JitConstants EltwiseKernelBase::GetOperationsJitConstants(const eltwise_params& case EltwiseMode::IS_NAN: op += "(isnan(" + input0_str + "))"; break; + case EltwiseMode::RIGHT_SHIFT: + op += "(" + input0_str + " >> " + input1_str + ")"; + break; + case EltwiseMode::LEFT_SHIFT: + op += "(" + input0_str + " << " + input1_str + ")"; + break; default: break; } diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_ref.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_ref.cpp index 1f598759fb1580..71f72d68089f25 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_ref.cpp +++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/eltwise/eltwise_kernel_ref.cpp @@ -14,12 +14,14 @@ ParamsKey EltwiseKernelRef::GetSupportedKey() const { k.EnableInputDataType(Datatype::INT8); k.EnableInputDataType(Datatype::UINT8); k.EnableInputDataType(Datatype::INT32); + k.EnableInputDataType(Datatype::UINT32); k.EnableInputDataType(Datatype::INT64); k.EnableOutputDataType(Datatype::F16); k.EnableOutputDataType(Datatype::F32); k.EnableOutputDataType(Datatype::INT8); k.EnableOutputDataType(Datatype::UINT8); k.EnableOutputDataType(Datatype::INT32); + k.EnableOutputDataType(Datatype::UINT32); k.EnableOutputDataType(Datatype::INT64); k.EnableDifferentTypes(); k.EnableAllInputLayout(); diff --git a/src/plugins/intel_gpu/src/plugin/ops/constant.cpp b/src/plugins/intel_gpu/src/plugin/ops/constant.cpp index ec48aff9736a9a..f87c2b39848d09 100644 --- a/src/plugins/intel_gpu/src/plugin/ops/constant.cpp +++ b/src/plugins/intel_gpu/src/plugin/ops/constant.cpp @@ -22,6 +22,7 @@ #include "openvino/op/util/op_types.hpp" #include "openvino/op/loop.hpp" #include "openvino/op/tensor_iterator.hpp" +#include "openvino/op/util/binary_elementwise_bitwise.hpp" #include "intel_gpu/primitives/data.hpp" #include "intel_gpu/runtime/debug_configuration.hpp" @@ -126,6 +127,10 @@ static void create_data(ProgramBuilder& p, const ov::Shape& const_shape, const s } } +static bool is_btiwise(Node* node) { + return dynamic_cast(node) != nullptr; +} + static void CreateConstantOp(ProgramBuilder& p, const std::shared_ptr& op) { ov::Shape constDims = op->get_shape(); auto constUsers = op->get_output_target_inputs(0); @@ -137,7 +142,8 @@ static void CreateConstantOp(ProgramBuilder& p, const std::shared_ptr bool { if (ov::op::util::is_binary_elementwise_arithmetic(op) || ov::op::util::is_binary_elementwise_logical(op) || - ov::op::util::is_binary_elementwise_comparison(op)) { + ov::op::util::is_binary_elementwise_comparison(op) || + is_btiwise(op)) { return true; } else { return false; diff --git a/src/plugins/intel_gpu/src/plugin/ops/eltwise.cpp b/src/plugins/intel_gpu/src/plugin/ops/eltwise.cpp index 0c70da0938da59..2866e9151e214d 100644 --- a/src/plugins/intel_gpu/src/plugin/ops/eltwise.cpp +++ b/src/plugins/intel_gpu/src/plugin/ops/eltwise.cpp @@ -29,6 +29,8 @@ #include "openvino/op/xor.hpp" #include "openvino/op/power.hpp" #include "openvino/op/floor_mod.hpp" +#include "openvino/op/bitwise_right_shift.hpp" +#include "openvino/op/bitwise_left_shift.hpp" #include "intel_gpu/primitives/activation.hpp" #include "intel_gpu/primitives/eltwise.hpp" @@ -198,6 +200,14 @@ static void CreateIsNaNOp(ProgramBuilder& p, const std::shared_ptr& op) { + CreateElementwiseOp(p, op, cldnn::eltwise_mode::right_shift); +} + +static void CreateBitwiseLeftShiftOp(ProgramBuilder& p, const std::shared_ptr& op) { + CreateElementwiseOp(p, op, cldnn::eltwise_mode::left_shift); +} + REGISTER_FACTORY_IMPL(v1, Add); REGISTER_FACTORY_IMPL(v1, Multiply); REGISTER_FACTORY_IMPL(v1, Maximum); @@ -220,6 +230,8 @@ REGISTER_FACTORY_IMPL(v1, Mod); REGISTER_FACTORY_IMPL(v10, IsFinite); REGISTER_FACTORY_IMPL(v10, IsInf); REGISTER_FACTORY_IMPL(v10, IsNaN); +REGISTER_FACTORY_IMPL(v15, BitwiseRightShift); +REGISTER_FACTORY_IMPL(v15, BitwiseLeftShift); } // namespace intel_gpu } // namespace ov diff --git a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eltwise.cpp b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eltwise.cpp index 526bde9495661d..c5447983ba9492 100644 --- a/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eltwise.cpp +++ b/src/plugins/intel_gpu/tests/functional/shared_tests_instances/single_layer_tests/eltwise.cpp @@ -66,8 +66,30 @@ std::vector eltwiseOpTypes = { EltwiseTypes::MOD }; +std::vector smoke_intOnly_eltwiseOpTypes = { + EltwiseTypes::RIGHT_SHIFT +}; + +std::vector intOnly_netPrecisions = { + ov::element::i32 +}; + ov::AnyMap additional_config = {}; +INSTANTIATE_TEST_SUITE_P( + smoke_intOnly_CompareWithRefs, + EltwiseLayerTest, + ::testing::Combine(::testing::ValuesIn(ov::test::static_shapes_to_test_representation(inShapes)), + ::testing::ValuesIn(smoke_intOnly_eltwiseOpTypes), + ::testing::ValuesIn(secondaryInputTypes), + ::testing::ValuesIn(opTypes), + ::testing::ValuesIn(intOnly_netPrecisions), + ::testing::Values(ov::element::undefined), + ::testing::Values(ov::element::undefined), + ::testing::Values(ov::test::utils::DEVICE_GPU), + ::testing::Values(additional_config)), + EltwiseLayerTest::getTestCaseName); + INSTANTIATE_TEST_SUITE_P( smoke_CompareWithRefs, EltwiseLayerTest, diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/eltwise_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/eltwise_gpu_test.cpp index 690e570e42ec68..1ae82b70ff1d41 100644 --- a/src/plugins/intel_gpu/tests/unit/test_cases/eltwise_gpu_test.cpp +++ b/src/plugins/intel_gpu/tests/unit/test_cases/eltwise_gpu_test.cpp @@ -170,8 +170,8 @@ void run_eltwise_generic_test(cldnn::eltwise_mode mode) { } -template -int8_t eltwise_bool_execute(cldnn::eltwise_mode mode, T x, T y) { +template +TOut eltwise_int_execute(cldnn::eltwise_mode mode, T x, T y) { switch (mode) { case eltwise_mode::eq: return x == y; @@ -189,13 +189,17 @@ int8_t eltwise_bool_execute(cldnn::eltwise_mode mode, T x, T y) { return x && y; case eltwise_mode::logic_or: return x || y; + case eltwise_mode::right_shift: + return x >> y; + case eltwise_mode::left_shift: + return x << y; default: - return (int8_t)0; + return (TOut)0; } } -template -VVVVF eltwise_bool_reference(VVVVF &input1, VVVVF &input2, +template +VVVVF eltwise_int_reference(VVVVF &input1, VVVVF &input2, cldnn::eltwise_mode mode, int input_padding_y = 0, int input_padding_x = 0, int output_padding_y = 0, int output_padding_x = 0) { @@ -206,14 +210,14 @@ VVVVF eltwise_bool_reference(VVVVF &input1, VVVVF &input2, size_t output_f = input1[0].size(); size_t output_y = input1[0][0].size() + 2 * padding_y; size_t output_x = input1[0][0][0].size() + 2 * padding_x; - VVVVF output(output_b, VVVF(output_f, VVF(output_y, VF(output_x)))); + VVVVF output(output_b, VVVF(output_f, VVF(output_y, VF(output_x)))); T res; for (size_t b = 0; b < output_b; ++b) { for (size_t f = 0; f < output_f; ++f) { for (size_t y = 0; y < input1[0][0].size(); ++y) { for (size_t x = 0; x < input1[0][0][0].size(); ++x) { - res = eltwise_bool_execute(mode, input1[b][f][y][x], input2[b][f][y][x]); + res = eltwise_int_execute(mode, input1[b][f][y][x], input2[b][f][y][x]); output[b][f][y + padding_y][x + padding_x] = res; } } @@ -222,14 +226,28 @@ VVVVF eltwise_bool_reference(VVVVF &input1, VVVVF &input2, return output; } -template -void generic_eltwise_bool_test(cldnn::format test_input_fmt, int input_b, int input_f, int input_y, int input_x, cldnn::eltwise_mode mode, - int input_padding_y, int input_padding_x, int output_padding_y, int output_padding_x) { +template +void generic_eltwise_int_test(cldnn::format test_input_fmt, + int input_b, + int input_f, + int input_y, + int input_x, + cldnn::eltwise_mode mode, + int input_padding_y, + int input_padding_x, + int output_padding_y, + int output_padding_x, + int input1_min_val, + int input1_max_val, + int input2_min_val, + int input2_max_val) { + static_assert(std::is_integral::value, "T must be an integral type"); + static_assert(std::is_integral::value, "TOut must be an integral type"); + tests::random_generator rg(GET_SUITE_NAME); - int min_random = -2, max_random = 2; - VVVVF input1_rnd = rg.generate_random_4d(input_b, input_f, input_y, input_x, min_random, max_random); - VVVVF input2_rnd = rg.generate_random_4d(input_b, input_f, input_y, input_x, min_random, max_random); + VVVVF input1_rnd = rg.generate_random_4d(input_b, input_f, input_y, input_x, input1_min_val, input1_max_val); + VVVVF input2_rnd = rg.generate_random_4d(input_b, input_f, input_y, input_x, input2_min_val, input2_max_val); VF input1_rnd_vec = flatten_4d(test_input_fmt, input1_rnd); VF input2_rnd_vec = flatten_4d(test_input_fmt, input2_rnd); @@ -257,9 +275,9 @@ void generic_eltwise_bool_test(cldnn::format test_input_fmt, int input_b, int in auto output_memory = outputs.at("eltwise").get_memory(); auto output_layout = output_memory->get_layout(); - cldnn::mem_lock output_ptr(output_memory, get_test_stream()); + cldnn::mem_lock output_ptr(output_memory, get_test_stream()); - VVVVF output_cpu = eltwise_bool_reference(input1_rnd, input2_rnd, mode, input_padding_y, input_padding_x, output_padding_y, output_padding_x); + VVVVF output_cpu = eltwise_int_reference(input1_rnd, input2_rnd, mode, input_padding_y, input_padding_x, output_padding_y, output_padding_x); ASSERT_EQ(output_layout.format.value, test_input_fmt.value); auto output_tensor = output_layout.get_padded_dims(); int x_size = output_tensor[3]; @@ -273,9 +291,11 @@ void generic_eltwise_bool_test(cldnn::format test_input_fmt, int input_b, int in ASSERT_EQ(b_size, (int)output_cpu.size()); bool test_is_correct = true; - VF output_cpu_vec = flatten_4d(test_input_fmt, output_cpu); + VF output_cpu_vec = flatten_4d(test_input_fmt, output_cpu); for (size_t i = 0; i < output_cpu_vec.size(); ++i) { - if (output_cpu_vec[i] != output_ptr[i]) { + const TOut cpu_val = output_cpu_vec[i]; + const TOut gpu_val = output_ptr[i]; + if (cpu_val != gpu_val) { test_is_correct = false; break; } @@ -299,8 +319,39 @@ void run_eltwise_bool_generic_test(cldnn::eltwise_mode mode) cldnn::format test_inputs_fmt = cldnn::format::bfyx; std::pair input_size = { 227, 227 }; - generic_eltwise_bool_test(test_inputs_fmt, 1, 1, input_size.first, input_size.second, mode, 0, 0, 0, 0); - generic_eltwise_bool_test(test_inputs_fmt, 1, 1, input_size.first, input_size.second, mode, 0, 0, 0, 0); + generic_eltwise_int_test(test_inputs_fmt, 1, 1, input_size.first, input_size.second, mode, 0, 0, 0, 0, -2, 2, -2, 2); + generic_eltwise_int_test(test_inputs_fmt, 1, 1, input_size.first, input_size.second, mode, 0, 0, 0, 0, -2, 2, -2, 2); +} + +void run_eltwise_int_shift_generic_test(cldnn::eltwise_mode mode) { + OPENVINO_ASSERT(mode == eltwise_mode::right_shift || mode == eltwise_mode::left_shift, + "Only right_shift amd left_shift mode is supported for this test"); + cldnn::format test_inputs_fmt = cldnn::format::bfyx; + const int dim_size = 227; + +#define ELTWISE_INT_TEST_CASES(type) \ + generic_eltwise_int_test(test_inputs_fmt, \ + 1, \ + 1, \ + dim_size, \ + dim_size, \ + mode, \ + 0, \ + 0, \ + 0, \ + 0, \ + 0, \ + static_cast(std::numeric_limits::max()) / 2, \ + 0, \ + ((sizeof(type) * 8) - 1) / 2); + + ELTWISE_INT_TEST_CASES(int8_t); + ELTWISE_INT_TEST_CASES(uint8_t); + ELTWISE_INT_TEST_CASES(int32_t); + ELTWISE_INT_TEST_CASES(uint32_t); + ELTWISE_INT_TEST_CASES(int64_t); + +#undef ELTWISE_INT_TEST_CASES } } // namespace @@ -3894,6 +3945,14 @@ TEST(eltwise_gpu_bool, eltwise_or) { run_eltwise_bool_generic_test(cldnn::eltwise_mode::logic_or); } +TEST(eltwise_gpu, eltwise_right_shift) { + run_eltwise_int_shift_generic_test(cldnn::eltwise_mode::right_shift); +} + +TEST(eltwise_gpu, eltwise_left_shift) { + run_eltwise_int_shift_generic_test(cldnn::eltwise_mode::left_shift); +} + TEST(eltwise_gpu, eltwise_div) { run_eltwise_generic_test(cldnn::eltwise_mode::div); } diff --git a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_enums.hpp b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_enums.hpp index 7b8e4b8834f854..8a7fe4fec02874 100644 --- a/src/tests/test_utils/common_test_utils/include/common_test_utils/test_enums.hpp +++ b/src/tests/test_utils/common_test_utils/include/common_test_utils/test_enums.hpp @@ -60,7 +60,9 @@ enum EltwiseTypes { BITWISE_AND, BITWISE_NOT, BITWISE_OR, - BITWISE_XOR + BITWISE_XOR, + RIGHT_SHIFT, + LEFT_SHIFT }; enum SqueezeOpType { diff --git a/src/tests/test_utils/common_test_utils/src/node_builders/eltwise.cpp b/src/tests/test_utils/common_test_utils/src/node_builders/eltwise.cpp index 262ceb30187137..b5c792aa26e021 100644 --- a/src/tests/test_utils/common_test_utils/src/node_builders/eltwise.cpp +++ b/src/tests/test_utils/common_test_utils/src/node_builders/eltwise.cpp @@ -6,8 +6,10 @@ #include "openvino/op/add.hpp" #include "openvino/op/bitwise_and.hpp" +#include "openvino/op/bitwise_left_shift.hpp" #include "openvino/op/bitwise_not.hpp" #include "openvino/op/bitwise_or.hpp" +#include "openvino/op/bitwise_right_shift.hpp" #include "openvino/op/bitwise_xor.hpp" #include "openvino/op/divide.hpp" #include "openvino/op/erf.hpp" @@ -51,6 +53,10 @@ std::shared_ptr make_eltwise(const ov::Output& in0, return std::make_shared(in0, in1); case ov::test::utils::EltwiseTypes::BITWISE_XOR: return std::make_shared(in0, in1); + case ov::test::utils::EltwiseTypes::RIGHT_SHIFT: + return std::make_shared(in0, in1); + case ov::test::utils::EltwiseTypes::LEFT_SHIFT: + return std::make_shared(in0, in1); default: { OPENVINO_THROW("Incorrect type of Eltwise operation"); } diff --git a/src/tests/test_utils/common_test_utils/src/test_enums.cpp b/src/tests/test_utils/common_test_utils/src/test_enums.cpp index 83ab16fc34d2d0..219aeec1e9fa50 100644 --- a/src/tests/test_utils/common_test_utils/src/test_enums.cpp +++ b/src/tests/test_utils/common_test_utils/src/test_enums.cpp @@ -82,6 +82,12 @@ std::ostream& operator<<(std::ostream& os, const ov::test::utils::EltwiseTypes t case ov::test::utils::EltwiseTypes::BITWISE_XOR: os << "BitwiseXor"; break; + case ov::test::utils::EltwiseTypes::RIGHT_SHIFT: + os << "BitwiseRightShift"; + break; + case ov::test::utils::EltwiseTypes::LEFT_SHIFT: + os << "BitwiseLeftShift"; + break; default: throw std::runtime_error("NOT_SUPPORTED_OP_TYPE"); } From 49601012d05a9e5bb5b36ce42e96668a80d2c893 Mon Sep 17 00:00:00 2001 From: Mingyu Kim Date: Thu, 22 Aug 2024 19:28:34 +0900 Subject: [PATCH 16/35] [GPU] propagate fc output type for dynamic quantization case (#26133) ### Details: - When FC output type is undefined, propagate the original input type - Otherwise, dynamic_quantized type is propagated ### Tickets: - 149663 --- .../dynamic_quantize_fully_connected.cpp | 7 +++- .../transformations/fc_horizontal_fusion.cpp | 13 +++++- .../dynamic/dynamic_fc_horizontal_fusion.cpp | 42 ++++++++++++++++--- .../dynamic/matmul_weights_decompression.cpp | 6 +-- 4 files changed, 56 insertions(+), 12 deletions(-) diff --git a/src/plugins/intel_gpu/src/plugin/transformations/dynamic_quantize_fully_connected.cpp b/src/plugins/intel_gpu/src/plugin/transformations/dynamic_quantize_fully_connected.cpp index a2e4a1a38983c8..eb16213bcb936c 100644 --- a/src/plugins/intel_gpu/src/plugin/transformations/dynamic_quantize_fully_connected.cpp +++ b/src/plugins/intel_gpu/src/plugin/transformations/dynamic_quantize_fully_connected.cpp @@ -64,13 +64,18 @@ DynamicQuantizeFullyConnected::DynamicQuantizeFullyConnected(uint64_t group_size auto dyn_quan = std::make_shared(m_data, shape_group_size, element::f16); auto optional_w_zp = m_fc->get_input_size() > 4 ? m_fc->get_input_node_shared_ptr(4) : std::make_shared(); + auto output_type = m_fc->get_output_type(); + if (output_type == ov::element::undefined) + output_type = m_fc->get_input_element_type(0); + auto new_fc = std::make_shared(dyn_quan->output(0), m_fc->get_input_node_shared_ptr(1), m_fc->get_input_node_shared_ptr(2), m_fc->get_input_node_shared_ptr(3), optional_w_zp, dyn_quan->output(1), - m_fc->get_output_type()); + output_type); + ov::replace_node(m_fc, new_fc); new_fc->set_friendly_name(m_fc->get_friendly_name()); diff --git a/src/plugins/intel_gpu/src/plugin/transformations/fc_horizontal_fusion.cpp b/src/plugins/intel_gpu/src/plugin/transformations/fc_horizontal_fusion.cpp index 14bb5596b1cc12..a5ec6c0060ef81 100644 --- a/src/plugins/intel_gpu/src/plugin/transformations/fc_horizontal_fusion.cpp +++ b/src/plugins/intel_gpu/src/plugin/transformations/fc_horizontal_fusion.cpp @@ -169,9 +169,18 @@ FullyConnectedHorizontalFusion::FullyConnectedHorizontalFusion() { // Create new fc with merged weights, bias, scale, zp std::shared_ptr new_fc; if (fused_zps) - new_fc = std::make_shared(input_node, fused_weight, fused_bias, fused_scale, fused_zps); + new_fc = std::make_shared(input_node, + fused_weight, + fused_bias, + fused_scale, + fused_zps, + fc_nodes[0]->get_output_type()); else - new_fc = std::make_shared(input_node, fused_weight, fused_bias, fused_scale); + new_fc = std::make_shared(input_node, + fused_weight, + fused_bias, + fused_scale, + fc_nodes[0]->get_output_type()); auto new_fc_name = fc_nodes[0]->get_friendly_name() + "_fused"; new_fc->set_friendly_name(new_fc_name); diff --git a/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/dynamic_fc_horizontal_fusion.cpp b/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/dynamic_fc_horizontal_fusion.cpp index 4aad7f554a0dfa..47dfb8ab3b80da 100644 --- a/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/dynamic_fc_horizontal_fusion.cpp +++ b/src/plugins/intel_gpu/tests/functional/subgraph_tests/dynamic/dynamic_fc_horizontal_fusion.cpp @@ -38,7 +38,9 @@ using FullyConnectedHorizontalFusionParams = std::tuple; // has biahs + bool, // has bias + uint64_t // dynamic_quantization_group_size + >; class FullyConnectedHorizontalFusion : public testing::WithParamInterface, @@ -53,6 +55,7 @@ class FullyConnectedHorizontalFusion : public testing::WithParamInterface& m_pool_gpr_idxs; + Reg64 m_aux_gpr_idx {}; + bool m_is_preserved = false; +}; +} // namespace + /* ================== jit_loop_begin_emitter ====================== */ jit_loop_begin_emitter::jit_loop_begin_emitter(dnnl::impl::cpu::x64::jit_generator* h, dnnl::impl::cpu::x64::cpu_isa_t isa, @@ -30,12 +76,6 @@ jit_loop_begin_emitter::jit_loop_begin_emitter(dnnl::impl::cpu::x64::jit_generat in_out_type_ = emitter_in_out_map::gpr_to_gpr; } -size_t jit_loop_begin_emitter::aux_gprs_count() const { - // We should have aux GPR to store Loop arguments from `runtime_args` - // where we will take all needed information about the current loop: work amount - return is_work_amount_dynamic ? 1 : 0; -} - void jit_loop_begin_emitter::validate_arguments(const std::vector &in, const std::vector &out) const { OV_CPU_JIT_EMITTER_ASSERT(in.empty(), "Invalid inputs size: expected 0 got " + std::to_string(in.size())); // Note: the only expected output is work amount register (communicated to jit_loop_end_emitter) @@ -59,10 +99,10 @@ void jit_loop_begin_emitter::emit_impl(const std::vector& in, const std: Reg64 reg_work_amount = Reg64(static_cast(out.back())); if (is_work_amount_dynamic) { - Reg64 reg_runtime_params = abi_param1; // defined by jit_kernel_emitter - Reg64 reg_loop_args_ptr = Reg64(static_cast(aux_gpr_idxs[0])); + jit_aux_gpr_holder gpr_holder(h, aux_gpr_idxs, out); // loop_begin has only output registers + Reg64 reg_loop_args_ptr = gpr_holder.get_reg(); const auto id_offset = loop_id * sizeof(jit_snippets_call_args::loop_args_t); - h->mov(reg_loop_args_ptr, h->ptr[reg_runtime_params + GET_OFF(loop_args)]); + h->mov(reg_loop_args_ptr, h->ptr[abi_param1 + GET_OFF(loop_args)]); h->mov(reg_work_amount, h->ptr[reg_loop_args_ptr + id_offset + GET_OFF_LOOP_ARGS(m_work_amount)]); } else { h->mov(reg_work_amount, work_amount); @@ -141,37 +181,37 @@ void jit_loop_end_emitter::emit_code(const std::vector &in, const std::v jit_emitter::emit_code(in, out, pool_vec_idxs, pool_gpr_idxs); } -size_t jit_loop_end_emitter::aux_gprs_count() const { - // We should have aux GPR to store Loop arguments from `runtime_args` - // where we will take all needed information about the current loop: data pointer shifts - return are_ptr_shifts_dynamic ? 1 : 0; -} - void jit_loop_end_emitter::emit_impl(const std::vector& in, const std::vector& out) const { std::vector data_ptr_reg_idxs; // the last input is actually a work_amount reg data_ptr_reg_idxs.reserve(num_inputs + num_outputs); std::copy(in.begin(), in.end() - 1, std::back_inserter(data_ptr_reg_idxs)); - const auto id_offset = loop_id * sizeof(jit_snippets_call_args::loop_args_t); - Reg64 reg_increments = are_ptr_shifts_dynamic ? Reg64(static_cast(aux_gpr_idxs[0])) : Reg64(); - auto apply_increments = [&](bool use_runtime_args, size_t field_offset, const std::vector& increments, size_t scale) { - if (use_runtime_args) { - Reg64 reg_runtime_params = abi_param1; /* defined by jit_kernel_emitter */ - h->mov(reg_increments, h->ptr[reg_runtime_params + GET_OFF(loop_args)]); - h->mov(reg_increments, h->ptr[reg_increments + id_offset + field_offset]); - } - for (size_t idx = 0; idx < data_ptr_reg_idxs.size(); idx++) { - const auto& increment = increments[idx]; - if (is_incremented[idx] && increment != 0) { - if (ov::snippets::utils::is_dynamic_value(increment)) { - OV_CPU_JIT_EMITTER_ASSERT(use_runtime_args, "Loop argument structure cannot be pushed to aux GPR"); - h->add(Reg64(static_cast(data_ptr_reg_idxs[idx])), h->ptr[reg_increments + idx * sizeof(int64_t)]); - } else { - h->add(Reg64(static_cast(data_ptr_reg_idxs[idx])), increment * scale * data_sizes[idx]); + Reg64 reg_increments; + auto add_increments = [&]() { + for (size_t idx = 0; idx < data_ptr_reg_idxs.size(); idx++) { + const auto& increment = increments[idx]; + if (is_incremented[idx] && increment != 0) { + if (ov::snippets::utils::is_dynamic_value(increment)) { + OV_CPU_JIT_EMITTER_ASSERT(use_runtime_args, "Loop argument structure cannot be pushed to aux GPR"); + h->add(Reg64(static_cast(data_ptr_reg_idxs[idx])), h->ptr[reg_increments + idx * sizeof(int64_t)]); + } else { + h->add(Reg64(static_cast(data_ptr_reg_idxs[idx])), increment * scale * data_sizes[idx]); + } } } + }; + + const auto id_offset = loop_id * sizeof(jit_snippets_call_args::loop_args_t); + if (use_runtime_args) { + jit_aux_gpr_holder gpr_holder(h, aux_gpr_idxs, in); // loop_end has only input registers + reg_increments = gpr_holder.get_reg(); + h->mov(reg_increments, h->ptr[abi_param1 + GET_OFF(loop_args)]); + h->mov(reg_increments, h->ptr[reg_increments + id_offset + field_offset]); + add_increments(); + } else { + add_increments(); } }; diff --git a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_loop_emitters.hpp b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_loop_emitters.hpp index d7444dfd55cebc..262bba39b7d74c 100644 --- a/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_loop_emitters.hpp +++ b/src/plugins/intel_cpu/src/emitters/snippets/x64/jit_loop_emitters.hpp @@ -31,7 +31,8 @@ class jit_loop_begin_emitter: public jit_emitter { void validate_arguments(const std::vector &in, const std::vector &out) const override; void emit_impl(const std::vector& in, const std::vector& out) const override; - size_t aux_gprs_count() const override; + // `jit_loop_begin_emitter` handles manually aux_gpr allocation using `jit_aux_gpr_holder` + size_t aux_gprs_count() const override { return 0; } std::shared_ptr loop_begin_label = nullptr; std::shared_ptr loop_end_label = nullptr; @@ -61,7 +62,8 @@ class jit_loop_end_emitter: public jit_emitter { void validate_arguments(const std::vector &in, const std::vector &out) const override; void emit_impl(const std::vector& in, const std::vector& out) const override; - size_t aux_gprs_count() const override; + // `jit_loop_end_emitter` handles manually aux_gpr allocation using `jit_aux_gpr_holder` + size_t aux_gprs_count() const override { return 0; } static ov::snippets::lowered::ExpressionPtr get_loop_begin_expr(const ov::snippets::lowered::ExpressionPtr& expr); diff --git a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/mha.cpp b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/mha.cpp index 19f1b230cd2c58..62edcba0de74e3 100644 --- a/src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/mha.cpp +++ b/src/plugins/intel_cpu/tests/functional/shared_tests_instances/snippets/mha.cpp @@ -527,6 +527,35 @@ INSTANTIATE_TEST_SUITE_P( ::testing::Values(CPUTestUtils::empty_plugin_config)), MHA::getTestCaseName); +std::vector> inputShapes_4D_WithMul_dynamic{ + { + {PartialShape{-1, -1, -1, -1}, {{1, 128, 3, 64}, {1, 70, 3, 19}, {1, 128, 3, 64}, {1, 68, 6, 87}}}, + {PartialShape{-1, -1, -1, -1}, {{1, 128, 1, 64}, {2, 49, 1, 19}, {1, 128, 1, 64}, {2, 13, 6, 87}}}, + {PartialShape{1}, {{1}, {1}, {1}, {1} }}, + {PartialShape{-1, -1, -1, -1}, {{2, 1, 128, 128}, {1, 1, 70, 49}, {2, 1, 128, 128}, {1, 1, 68, 13}}}, + {PartialShape{-1, -1, -1, -1}, {{1, 128, 3, 64}, {1, 49, 3, 19}, {1, 128, 3, 64}, {2, 13, 6, 87}}}, + }, + { + {PartialShape{-1, -1, 12, 64}, {{1, 70, 12, 64}, {1, 20, 12, 64}, {1, 20, 12, 64}, {1, 20, 12, 64}, {1, 70, 12, 64}}}, + {PartialShape{-1, -1, 12, 64}, {{1, 35, 12, 64}, {2, 10, 12, 64}, {2, 1, 12, 64}, {2, 10, 12, 64}, {1, 35, 12, 64}}}, + {PartialShape{-1, 12, 64, -1}, {{1, 12, 64, 35}, {1, 12, 64, 10}, {1, 12, 64, 10}, {1, 12, 64, 1}, {1, 12, 64, 35}}}, + {PartialShape{-1, 12, -1, -1}, {{2, 12, 70, 35}, {1, 12, 20, 10}, {1, 12, 20, 10}, {1, 12, 20, 1}, {2, 12, 70, 35}}}, + {PartialShape{-1, -1, 12, 64}, {{1, 35, 12, 64}, {1, 10, 12, 64}, {1, 10, 12, 64}, {1, 10, 12, 64}, {1, 35, 12, 64}}}, + } +}; + +INSTANTIATE_TEST_SUITE_P(smoke_Snippets_DynMHA_4D_WithMul, + MHAWithDynamicMul, + ::testing::Combine(::testing::ValuesIn(inputShapes_4D_WithMul_dynamic), + ::testing::ValuesIn(precision_f32(5)), + ::testing::Values(ov::element::f32), + ::testing::Values(MHA::default_thread_count), + ::testing::Values(1), + ::testing::Values(1), + ::testing::Values(ov::test::utils::DEVICE_CPU), + ::testing::Values(CPUTestUtils::empty_plugin_config)), + MHAWithDynamicMul::getTestCaseName); + } // namespace } // namespace snippets } // namespace test diff --git a/src/tests/functional/plugin/shared/include/snippets/mha.hpp b/src/tests/functional/plugin/shared/include/snippets/mha.hpp index f73dba5d4ad5ce..0a1733a6099242 100644 --- a/src/tests/functional/plugin/shared/include/snippets/mha.hpp +++ b/src/tests/functional/plugin/shared/include/snippets/mha.hpp @@ -11,7 +11,7 @@ namespace ov { namespace test { namespace snippets { -typedef std::tuple, // Input shapes +typedef std::tuple, // Input shapes std::vector, // Input Element types ov::element::Type, // Inference precision bool, // With Multiply @@ -23,72 +23,101 @@ typedef std::tuple, // Input shapes > MHAParams; -class MHA : public testing::WithParamInterface, - virtual public ov::test::SnippetsTestsCommon { -public: - static std::string getTestCaseName(testing::TestParamInfo obj); +typedef std::tuple, // Input shapes + std::vector, // Input Element types + ov::element::Type, // Inference precision + size_t, // Thread count + size_t, // Expected num nodes + size_t, // Expected num subgraphs + std::string, // Target Device + ov::AnyMap // Config + > +MHAWithDynamicMulParams; +class MHABase : virtual public ov::test::SnippetsTestsCommon { +public: constexpr static size_t default_thread_count = 0; protected: void SetUp() override; - void compile_model() override; void generate_inputs(const std::vector& targetInputStaticShapes) override; - virtual std::shared_ptr get_subgraph(); + virtual std::shared_ptr get_subgraph() const = 0; + virtual void init_params(std::vector& input_shapes, ov::element::Type& prc, ov::AnyMap& additional_config) = 0; - bool m_with_mul = false; size_t m_thread_count; std::vector m_input_types; }; +class MHA : public testing::WithParamInterface, + virtual public MHABase { +public: + static std::string getTestCaseName(testing::TestParamInfo obj); + +protected: + std::shared_ptr get_subgraph() const override; + void init_params(std::vector& input_shapes, ov::element::Type& prc, ov::AnyMap& additional_config) override; + + bool m_with_mul = false; +}; + class MHASelect : public MHA { protected: void generate_inputs(const std::vector& targetInputStaticShapes) override; - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHAWOTransposeOnInputs : public MHA { protected: - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHAWOTranspose : public MHA { protected: - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHAMulAdd : public MHA { - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHATransposedB : public MHA { - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHAINT8MatMul : public MHA { protected: - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHAQuantMatMul0 : public MHA { protected: - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHAFQAfterMatMul : public MHA { protected: - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHAFQ : public MHA { protected: - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; }; class MHAWithExtractedReshape : public MHA { protected: - std::shared_ptr get_subgraph() override; + std::shared_ptr get_subgraph() const override; +}; + +class MHAWithDynamicMul : public testing::WithParamInterface, + virtual public MHABase { +public: + static std::string getTestCaseName(testing::TestParamInfo obj); + +protected: + std::shared_ptr get_subgraph() const override; + void init_params(std::vector& input_shapes, ov::element::Type& prc, ov::AnyMap& additional_config) override; }; } // namespace snippets diff --git a/src/tests/functional/plugin/shared/src/snippets/mha.cpp b/src/tests/functional/plugin/shared/src/snippets/mha.cpp index 9b5cbe2bafaf43..956b115213e3f1 100644 --- a/src/tests/functional/plugin/shared/src/snippets/mha.cpp +++ b/src/tests/functional/plugin/shared/src/snippets/mha.cpp @@ -14,6 +14,51 @@ namespace ov { namespace test { namespace snippets { +void MHABase::compile_model() { + if (m_thread_count != default_thread_count) + core->set_property(targetDevice, ov::inference_num_threads(m_thread_count)); + SubgraphBaseTest::compile_model(); +} + +void MHABase::generate_inputs(const std::vector& targetInputStaticShapes) { + inputs.clear(); + const auto& model_inputs = function->inputs(); + + for (int i = 0; i < model_inputs.size(); ++i) { + const auto& model_input = model_inputs[i]; + ov::Tensor tensor; + ov::test::utils::InputGenerateData in_data; + // To avoid big relative errors in the vicinity of zero, only positive values are generated for bf16 precision + in_data.start_from = model_input.get_element_type() == ov::element::bf16 ? 0 : -1; + in_data.range = 2; + in_data.resolution = 256; + tensor = + ov::test::utils::create_and_fill_tensor(model_input.get_element_type(), targetInputStaticShapes[i], in_data); + inputs.insert({model_input.get_node_shared_ptr(), tensor}); + } +} + +void MHABase::SetUp() { + std::vector input_shapes; + ov::element::Type prc; + ov::AnyMap additional_config; + init_params(input_shapes, prc, additional_config); + init_input_shapes(input_shapes); + + const auto subgraph_model = get_subgraph(); + function = subgraph_model->getOriginal(); + + configuration.insert(additional_config.begin(), additional_config.end()); + if (!configuration.count("SNIPPETS_MODE")) { + configuration.insert({"SNIPPETS_MODE", "IGNORE_CALLBACK"}); + } + + setInferenceType(prc); + inType = outType = prc; + if (prc == ov::element::bf16) + rel_threshold = 0.05f; +} + std::string MHA::getTestCaseName(testing::TestParamInfo obj) { std::vector input_shapes; std::vector elem_types; @@ -54,60 +99,46 @@ std::string MHA::getTestCaseName(testing::TestParamInfo obj) { std::vector input_shapes; + std::vector elem_types; ov::element::Type prc; + size_t thread_count; + std::string target_device; + size_t num_nodes, num_subgraphs; ov::AnyMap additional_config; - std::tie(input_shapes, - m_input_types, - prc, - m_with_mul, - m_thread_count, - ref_num_nodes, - ref_num_subgraphs, - targetDevice, - additional_config) = this->GetParam(); - init_input_shapes(input_shapes); + std::tie(input_shapes, elem_types, prc, thread_count, num_nodes, num_subgraphs, target_device, additional_config) = obj.param; - const auto subgraph_model = get_subgraph(); - function = subgraph_model->getOriginal(); + std::ostringstream result; + for (size_t i = 0; i < input_shapes.size(); i++) + result << "IS[" << i << "]=" << input_shapes[i] << "_"; + for (size_t i = 0; i < elem_types.size(); i++) + result << "T[" << i << "]=" << elem_types[i] << "_"; + result << "ThreadNum=" << thread_count << "_"; + result << "PRC=" << prc << "_"; + result << "#N=" << num_nodes << "_"; + result << "#S=" << num_subgraphs << "_"; + result << "targetDevice=" << target_device << "_"; - configuration.insert(additional_config.begin(), additional_config.end()); - if (!configuration.count("SNIPPETS_MODE")) { - configuration.insert({"SNIPPETS_MODE", "IGNORE_CALLBACK"}); + if (!additional_config.empty()) { + result << "_PluginConf"; + for (auto& item : additional_config) { + result << "_" << item.first << "=" << item.second.as(); + } } - - setInferenceType(prc); - inType = outType = prc; - if (prc == ov::element::bf16) - rel_threshold = 0.05f; + return result.str(); } -void MHA::compile_model() { - if (m_thread_count != default_thread_count) - core->set_property(targetDevice, ov::inference_num_threads(m_thread_count)); - SubgraphBaseTest::compile_model(); +void MHA::init_params(std::vector& input_shapes, ov::element::Type& prc, ov::AnyMap& additional_config) { + std::tie(input_shapes, m_input_types, prc, m_with_mul, m_thread_count, ref_num_nodes, ref_num_subgraphs, + targetDevice, additional_config) = this->GetParam(); } -void MHA::generate_inputs(const std::vector& targetInputStaticShapes) { - inputs.clear(); - const auto& model_inputs = function->inputs(); - - for (int i = 0; i < model_inputs.size(); ++i) { - const auto& model_input = model_inputs[i]; - ov::Tensor tensor; - ov::test::utils::InputGenerateData in_data; - // To avoid big relative errors in the vicinity of zero, only positive values are generated for bf16 precision - in_data.start_from = model_input.get_element_type() == ov::element::bf16 ? 0 : -1; - in_data.range = 2; - in_data.resolution = 256; - tensor = - ov::test::utils::create_and_fill_tensor(model_input.get_element_type(), targetInputStaticShapes[i], in_data); - inputs.insert({model_input.get_node_shared_ptr(), tensor}); - } +void MHAWithDynamicMul::init_params(std::vector& input_shapes, ov::element::Type& prc, ov::AnyMap& additional_config) { + std::tie(input_shapes, m_input_types, prc, m_thread_count, ref_num_nodes, ref_num_subgraphs, targetDevice, additional_config) = this->GetParam(); } -std::shared_ptr MHA::get_subgraph() { +std::shared_ptr MHA::get_subgraph() const { bool is_with_reshape = std::all_of(inputDynamicShapes.begin(), inputDynamicShapes.end(), [](const PartialShape& ps){ return ps.is_static(); }); return std::make_shared(inputDynamicShapes, m_input_types, m_with_mul, is_with_reshape); } @@ -143,46 +174,50 @@ void MHASelect::generate_inputs(const std::vector& targetInputStaticS } } -std::shared_ptr MHASelect::get_subgraph() { +std::shared_ptr MHASelect::get_subgraph() const { return std::make_shared(inputDynamicShapes, m_input_types); } -std::shared_ptr MHAWOTransposeOnInputs::get_subgraph() { +std::shared_ptr MHAWOTransposeOnInputs::get_subgraph() const { return std::make_shared(inputDynamicShapes); } -std::shared_ptr MHAWOTranspose::get_subgraph() { +std::shared_ptr MHAWOTranspose::get_subgraph() const { return std::make_shared(inputDynamicShapes, m_input_types); } -std::shared_ptr MHAINT8MatMul::get_subgraph() { +std::shared_ptr MHAINT8MatMul::get_subgraph() const { return std::make_shared(inputDynamicShapes); } -std::shared_ptr MHAQuantMatMul0::get_subgraph() { +std::shared_ptr MHAQuantMatMul0::get_subgraph() const { return std::make_shared(inputDynamicShapes); } -std::shared_ptr MHAFQAfterMatMul::get_subgraph() { +std::shared_ptr MHAFQAfterMatMul::get_subgraph() const { return std::make_shared(inputDynamicShapes); } -std::shared_ptr MHAFQ::get_subgraph() { +std::shared_ptr MHAFQ::get_subgraph() const { return std::make_shared(inputDynamicShapes); } -std::shared_ptr MHAMulAdd::get_subgraph() { +std::shared_ptr MHAMulAdd::get_subgraph() const { return std::make_shared(inputDynamicShapes); } -std::shared_ptr MHATransposedB::get_subgraph() { +std::shared_ptr MHATransposedB::get_subgraph() const { return std::make_shared(inputDynamicShapes, true); } -std::shared_ptr MHAWithExtractedReshape::get_subgraph() { +std::shared_ptr MHAWithExtractedReshape::get_subgraph() const { return std::make_shared(inputDynamicShapes, false); } +std::shared_ptr MHAWithDynamicMul::get_subgraph() const { + return std::make_shared(inputDynamicShapes, m_input_types); +} + TEST_P(MHA, CompareWithRefImpl) { SKIP_IF_CURRENT_TEST_IS_DISABLED() run(); @@ -250,6 +285,12 @@ TEST_P(MHAWithExtractedReshape, CompareWithRefImpl) { validateNumSubgraphs(); } +TEST_P(MHAWithDynamicMul, CompareWithRefImpl) { + SKIP_IF_CURRENT_TEST_IS_DISABLED() + run(); + validateNumSubgraphs(); +} + } // namespace snippets } // namespace test } // namespace ov diff --git a/src/tests/ov_helpers/ov_snippets_models/include/subgraph_mha.hpp b/src/tests/ov_helpers/ov_snippets_models/include/subgraph_mha.hpp index 60af12e27e5f48..90ab47214effee 100644 --- a/src/tests/ov_helpers/ov_snippets_models/include/subgraph_mha.hpp +++ b/src/tests/ov_helpers/ov_snippets_models/include/subgraph_mha.hpp @@ -70,6 +70,32 @@ class MHASplitMFunction : public MHAFunction { std::vector reshapes; }; +/* Graph: + * Transpose1[0,2,3,1] Parameter + * \ / + * Transpose0[0,2,1,3] Multiply + * \ / + * MatMul0 + * \ / + * Add + * Softmax Transpose2[0,2,1,3] + * \ / + * MatMul1 + * Transpose3[0,2,1,3] + */ +class MHAWithDynamicMulFunction : public SnippetsFunctionBase { +public: + explicit MHAWithDynamicMulFunction(const std::vector& inputShapes, const std::vector& precisions) + : SnippetsFunctionBase(inputShapes), precisions(precisions) { + OPENVINO_ASSERT(input_shapes.size() == 5, "Got invalid number of input shapes"); + OPENVINO_ASSERT(precisions.size() == 5, "Got invalid number of input precisions"); + } +protected: + std::shared_ptr initOriginal() const override; + + const std::vector precisions; +}; + /* Graph: * Transpose1[0,2,1,3] Constant * \ / diff --git a/src/tests/ov_helpers/ov_snippets_models/src/subgraph_mha.cpp b/src/tests/ov_helpers/ov_snippets_models/src/subgraph_mha.cpp index 3ca78918f5e925..c47cb754b5b891 100644 --- a/src/tests/ov_helpers/ov_snippets_models/src/subgraph_mha.cpp +++ b/src/tests/ov_helpers/ov_snippets_models/src/subgraph_mha.cpp @@ -231,6 +231,37 @@ std::shared_ptr MHASplitMFunction::initReference() const { return std::make_shared(results, ngraphParams, "mha"); } +std::shared_ptr MHAWithDynamicMulFunction::initOriginal() const { + auto transpose0Param = std::make_shared(precisions[0], input_shapes[0]); + auto transpose1Param = std::make_shared(precisions[1], input_shapes[1]); + auto mulParam = std::make_shared(precisions[2], input_shapes[2]); + auto addParam = std::make_shared(precisions[3], input_shapes[3]); + auto transpose2Param = std::make_shared(precisions[4], input_shapes[4]); + ov::ParameterVector ngraphParam = {transpose0Param, transpose1Param, mulParam, addParam, transpose2Param}; + + const auto rank = input_shapes[0].size(); + const auto fusion_order = get_fusion_order(rank); + const auto decomposed_order = get_decomposed_order(rank); + + const auto transpose0Const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{rank}, fusion_order); + const auto transpose1Const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{rank}, decomposed_order); + const auto transpose2Const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{rank}, fusion_order); + const auto transpose3Const = ov::op::v0::Constant::create(ov::element::i64, ov::Shape{rank}, fusion_order); + + const auto transpose0 = std::make_shared(transpose0Param, transpose0Const); + const auto transpose1 = std::make_shared(transpose1Param, transpose1Const); + const auto mul = std::make_shared(transpose1, mulParam); + const auto matMul0 = std::make_shared(transpose0, mul); + const auto add = std::make_shared(matMul0, addParam); + const auto softMax = std::make_shared(add, rank - 1); + const auto transpose2 = std::make_shared(transpose2Param, transpose2Const); + const auto matMul1 = std::make_shared(softMax, transpose2); + const auto transpose3 = std::make_shared(matMul1, transpose3Const); + + ov::ResultVector results{std::make_shared(transpose3)}; + return std::make_shared(results, ngraphParam, "mha"); +} + std::shared_ptr MHAMatMul0TransposeFunction::initOriginal() const { auto transpose0Param = std::make_shared(precisions[0], input_shapes[0]); auto transpose1Param = std::make_shared(precisions[1], input_shapes[1]); From bd7dc80242ee349da4c327ccaf7809932e3cff58 Mon Sep 17 00:00:00 2001 From: Vladimir Paramuzov Date: Thu, 22 Aug 2024 15:52:25 +0400 Subject: [PATCH 19/35] [GPU] Fix move per channel eltwise pass (#26154) ### Details: - Fixes regression after #24401 ### Tickets: - *CVS-150278* - *CVS-149636* - *CVS-149620* --- .../move_eltwise_up_data_movement.cpp | 3 ++- .../move_eltwise_up_data_movement_test.cpp | 20 +++++++++++++++++++ .../src/plugin/transformations_pipeline.cpp | 6 +++++- 3 files changed, 27 insertions(+), 2 deletions(-) diff --git a/src/common/transformations/src/transformations/common_optimizations/move_eltwise_up_data_movement.cpp b/src/common/transformations/src/transformations/common_optimizations/move_eltwise_up_data_movement.cpp index fd9e7417261b81..82efb3699b2c40 100644 --- a/src/common/transformations/src/transformations/common_optimizations/move_eltwise_up_data_movement.cpp +++ b/src/common/transformations/src/transformations/common_optimizations/move_eltwise_up_data_movement.cpp @@ -167,7 +167,8 @@ ov::pass::MoveEltwiseUpThroughDataMovPerChannel::MoveEltwiseUpThroughDataMovPerC }; auto eltw_data_flow_in = - ov::pass::pattern::wrap_type(); + ov::pass::pattern::wrap_type( + pattern::consumers_count(1)); auto eltw_const_in = ov::pass::pattern::wrap_type(const_predicate); auto eltwise_pattern = ov::pass::pattern::wrap_type({eltw_data_flow_in, eltw_const_in}, diff --git a/src/common/transformations/tests/common_optimizations/move_eltwise_up_data_movement_test.cpp b/src/common/transformations/tests/common_optimizations/move_eltwise_up_data_movement_test.cpp index 374cd208fefb7d..07eb3ec2b3e983 100644 --- a/src/common/transformations/tests/common_optimizations/move_eltwise_up_data_movement_test.cpp +++ b/src/common/transformations/tests/common_optimizations/move_eltwise_up_data_movement_test.cpp @@ -446,6 +446,26 @@ TEST_F(MoveEltwiseUpThroughDataMovTest, PerChannelEltwiseSqueezeIllegal_1) { manager.register_pass(); } +TEST_F(MoveEltwiseUpThroughDataMovTest, PerChannelEltwiseSqueezeIllegal_2) { + const ov::Shape shape{10, 20, 1, 1}; + // Data movement op with multiple consumers is not applicable + auto input = std::make_shared(ov::element::f32, shape); + + auto squeeze_const = ov::opset8::Constant::create(ov::element::i64, ov::Shape{}, {2}); // {10, 20, 1} + auto squeeze = std::make_shared(input, squeeze_const); + + auto per_channel_const1 = ov::opset8::Constant::create(ov::element::f32, {10, 1, 1}, {0.5}); + auto add1 = std::make_shared(squeeze, per_channel_const1); + + auto per_channel_const2 = ov::opset8::Constant::create(ov::element::f32, {10, 1, 1}, {0.5}); + auto add2 = std::make_shared(squeeze, per_channel_const2); + + auto add3 = std::make_shared(add1, add2); + + model = std::make_shared(ov::NodeVector{add3}, ov::ParameterVector{input}); + manager.register_pass(); +} + TEST_F(MoveEltwiseUpThroughDataMovTest, PerChannelReshapeMultiply) { const ov::Shape shape{1, 3, 20}; const std::vector target_shape = {1, 3, 4, 5}; diff --git a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp index f5cb6783d4b080..fc62f68af2f6a2 100644 --- a/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp +++ b/src/plugins/intel_gpu/src/plugin/transformations_pipeline.cpp @@ -794,7 +794,7 @@ void TransformationsPipeline::apply(std::shared_ptr func) { ov::op::v1::Broadcast::get_type_info_static(), ov::op::v3::Broadcast::get_type_info_static(), }; - manager.register_pass(allowed_data_movement_ops); + manager.register_pass(allowed_data_movement_ops); manager.register_pass(); manager.register_pass(); @@ -842,6 +842,10 @@ void TransformationsPipeline::apply(std::shared_ptr func) { manager.register_pass(); manager.register_pass(); manager.register_pass(); + + // This pass should be done after asymmetric quantization matching as it can move zp subtraction upper in the graph + manager.register_pass(); + manager.register_pass(); const size_t zp_pad_size = device_info.supports_immad ? 16 : 32; From 3c34cc6520bd386ee572a417fa779bcd416efa3c Mon Sep 17 00:00:00 2001 From: Damian Kurek Date: Thu, 22 Aug 2024 14:46:48 +0200 Subject: [PATCH 20/35] [GPU] Improve maximum value calculation in softmax BF kernel (#26101) ### Details: - Calculate maximum values for each work-item while fetching data, slightly improving performance - Remove one loop - Decrease private memory fetch ### Tickets: - 150093 --- .../cl_kernels/softmax_gpu_bf.cl | 48 +++++++------------ 1 file changed, 18 insertions(+), 30 deletions(-) diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/softmax_gpu_bf.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/softmax_gpu_bf.cl index c391afdc4adbb4..990da1509558bd 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/softmax_gpu_bf.cl +++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/softmax_gpu_bf.cl @@ -87,6 +87,7 @@ KERNEL (softmax_gpu_continuous_bfyx)( INPUT0_TYPE my_chunk[STACK_SIZE]; INPUT0_TYPE my_sum = UNIT_VAL_ZERO; + INPUT0_TYPE my_maximum = -UNIT_VAL_MAX; __local INPUT0_TYPE lg_storage[SLM_SIZE]; @@ -108,6 +109,7 @@ KERNEL (softmax_gpu_continuous_bfyx)( unroll_for (int j = 0; j < OPT_BLOCK_SIZE; j++) { my_chunk[input_idx+j] = vec_tmp[j]; + my_maximum = max(my_maximum, vec_tmp[j]); } } @@ -115,6 +117,7 @@ KERNEL (softmax_gpu_continuous_bfyx)( { BLOCK_TYPE vec_tmp = BLOCK_READ(input, aligned_data_offset + input_idx * get_sub_group_size()); my_chunk[input_idx] = vec_tmp; + my_maximum = max(my_maximum, vec_tmp); } } #else @@ -125,11 +128,13 @@ KERNEL (softmax_gpu_continuous_bfyx)( BLOCK_TYPE vec_tmp = BLOCK_READ(input, aligned_data_offset + input_idx * get_sub_group_size()); #if SUBGROUP_BLOCK_SIZE == 1 my_chunk[input_idx] = vec_tmp; + my_maximum = max(my_maximum, vec_tmp); #else unroll_for (int j = 0; j < SUBGROUP_BLOCK_SIZE; j++) { INPUT0_TYPE tmp = vec_tmp[j]; my_chunk[input_idx+j] = tmp; + my_maximum = max(my_maximum, tmp); } #endif } @@ -138,29 +143,23 @@ KERNEL (softmax_gpu_continuous_bfyx)( for (; input_idx < items_num; input_idx++) { - my_chunk[input_idx] = input[aligned_data_offset + get_sub_group_local_id() + input_idx * get_sub_group_size()]; + INPUT0_TYPE tmp = input[aligned_data_offset + get_sub_group_local_id() + input_idx * get_sub_group_size()]; + my_chunk[input_idx] = tmp; + my_maximum = max(my_maximum, tmp); } if (in_data_set_idx < aligned_offset) { INPUT0_TYPE tmp = input[data_set_offset + in_data_set_idx]; my_chunk[input_idx++] = tmp; + my_maximum = max(my_maximum, tmp); } if (in_data_set_idx < actual_leftovers) { INPUT0_TYPE tmp = input[leftover_idx]; my_chunk[input_idx++] = tmp; - } - - INPUT0_TYPE my_maximum = -UNIT_VAL_MAX; - { - const uint num_iters = input_idx; - - for (uint j=0; j Date: Thu, 22 Aug 2024 15:52:16 +0200 Subject: [PATCH 21/35] Use AvgPool-14 in PT FE (#25471) ### Details: - Extend PT FE with AvgPool-14 - Move the `count_include_pad` workaround subgraph from PT FE to a downgrade transformation - The subgraph in downgrade transformations is defined in https://github.com/openvinotoolkit/openvino/blob/master/src/common/transformations/src/transformations/op_conversions/convert_avgpool_downgrade.cpp#L52 - The new `RoundingType::CEIL_TORCH` has impact only on the output shape - Out of two tests which previously have been xfailed due to output shape mismatch one is still failing, but now due to accuracy validation failure - No old tests were broken by upgrading to V14, so it seems the subgraph has issue with this edge case specifically - The edge case is connected to `count_include_pad:True`, and `pad` value, depending on the latter FP16 test case sometimes passes, but FP32 always fails - Since CPU Plugin is calling oneDNN AvgPool implementation there may be some difference in results calculation or an issue with parameter preprocessing before calling oneDNN function - Continued in XXXXX ### Tickets: - CVS-133929 --------- Co-authored-by: Michal Lukaszewski --- src/frontends/pytorch/src/op/avg_poolnd.cpp | 24 ++----------------- .../layer_tests/pytorch_tests/test_pooling.py | 18 +++++--------- 2 files changed, 8 insertions(+), 34 deletions(-) diff --git a/src/frontends/pytorch/src/op/avg_poolnd.cpp b/src/frontends/pytorch/src/op/avg_poolnd.cpp index 65e1e3ba92dc97..03c32259b45091 100644 --- a/src/frontends/pytorch/src/op/avg_poolnd.cpp +++ b/src/frontends/pytorch/src/op/avg_poolnd.cpp @@ -40,35 +40,15 @@ OutputVector translate_avg_poolnd(const NodeContext& context) { } ov::op::RoundingType rounding_type = ov::op::RoundingType::FLOOR; if (!(context.input_is_none(4))) { - rounding_type = context.const_input(4) ? ov::op::RoundingType::CEIL : ov::op::RoundingType::FLOOR; + rounding_type = context.const_input(4) ? ov::op::RoundingType::CEIL_TORCH : ov::op::RoundingType::FLOOR; } if (!(context.input_is_none(5))) { count_include_pad = context.const_input(5); } PYTORCH_OP_CONVERSION_CHECK(context.input_is_none(6), "Translation for aten::avg_pool2d do not support divisor_override input."); - // Although ov::AvgPool provides exclude_pad=false, - // The corner case of Average Pooling with ceil_mode on - // PyTorch allows sliding window go off bound, which leads to this accommodation. - // More detail on https://github.com/pytorch/pytorch/issues/57178 - if (count_include_pad) { - auto zero = context.mark_node(v0::Constant::create(element::f32, Shape{}, {0})); - zero = context.mark_node(std::make_shared(zero, input)); - auto zero_i32 = context.mark_node(v0::Constant::create(element::i32, Shape{}, {0})); - Output rank; - std::tie(std::ignore, rank) = get_shape_rank(context, input); - auto pad_values = get_input_as_i32(context, 3); - auto pads_len = context.mark_node(v0::Constant::create(element::i32, Shape{}, {pads.size()})); - auto pads_diff = context.mark_node(std::make_shared(rank, pads_len)); - auto pads_remaining = context.mark_node(std::make_shared(zero_i32, pads_diff)); - auto padding = context.mark_node( - std::make_shared(OutputVector{std::move(pads_remaining), std::move(pad_values)}, 0)); - input = context.mark_node(std::make_shared(input, padding, padding, zero, ov::op::PadMode::CONSTANT)); - pads = Shape(pads.size(), 0); - } - return {context.mark_node( - std::make_shared(input, strides, pads, pads, kernel, !count_include_pad, rounding_type))}; + std::make_shared(input, strides, pads, pads, kernel, !count_include_pad, rounding_type))}; }; } // namespace op diff --git a/tests/layer_tests/pytorch_tests/test_pooling.py b/tests/layer_tests/pytorch_tests/test_pooling.py index 0cf2446abd1125..32c8a973cb1c92 100644 --- a/tests/layer_tests/pytorch_tests/test_pooling.py +++ b/tests/layer_tests/pytorch_tests/test_pooling.py @@ -17,10 +17,9 @@ {'kernel_size': [2, 1], 'stride': [2, 1], 'padding': 0}, {'kernel_size': [2, 1], 'stride': None, 'padding': 0}, {'kernel_size': [2, 1], 'stride': [], 'padding': 0}, + {'kernel_size': [8, 8], 'stride': [8, 4], 'padding': 1}, ] -d2_params_corner_case = [{'kernel_size': [8, 8], 'stride': [8, 4], 'padding': 1}] - d1_params = [{'kernel_size': 3, 'stride': 1, 'padding': 0}, {'kernel_size': (4,), 'stride': 1, 'padding': 1}, {'kernel_size': 4, 'stride': (5,), 'padding': 2}, @@ -144,14 +143,7 @@ def test_avg_pool1d(self, params, ceil_mode, count_include_pad, ie_device, preci dynamic_shapes=False) @pytest.mark.parametrize( - "params", - d2_params - + [ - pytest.param( - {"kernel_size": [8, 8], "stride": [8, 4], "padding": 1}, - marks=pytest.mark.xfail(reason="Sliding windows that would start in the right padded are ignored.") - ) - ]) + "params", d2_params) @pytest.mark.parametrize("ceil_mode", [True, False]) @pytest.mark.parametrize("count_include_pad", [True, False]) @pytest.mark.nightly @@ -161,6 +153,8 @@ def test_avg_pool1d(self, params, ceil_mode, count_include_pad, ie_device, preci @pytest.mark.xfail(condition=platform.system() == 'Darwin' and platform.machine() == 'arm64', reason='Ticket - 122715') def test_avg_pool2d(self, params, ceil_mode, count_include_pad, ie_device, precision, ir_version): + if ceil_mode and count_include_pad and np.array_equal(np.array(params["kernel_size"]), np.array([8, 8])): + pytest.xfail("Ticket - 150292") self._test(*self.create_model("avg_pool2d", **params, ceil_mode=ceil_mode, count_include_pad=count_include_pad), ie_device, precision, ir_version, trace_model=True, freeze_model=False, dynamic_shapes=False) @@ -190,7 +184,7 @@ def test_max_pool1d(self, params, ceil_mode, dilation, ie_device, precision, ir_ self._test(*self.create_model("max_pool1d", **params, ceil_mode=ceil_mode, dilation=dilation), ie_device, precision, ir_version, kwargs_to_prepare_input={'ndim': 3}, dynamic_shapes=False) - @pytest.mark.parametrize("params", d2_params + d2_params_corner_case) + @pytest.mark.parametrize("params", d2_params) @pytest.mark.parametrize("ceil_mode", [True, False]) @pytest.mark.parametrize("dilation", [1, 2]) @pytest.mark.parametrize("dtype", [torch.float32, torch.int32]) @@ -227,7 +221,7 @@ def test_max_pool1d_indices(self, params, ceil_mode, dilation, ie_device, precis self._test(*self.create_model("max_pool1d_with_indices", **params, ceil_mode=ceil_mode, dilation=dilation), ie_device, precision, ir_version, kwargs_to_prepare_input={'ndim': 3}, dynamic_shapes=False) - @pytest.mark.parametrize("params", d2_params + d2_params_corner_case) + @pytest.mark.parametrize("params", d2_params) @pytest.mark.parametrize("ceil_mode", [True, False]) @pytest.mark.parametrize("dilation", [1, 2]) @pytest.mark.nightly From f50a8764d5b4804ab1f126e6be11328c53ed7ca0 Mon Sep 17 00:00:00 2001 From: Tatiana Savina Date: Thu, 22 Aug 2024 16:31:40 +0200 Subject: [PATCH 22/35] [DOCS] Bump zipp in docs (#26179) ### Details: recreates PR 25474 ### Tickets: - *ticket-id* --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index 5703503a9ba158..a3873d9e77c27e 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -46,4 +46,4 @@ sphinxcontrib-qthelp==1.0.3 sphinxcontrib-serializinghtml==1.1.9 toml==0.10.2 urllib3==1.26.19 -zipp==3.4.1 \ No newline at end of file +zipp==3.19.1 \ No newline at end of file From 407b012ce353b991a04a094e8de691fc477fd96d Mon Sep 17 00:00:00 2001 From: Vladimir Paramuzov Date: Thu, 22 Aug 2024 18:40:38 +0400 Subject: [PATCH 23/35] [GPU] SYCL infra for GPU plugin and GHA pipeline (#26067) Co-authored-by: Ilya Lavrenov --- .github/dockerfiles/docker_tag | 2 +- .../ubuntu_22_04_x64_dpcpp/Dockerfile | 95 +++++++++++++++++++ .github/workflows/ubuntu_22_dpcpp.yml | 90 ++++++++++++++++++ .../compile_flags/os_flags.cmake | 2 + .../scaled_attn/executor_pa_common.hpp | 1 + src/plugins/intel_gpu/CMakeLists.txt | 4 + .../intel_gpu/graph/kernel_impl_params.hpp | 2 +- .../primitives/implementation_desc.hpp | 1 + .../runtime/engine_configuration.hpp | 2 + .../intel_gpu/src/graph/CMakeLists.txt | 10 ++ .../intel_gpu/src/graph/impls/sycl/README.md | 16 ++++ .../graph/impls/sycl/primitive_sycl_base.h | 90 ++++++++++++++++++ .../src/graph/impls/sycl/register.cpp | 17 ++++ .../src/graph/impls/sycl/register.hpp | 23 +++++ src/plugins/intel_gpu/src/graph/program.cpp | 6 ++ src/plugins/intel_gpu/src/plugin/plugin.cpp | 4 + .../intel_gpu/src/plugin/remote_context.cpp | 5 + .../intel_gpu/src/runtime/CMakeLists.txt | 11 +++ .../intel_gpu/src/runtime/device_query.cpp | 1 + src/plugins/intel_gpu/src/runtime/engine.cpp | 5 + .../intel_gpu/src/runtime/kernels_cache.cpp | 4 +- .../intel_gpu/src/runtime/kernels_factory.cpp | 4 +- src/plugins/intel_gpu/src/runtime/memory.cpp | 4 +- .../src/runtime/ocl/ocl_engine_factory.hpp | 3 + .../intel_gpu/src/runtime/ocl/sycl_engine.cpp | 37 ++++++++ .../intel_gpu/src/runtime/ocl/sycl_engine.hpp | 28 ++++++ .../intel_gpu/src/runtime/ocl/sycl_stream.cpp | 30 ++++++ .../intel_gpu/src/runtime/ocl/sycl_stream.hpp | 26 +++++ src/plugins/intel_gpu/src/runtime/stream.cpp | 4 +- .../intel_gpu/tests/unit/CMakeLists.txt | 5 + .../intel_npu/src/plugin/CMakeLists.txt | 2 +- 31 files changed, 526 insertions(+), 8 deletions(-) create mode 100644 .github/dockerfiles/ov_build/ubuntu_22_04_x64_dpcpp/Dockerfile create mode 100644 .github/workflows/ubuntu_22_dpcpp.yml create mode 100644 src/plugins/intel_gpu/src/graph/impls/sycl/README.md create mode 100644 src/plugins/intel_gpu/src/graph/impls/sycl/primitive_sycl_base.h create mode 100644 src/plugins/intel_gpu/src/graph/impls/sycl/register.cpp create mode 100644 src/plugins/intel_gpu/src/graph/impls/sycl/register.hpp create mode 100644 src/plugins/intel_gpu/src/runtime/ocl/sycl_engine.cpp create mode 100644 src/plugins/intel_gpu/src/runtime/ocl/sycl_engine.hpp create mode 100644 src/plugins/intel_gpu/src/runtime/ocl/sycl_stream.cpp create mode 100644 src/plugins/intel_gpu/src/runtime/ocl/sycl_stream.hpp diff --git a/.github/dockerfiles/docker_tag b/.github/dockerfiles/docker_tag index 38479d3f7437dc..5bd51d5f04378a 100644 --- a/.github/dockerfiles/docker_tag +++ b/.github/dockerfiles/docker_tag @@ -1 +1 @@ -pr-25992 \ No newline at end of file +pr-26067 diff --git a/.github/dockerfiles/ov_build/ubuntu_22_04_x64_dpcpp/Dockerfile b/.github/dockerfiles/ov_build/ubuntu_22_04_x64_dpcpp/Dockerfile new file mode 100644 index 00000000000000..292ae46cab94a3 --- /dev/null +++ b/.github/dockerfiles/ov_build/ubuntu_22_04_x64_dpcpp/Dockerfile @@ -0,0 +1,95 @@ +FROM openvinogithubactions.azurecr.io/dockerhub/ubuntu:22.04 + +USER root + +# APT configuration +RUN echo 'Acquire::Retries "10";' > /etc/apt/apt.conf && \ + echo 'APT::Get::Assume-Yes "true";' >> /etc/apt/apt.conf && \ + echo 'APT::Get::Fix-Broken "true";' >> /etc/apt/apt.conf && \ + echo 'APT::Get::no-install-recommends "true";' >> /etc/apt/apt.conf + +ENV DEBIAN_FRONTEND="noninteractive" \ + TZ="Europe/London" + +RUN apt-get update && \ + apt-get install software-properties-common && \ + add-apt-repository --yes --no-update ppa:git-core/ppa && \ + add-apt-repository --yes --no-update ppa:deadsnakes/ppa && \ + apt-get update && \ + apt-get install \ + wget \ + curl \ + git \ + ca-certificates \ + gpg-agent \ + tzdata \ + libtbb2 \ + # Pythons \ + python3.11-dev \ + python3.11-venv \ + python3.11-distutils \ + default-jdk \ + && \ + rm -rf /var/lib/apt/lists/* + + +# Install OneAPI Toolkit +RUN wget -O- https://apt.repos.intel.com/intel-gpg-keys/GPG-PUB-KEY-INTEL-SW-PRODUCTS.PUB | gpg --dearmor | tee /usr/share/keyrings/intel-oneapi-archive-keyring.gpg > /dev/null +RUN echo "deb [signed-by=/usr/share/keyrings/intel-oneapi-archive-keyring.gpg] https://apt.repos.intel.com/oneapi all main " > /etc/apt/sources.list.d/oneAPI.list + +RUN apt-get update && \ + apt-get install \ + intel-oneapi-compiler-dpcpp-cpp=2024.2.1-1079 \ + && \ + rm -rf /var/lib/apt/lists/* + +# Install build dependencies +ADD install_build_dependencies.sh /install_build_dependencies.sh +RUN chmod +x /install_build_dependencies.sh && \ + /install_build_dependencies.sh && \ + rm -rf /var/lib/apt/lists/* + +# Install sscache +ARG SCCACHE_VERSION="v0.7.5" +ENV SCCACHE_HOME="/opt/sccache" \ + SCCACHE_PATH="/opt/sccache/sccache" + +RUN mkdir ${SCCACHE_HOME} && cd ${SCCACHE_HOME} && \ + SCCACHE_ARCHIVE="sccache-${SCCACHE_VERSION}-x86_64-unknown-linux-musl.tar.gz" && \ + curl -SLO https://github.com/mozilla/sccache/releases/download/${SCCACHE_VERSION}/${SCCACHE_ARCHIVE} && \ + tar -xzf ${SCCACHE_ARCHIVE} --strip-components=1 && rm ${SCCACHE_ARCHIVE} + +# Setup pip +ENV PIP_VERSION="24.0" +RUN curl https://bootstrap.pypa.io/get-pip.py -o get-pip.py && \ + python3 get-pip.py --no-cache-dir pip==${PIP_VERSION} && \ + python3.11 get-pip.py --no-cache-dir pip==${PIP_VERSION} && \ + rm -f get-pip.py + +# Use Python 3.11 as default +# Using venv here 'cause other methods to switch the default Python on Ubuntu break both system and wheels build +RUN python3.11 -m venv venv +ENV PATH="/venv/bin:$SCCACHE_HOME:$PATH" + +ENV PIP_CACHE_DIR=/mount/caches/pip/linux/${PIP_VERSION} + +# OneAPI env +ENV ONEAPI_ROOT=/opt/intel/oneapi +ENV PKG_CONFIG_PATH=/opt/intel/oneapi/compiler/2024.2/lib/pkgconfig +ENV DIAGUTIL_PATH=/opt/intel/oneapi/dpcpp-ct/2024.2/etc/dpct/sys_check/sys_check.sh:/opt/intel/oneapi/debugger/2024.2/etc/debugger/sys_check/sys_check.py:/opt/intel/oneapi/compiler/2024.2/etc/compiler/sys_check/sys_check.sh +ENV MANPATH=/opt/intel/oneapi/debugger/2024.2/share/man:/opt/intel/oneapi/compiler/2024.2/share/man: +ENV GDB_INFO=/opt/intel/oneapi/debugger/2024.2/share/info/ +ENV CMAKE_PREFIX_PATH=/opt/intel/oneapi/compiler/2024.2 +ENV CMPLR_ROOT=/opt/intel/oneapi/compiler/2024.2 +ENV INFOPATH=/opt/intel/oneapi/debugger/2024.2/share/info +ENV LIBRARY_PATH=/opt/intel/oneapi/compiler/2024.2/lib +ENV OCL_ICD_FILENAMES=/opt/intel/oneapi/compiler/2024.2/lib/libintelocl.so +ENV LD_LIBRARY_PATH=/opt/intel/oneapi/debugger/2024.2/opt/debugger/lib:/opt/intel/oneapi/compiler/2024.2/opt/compiler/lib:/opt/intel/oneapi/compiler/2024.2/lib +ENV NLSPATH=/opt/intel/oneapi/mkl/2024.2/share/locale/%l_%t/%N:/opt/intel/oneapi/compiler/2024.2/lib/compiler/locale/%l_%t/%N +ENV PATH=$PATH:/opt/intel/oneapi/dev-utilities/2024.2/bin:/opt/intel/oneapi/debugger/2024.2/opt/debugger/bin:/opt/intel/oneapi/compiler/2024.2/bin +ENV INTEL_PYTHONHOME=/opt/intel/oneapi/debugger/2024.2/opt/debugger +ENV CPATH=/opt/intel/oneapi/dpl/2022.6/include:/opt/intel/oneapi/dev-utilities/2024.2/include: + +# Set Intel DPC++ as a default compiler +ENV CC=icx +ENV CXX=icpx diff --git a/.github/workflows/ubuntu_22_dpcpp.yml b/.github/workflows/ubuntu_22_dpcpp.yml new file mode 100644 index 00000000000000..4af7b08a334996 --- /dev/null +++ b/.github/workflows/ubuntu_22_dpcpp.yml @@ -0,0 +1,90 @@ +name: Linux (Ubuntu 22.04, Python 3.11, Intel DPC++ Compiler) +on: + workflow_dispatch: + pull_request: + merge_group: + +concurrency: + # github.ref is not unique in post-commit + group: ${{ github.event_name == 'push' && github.run_id || github.ref }}-ubuntu-22-dpcpp + cancel-in-progress: true + +permissions: read-all + +jobs: + Smart_CI: + runs-on: ubuntu-latest + outputs: + affected_components: "${{ steps.smart_ci.outputs.affected_components }}" + changed_components: "${{ steps.smart_ci.outputs.changed_components }}" + skip_workflow: "${{ steps.smart_ci.outputs.skip_workflow }}" + steps: + - name: checkout action + uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 + with: + sparse-checkout: .github/actions/smart-ci + + - name: Get affected components + id: smart_ci + uses: ./.github/actions/smart-ci + with: + repository: ${{ github.repository }} + pr: ${{ github.event.number }} + commit_sha: ${{ github.sha }} + ref_name: ${{ github.ref_name }} + component_pattern: "category: (.*)" + repo_token: ${{ secrets.GITHUB_TOKEN }} + skip_when_only_listed_labels_set: 'docs' + skip_when_only_listed_files_changed: '*.md,*.rst,*.png,*.jpg,*.svg' + + - name: Show affected components + run: | + echo "${{ toJSON(steps.smart_ci.outputs.affected_components) }}" + shell: bash + + Docker: + needs: Smart_CI + runs-on: aks-linux-4-cores-16gb-docker-build + container: + image: openvinogithubactions.azurecr.io/docker_build:0.2 + volumes: + - /mount:/mount + outputs: + images: "${{ steps.handle_docker.outputs.images }}" + steps: + - name: Checkout + uses: actions/checkout@692973e3d937129bcbf40652eb9f2f61becf3332 # v4.1.7 + + - uses: ./.github/actions/handle_docker + id: handle_docker + with: + images: | + ov_build/ubuntu_22_04_x64_dpcpp + registry: 'openvinogithubactions.azurecr.io' + dockerfiles_root_dir: '.github/dockerfiles' + changed_components: ${{ needs.smart_ci.outputs.changed_components }} + + Build: + needs: [Docker, Smart_CI] + if: "!needs.smart_ci.outputs.skip_workflow" + uses: ./.github/workflows/job_build_linux.yml + with: + runner: 'aks-linux-16-cores-32gb' + container: '{"image": "${{ fromJSON(needs.docker.outputs.images).ov_build.ubuntu_22_04_x64_dpcpp }}", "volumes": ["/mount:/mount"], "options": "-e SCCACHE_AZURE_BLOB_CONTAINER -e SCCACHE_AZURE_CONNECTION_STRING"}' + affected-components: ${{ needs.smart_ci.outputs.affected_components }} + event-name: ${{ github.event_name }} + os: 'ubuntu_22_04_dpcpp' + + Overall_Status: + name: ci/gha_overall_status_ubuntu_22.04_dpcpp + needs: [Smart_CI, Build] + if: ${{ always() }} + runs-on: ubuntu-latest + steps: + - name: Check status of all jobs + if: >- + ${{ + contains(needs.*.result, 'failure') || + contains(needs.*.result, 'cancelled') + }} + run: exit 1 diff --git a/cmake/developer_package/compile_flags/os_flags.cmake b/cmake/developer_package/compile_flags/os_flags.cmake index a49dce9bce7b50..bb80787ee3d811 100644 --- a/cmake/developer_package/compile_flags/os_flags.cmake +++ b/cmake/developer_package/compile_flags/os_flags.cmake @@ -289,6 +289,8 @@ endif() if(NOT DEFINED CMAKE_CXX_STANDARD) if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") set(CMAKE_CXX_STANDARD 14) + elseif(OV_COMPILER_IS_INTEL_LLVM) + set(CMAKE_CXX_STANDARD 17) else() set(CMAKE_CXX_STANDARD 11) endif() diff --git a/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/executor_pa_common.hpp b/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/executor_pa_common.hpp index 46e06466d5f8ba..237860ec692e76 100644 --- a/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/executor_pa_common.hpp +++ b/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/executor_pa_common.hpp @@ -34,6 +34,7 @@ struct PagedAttentionExecutor { static const size_t ID_ALIBI_SLOPES = 11; // [H|0], float static const size_t ID_MAX_CONTEXT_LEN = 12; // [] virtual void execute(const std::vector& inputs, const std::vector outputs) = 0; + virtual ~PagedAttentionExecutor() = default; }; #ifdef OPENVINO_ARCH_X86_64 diff --git a/src/plugins/intel_gpu/CMakeLists.txt b/src/plugins/intel_gpu/CMakeLists.txt index 53b4a203301c77..795d259b277e2b 100644 --- a/src/plugins/intel_gpu/CMakeLists.txt +++ b/src/plugins/intel_gpu/CMakeLists.txt @@ -8,6 +8,10 @@ endif() set (TARGET_NAME "openvino_intel_gpu_plugin") +if(OV_COMPILER_IS_INTEL_LLVM) + find_package(IntelSYCL REQUIRED) +endif() + if((CMAKE_COMPILER_IS_GNUCXX OR OV_COMPILER_IS_CLANG) AND CMAKE_CXX_STANDARD GREATER_EQUAL 20) set(CMAKE_CXX_FLAGS "-Wno-error=deprecated ${CMAKE_CXX_FLAGS}") endif() diff --git a/src/plugins/intel_gpu/include/intel_gpu/graph/kernel_impl_params.hpp b/src/plugins/intel_gpu/include/intel_gpu/graph/kernel_impl_params.hpp index fa8a8807bbd92c..3e8887fbb2f7ee 100644 --- a/src/plugins/intel_gpu/include/intel_gpu/graph/kernel_impl_params.hpp +++ b/src/plugins/intel_gpu/include/intel_gpu/graph/kernel_impl_params.hpp @@ -90,7 +90,7 @@ struct kernel_impl_params final { , primary_input_idx(0) { } - virtual ~kernel_impl_params() = default; + ~kernel_impl_params() = default; const layout& get_input_layout(size_t idx = 0) const { OPENVINO_ASSERT(input_layouts.size() > idx, diff --git a/src/plugins/intel_gpu/include/intel_gpu/primitives/implementation_desc.hpp b/src/plugins/intel_gpu/include/intel_gpu/primitives/implementation_desc.hpp index 4e5c53d6b37e3e..e84311a9cfb592 100644 --- a/src/plugins/intel_gpu/include/intel_gpu/primitives/implementation_desc.hpp +++ b/src/plugins/intel_gpu/include/intel_gpu/primitives/implementation_desc.hpp @@ -18,6 +18,7 @@ enum class impl_types : uint8_t { common = 1 << 1, ocl = 1 << 2, onednn = 1 << 3, + sycl = 1 << 4, any = 0xFF, }; diff --git a/src/plugins/intel_gpu/include/intel_gpu/runtime/engine_configuration.hpp b/src/plugins/intel_gpu/include/intel_gpu/runtime/engine_configuration.hpp index 042fa7cfd3dc67..e81dd9dec9d2f5 100644 --- a/src/plugins/intel_gpu/include/intel_gpu/runtime/engine_configuration.hpp +++ b/src/plugins/intel_gpu/include/intel_gpu/runtime/engine_configuration.hpp @@ -11,11 +11,13 @@ namespace cldnn { /// @brief Defines available engine types enum class engine_types : int32_t { ocl, + sycl }; inline std::ostream& operator<<(std::ostream& os, const engine_types& type) { switch (type) { case engine_types::ocl: os << "ocl"; break; + case engine_types::sycl: os << "sycl"; break; default: os << "unknown"; break; } diff --git a/src/plugins/intel_gpu/src/graph/CMakeLists.txt b/src/plugins/intel_gpu/src/graph/CMakeLists.txt index db25a90e42b446..d82984205c0307 100644 --- a/src/plugins/intel_gpu/src/graph/CMakeLists.txt +++ b/src/plugins/intel_gpu/src/graph/CMakeLists.txt @@ -4,6 +4,7 @@ set(TARGET_NAME "openvino_intel_gpu_graph") +file(GLOB_RECURSE SYCL_SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/impls/sycl/*") file(GLOB_RECURSE LIBRARY_SRC "${INCLUDE_DIR}/*.h" "${INCLUDE_DIR}/*.hpp" @@ -22,6 +23,10 @@ if (NOT ENABLE_ONEDNN_FOR_GPU) endforeach(SOURCE_FILE) endif() +if(NOT OV_COMPILER_IS_INTEL_LLVM) + list(REMOVE_ITEM LIBRARY_SRC ${SYCL_SOURCES}) +endif() + add_library(${TARGET_NAME} STATIC ${LIBRARY_SRC}) target_include_directories(${TARGET_NAME} PUBLIC @@ -33,6 +38,11 @@ target_include_directories(${TARGET_NAME} PUBLIC target_compile_options(${TARGET_NAME} PRIVATE $<$:$,/Os,-Os>>) +if(OV_COMPILER_IS_INTEL_LLVM) + add_sycl_to_target(TARGET ${TARGET_NAME} SOURCES ${SYCL_SOURCES}) + target_compile_definitions(${TARGET_NAME} PUBLIC OV_GPU_WITH_SYCL) +endif() + target_link_libraries(${TARGET_NAME} PUBLIC OpenCL::OpenCL openvino::shape_inference) target_link_libraries(${TARGET_NAME} PRIVATE openvino_intel_gpu_kernels openvino_intel_gpu_runtime diff --git a/src/plugins/intel_gpu/src/graph/impls/sycl/README.md b/src/plugins/intel_gpu/src/graph/impls/sycl/README.md new file mode 100644 index 00000000000000..851ef8f5d18519 --- /dev/null +++ b/src/plugins/intel_gpu/src/graph/impls/sycl/README.md @@ -0,0 +1,16 @@ +# How to build with DPC++ support + +1. Install OneAPI base toolkit. Guide: https://www.intel.com/content/www/us/en/docs/oneapi/installation-guide-linux/2024-0/installation.html +2. Export environment: + $ source /opt/intel/oneapi/setvars.sh + +3. configure cmake with the following additional options: + - `-DCMAKE_CXX_FLAGS:STRING=--gcc-install-dir=/lib/gcc/x86_64-linux-gnu/12/ -DCMAKE_C_FLAGS:STRING=--gcc-install-dir=/lib/gcc/x86_64-linux-gnu/12/` + - This WA is needed if multiple GCC version available in the system + - `-DCMAKE_CXX_STANDARD:STRING=17` + - Sycl requires c++17 + - `-DENABLE_SYSTEM_OPENCL=OFF` + - May help to avoid opencl icd/header conflicts as sycl package may have no clhpp headers + - `-DCMAKE_C_COMPILER:FILEPATH=icx -DCMAKE_CXX_COMPILER:FILEPATH=icpx` + - For now find_package(IntelSYCL) doesn't work if compiler is not icpx +4. make -j$(nproc) diff --git a/src/plugins/intel_gpu/src/graph/impls/sycl/primitive_sycl_base.h b/src/plugins/intel_gpu/src/graph/impls/sycl/primitive_sycl_base.h new file mode 100644 index 00000000000000..a6b0e3512e80b3 --- /dev/null +++ b/src/plugins/intel_gpu/src/graph/impls/sycl/primitive_sycl_base.h @@ -0,0 +1,90 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "primitive_inst.h" +#include "intel_gpu/runtime/memory.hpp" +#include "register.hpp" +#include "utils.hpp" +#include "runtime/ocl/ocl_event.hpp" + +#include + +#include "sycl/sycl.hpp" + +namespace cldnn { +namespace sycl { + +static std::mutex cacheAccessMutex; + +template +struct typed_primitive_sycl_impl : public typed_primitive_impl { + const engine* _engine; + + typed_primitive_sycl_impl(const engine& engine, const ExecutionConfig& config, std::shared_ptr weights_reorder = nullptr) + : typed_primitive_impl(weights_reorder, "sycl_kernel"), + _engine(&engine) { } + + typed_primitive_sycl_impl() + : typed_primitive_impl({}, "undef"), + _engine(nullptr) { + } + + bool is_cpu() const override { return false; } + bool is_onednn() const override { return false; } + + void save(BinaryOutputBuffer& ob) const override { + } + + void load(BinaryInputBuffer& ib) override { + } + +protected: + void init_kernels(const kernels_cache&, const kernel_impl_params&) override { } + + void set_arguments_impl(typed_primitive_inst& instance) override { + if (instance.can_be_optimized()) + return; + } + + void update_dispatch_data(const kernel_impl_params& impl_params) override {} + + void set_arguments_impl(typed_primitive_inst& instance, kernel_arguments_data& args) override { + if (instance.can_be_optimized()) { + return; + } + } + + event::ptr execute_impl(const std::vector& /* events */, + typed_primitive_inst& instance) override { + auto& network = instance.get_network(); + auto& stream = network.get_stream(); + auto net_id = network.get_id(); + event::ptr event; + + + return event; + } + + static event::ptr to_ocl_event(stream& stream, ::sycl::event e) { + if (stream.get_queue_type() == QueueTypes::out_of_order) { + auto native_events = get_native<::sycl::backend::opencl, ::sycl::event>(e); + std::vector events; + for (auto& e : native_events) { + events.push_back(std::make_shared(cl::Event(e, true))); + } + return events.empty() ? stream.create_user_event(true) : stream.group_events(events); + } else { + return stream.create_user_event(true); + } + } + + std::vector get_internal_buffer_layouts_impl() const override { + return {}; + } +}; + +} // namespace sycl +} // namespace cldnn diff --git a/src/plugins/intel_gpu/src/graph/impls/sycl/register.cpp b/src/plugins/intel_gpu/src/graph/impls/sycl/register.cpp new file mode 100644 index 00000000000000..9d2ae6808fbfc6 --- /dev/null +++ b/src/plugins/intel_gpu/src/graph/impls/sycl/register.cpp @@ -0,0 +1,17 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "register.hpp" + +namespace cldnn { +namespace sycl { + +#define REGISTER_SYCL_IMPL(prim) \ + static detail::attach_##prim##_sycl attach_##prim + +void register_implementations() { +} + +} // namespace sycl +} // namespace cldnn diff --git a/src/plugins/intel_gpu/src/graph/impls/sycl/register.hpp b/src/plugins/intel_gpu/src/graph/impls/sycl/register.hpp new file mode 100644 index 00000000000000..38fa9df02c5d88 --- /dev/null +++ b/src/plugins/intel_gpu/src/graph/impls/sycl/register.hpp @@ -0,0 +1,23 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + + +namespace cldnn { +namespace sycl { +void register_implementations(); + +namespace detail { + +#define REGISTER_SYCL_IMPL(prim) \ + struct attach_##prim##_sycl { \ + attach_##prim##_sycl(); \ + } + +#undef REGISTER_SYCL_IMPL + +} // namespace detail +} // namespace sycl +} // namespace cldnn diff --git a/src/plugins/intel_gpu/src/graph/program.cpp b/src/plugins/intel_gpu/src/graph/program.cpp index 820bb87f4a9d6f..40d858c38fe85a 100644 --- a/src/plugins/intel_gpu/src/graph/program.cpp +++ b/src/plugins/intel_gpu/src/graph/program.cpp @@ -75,6 +75,9 @@ #ifdef ENABLE_ONEDNN_FOR_GPU #include "impls/onednn/register.hpp" #endif +#ifdef OV_GPU_WITH_SYCL +#include "impls/sycl/register.hpp" +#endif #include "kernel_base.h" @@ -257,6 +260,9 @@ void program::init_primitives() { onednn::register_implementations(); #endif cpu::register_implementations(); +#ifdef OV_GPU_WITH_SYCL + sycl::register_implementations(); +#endif is_initialized = true; } } diff --git a/src/plugins/intel_gpu/src/plugin/plugin.cpp b/src/plugins/intel_gpu/src/plugin/plugin.cpp index e3f7c2cfe2f6b5..69bc3a912fda43 100644 --- a/src/plugins/intel_gpu/src/plugin/plugin.cpp +++ b/src/plugins/intel_gpu/src/plugin/plugin.cpp @@ -145,7 +145,11 @@ Plugin::Plugin() { register_primitives(); // Set OCL runtime which should be always available +#ifdef OV_GPU_WITH_SYCL + cldnn::device_query device_query(cldnn::engine_types::sycl, cldnn::runtime_types::ocl); +#else cldnn::device_query device_query(cldnn::engine_types::ocl, cldnn::runtime_types::ocl); +#endif m_device_map = device_query.get_available_devices(); // Set default configs for each device diff --git a/src/plugins/intel_gpu/src/plugin/remote_context.cpp b/src/plugins/intel_gpu/src/plugin/remote_context.cpp index b36fe7f38a776e..6d24eaea401689 100644 --- a/src/plugins/intel_gpu/src/plugin/remote_context.cpp +++ b/src/plugins/intel_gpu/src/plugin/remote_context.cpp @@ -28,7 +28,12 @@ Type extract_object(const ov::AnyMap& params, const ov::Property& p) { RemoteContextImpl::RemoteContextImpl(const std::string& device_name, std::vector devices) : m_device_name(device_name) { OPENVINO_ASSERT(devices.size() == 1, "[GPU] Currently context can be created for single device only"); +#ifdef OV_GPU_WITH_SYCL + const auto engine_type = cldnn::engine_types::sycl; +#else const auto engine_type = cldnn::engine_types::ocl; +#endif + const auto runtime_type = cldnn::runtime_types::ocl; m_engine = cldnn::engine::create(engine_type, runtime_type, devices.front()); diff --git a/src/plugins/intel_gpu/src/runtime/CMakeLists.txt b/src/plugins/intel_gpu/src/runtime/CMakeLists.txt index 1439620b71e386..23fce3a3354756 100644 --- a/src/plugins/intel_gpu/src/runtime/CMakeLists.txt +++ b/src/plugins/intel_gpu/src/runtime/CMakeLists.txt @@ -21,6 +21,12 @@ set(LIBRARY_SOURCES_ALL ${LIBRARY_SOURCES_OCL} ) +file(GLOB_RECURSE SYCL_SOURCES "${CMAKE_CURRENT_SOURCE_DIR}/ocl/sycl_*.cpp") + +if(NOT OV_COMPILER_IS_INTEL_LLVM) + list(REMOVE_ITEM LIBRARY_SOURCES_ALL ${SYCL_SOURCES}) +endif() + add_library(${TARGET_NAME} STATIC ${LIBRARY_SOURCES_ALL}) if(NOT BUILD_SHARED_LIBS) @@ -44,6 +50,11 @@ target_compile_options(${TARGET_NAME} PRIVATE add_cpplint_target(${TARGET_NAME}_cpplint FOR_TARGETS ${TARGET_NAME}) +if(OV_COMPILER_IS_INTEL_LLVM) + add_sycl_to_target(TARGET ${TARGET_NAME} SOURCES ${SYCL_SOURCES}) + target_compile_definitions(${TARGET_NAME} PUBLIC OV_GPU_WITH_SYYCL) +endif() + ov_set_threading_interface_for(${TARGET_NAME}) target_link_libraries(${TARGET_NAME} PRIVATE diff --git a/src/plugins/intel_gpu/src/runtime/device_query.cpp b/src/plugins/intel_gpu/src/runtime/device_query.cpp index 7cb419deee4f19..4306e7aada874b 100644 --- a/src/plugins/intel_gpu/src/runtime/device_query.cpp +++ b/src/plugins/intel_gpu/src/runtime/device_query.cpp @@ -17,6 +17,7 @@ device_query::device_query(engine_types engine_type, int ctx_device_id, int target_tile_id) { switch (engine_type) { + case engine_types::sycl: case engine_types::ocl: { if (runtime_type != runtime_types::ocl) throw std::runtime_error("Unsupported runtime type for ocl engine"); diff --git a/src/plugins/intel_gpu/src/runtime/engine.cpp b/src/plugins/intel_gpu/src/runtime/engine.cpp index e8879905ffe40a..7d1d016b77b5ff 100644 --- a/src/plugins/intel_gpu/src/runtime/engine.cpp +++ b/src/plugins/intel_gpu/src/runtime/engine.cpp @@ -252,6 +252,11 @@ void engine::subtract_memory_used(uint64_t bytes, allocation_type type) { std::shared_ptr engine::create(engine_types engine_type, runtime_types runtime_type, const device::ptr device) { std::shared_ptr ret; switch (engine_type) { +#ifdef OV_GPU_WITH_SYCL + case engine_types::sycl: + ret = ocl::create_sycl_engine(device, runtime_type); + break; +#endif // OV_GPU_WITH_SYCL case engine_types::ocl: ret = ocl::create_ocl_engine(device, runtime_type); break; diff --git a/src/plugins/intel_gpu/src/runtime/kernels_cache.cpp b/src/plugins/intel_gpu/src/runtime/kernels_cache.cpp index 3f19b348cca600..d6dcaf5dd72d14 100644 --- a/src/plugins/intel_gpu/src/runtime/kernels_cache.cpp +++ b/src/plugins/intel_gpu/src/runtime/kernels_cache.cpp @@ -620,7 +620,7 @@ void kernels_cache::add_to_cached_kernels(const std::vector& kernel } void kernels_cache::save(BinaryOutputBuffer& ob) const { - OPENVINO_ASSERT(_engine.type() == engine_types::ocl, "[GPU] Not supported engine type"); + OPENVINO_ASSERT(_engine.type() == engine_types::ocl || _engine.type() == engine_types::sycl, "[GPU] Not supported engine type"); ob << _cached_binaries.size(); for (auto& cached_binary : _cached_binaries) { @@ -630,7 +630,7 @@ void kernels_cache::save(BinaryOutputBuffer& ob) const { } void kernels_cache::load(BinaryInputBuffer& ib) { - OPENVINO_ASSERT(_engine.type() == engine_types::ocl, "[GPU] Not supported engine type"); + OPENVINO_ASSERT(_engine.type() == engine_types::ocl || _engine.type() == engine_types::sycl, "[GPU] Not supported engine type"); std::unordered_map> precompiled_kernels; diff --git a/src/plugins/intel_gpu/src/runtime/kernels_factory.cpp b/src/plugins/intel_gpu/src/runtime/kernels_factory.cpp index 7dd7be87365204..448afa863609f3 100644 --- a/src/plugins/intel_gpu/src/runtime/kernels_factory.cpp +++ b/src/plugins/intel_gpu/src/runtime/kernels_factory.cpp @@ -10,7 +10,9 @@ namespace kernels_factory { std::shared_ptr create(engine& engine, cl_context context, cl_kernel kernel, std::string entry_point) { switch (engine.type()) { - case engine_types::ocl: return ocl::create_ocl_kernel(engine, context, kernel, entry_point); + case engine_types::sycl: + case engine_types::ocl: + return ocl::create_ocl_kernel(engine, context, kernel, entry_point); default: throw std::runtime_error("Unsupported engine type in kernels_factory::create"); } } diff --git a/src/plugins/intel_gpu/src/runtime/memory.cpp b/src/plugins/intel_gpu/src/runtime/memory.cpp index 4e7f9b37c393f7..5e79af34c6ce14 100644 --- a/src/plugins/intel_gpu/src/runtime/memory.cpp +++ b/src/plugins/intel_gpu/src/runtime/memory.cpp @@ -47,7 +47,9 @@ memory::memory(engine* engine, const layout& layout, allocation_type type, std:: std::unique_ptr surfaces_lock::create(engine_types engine_type, std::vector mem, const stream& stream) { switch (engine_type) { - case engine_types::ocl: return std::unique_ptr(new ocl::ocl_surfaces_lock(mem, stream)); + case engine_types::sycl: + case engine_types::ocl: + return std::unique_ptr(new ocl::ocl_surfaces_lock(mem, stream)); default: throw std::runtime_error("Unsupported engine type in surfaces_lock::create"); } } diff --git a/src/plugins/intel_gpu/src/runtime/ocl/ocl_engine_factory.hpp b/src/plugins/intel_gpu/src/runtime/ocl/ocl_engine_factory.hpp index fad0a483e0c988..2ddfe28ceeb214 100644 --- a/src/plugins/intel_gpu/src/runtime/ocl/ocl_engine_factory.hpp +++ b/src/plugins/intel_gpu/src/runtime/ocl/ocl_engine_factory.hpp @@ -14,6 +14,9 @@ namespace ocl { // Factory for ocl_engine creation. It's moved outside of ocl_engine class to avoid possible CL includes conflict // between different engines in engine.cpp file std::shared_ptr create_ocl_engine(const device::ptr device, runtime_types runtime_type); +#ifdef OV_GPU_WITH_SYCL +std::shared_ptr create_sycl_engine(const device::ptr device, runtime_types runtime_type); +#endif // OV_GPU_WITH_SYCL } // namespace ocl } // namespace cldnn diff --git a/src/plugins/intel_gpu/src/runtime/ocl/sycl_engine.cpp b/src/plugins/intel_gpu/src/runtime/ocl/sycl_engine.cpp new file mode 100644 index 00000000000000..b5da32efbfa8c0 --- /dev/null +++ b/src/plugins/intel_gpu/src/runtime/ocl/sycl_engine.cpp @@ -0,0 +1,37 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "sycl_engine.hpp" +#include "sycl_stream.hpp" + + +namespace cldnn { +namespace ocl { + + +sycl_engine::sycl_engine(const device::ptr dev, runtime_types runtime_type) + : ocl_engine(dev, runtime_type) { + sycl_context = cldnn::make_unique<::sycl::context>(sycl::make_context<::sycl::backend::opencl>(get_cl_context().get())); +} + +stream::ptr sycl_engine::create_stream(const ExecutionConfig& config) const { + return std::make_shared(*this, config); +} + +stream::ptr sycl_engine::create_stream(const ExecutionConfig& config, void* handle) const { + return std::make_shared(*this, config, handle); +} + +std::shared_ptr create_sycl_engine(const device::ptr device, runtime_types runtime_type) { + return std::make_shared(device, runtime_type); +} + +::sycl::context& sycl_engine::get_sycl_context() const { + OPENVINO_ASSERT(sycl_context != nullptr); + + return *sycl_context; +} + +} // namespace ocl +} // namespace cldnn diff --git a/src/plugins/intel_gpu/src/runtime/ocl/sycl_engine.hpp b/src/plugins/intel_gpu/src/runtime/ocl/sycl_engine.hpp new file mode 100644 index 00000000000000..253f6af6795db9 --- /dev/null +++ b/src/plugins/intel_gpu/src/runtime/ocl/sycl_engine.hpp @@ -0,0 +1,28 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include +#include "ocl_engine.hpp" + +#include "sycl/sycl.hpp" + + +namespace cldnn { +namespace ocl { + +class sycl_engine : public ocl_engine { +public: + sycl_engine(const device::ptr dev, runtime_types runtime_type); + + stream_ptr create_stream(const ExecutionConfig& config) const override; + stream_ptr create_stream(const ExecutionConfig& config, void *handle) const override; + + ::sycl::context& get_sycl_context() const; + std::unique_ptr<::sycl::context> sycl_context = nullptr; +}; + +} // namespace ocl +} // namespace cldnn diff --git a/src/plugins/intel_gpu/src/runtime/ocl/sycl_stream.cpp b/src/plugins/intel_gpu/src/runtime/ocl/sycl_stream.cpp new file mode 100644 index 00000000000000..e9be7a060ba2e4 --- /dev/null +++ b/src/plugins/intel_gpu/src/runtime/ocl/sycl_stream.cpp @@ -0,0 +1,30 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "sycl_stream.hpp" +#include "intel_gpu/runtime/utils.hpp" +#include "sycl_engine.hpp" + +namespace cldnn { +namespace ocl { + + +sycl_stream::sycl_stream(const sycl_engine& engine, const ExecutionConfig& config) + : ocl_stream(engine, config) { + sycl_queue = cldnn::make_unique<::sycl::queue>(::sycl::make_queue<::sycl::backend::opencl>(get_cl_queue().get(), engine.get_sycl_context())); +} + + +sycl_stream::sycl_stream(const sycl_engine &engine, const ExecutionConfig& config, void *handle) + : ocl_stream(engine, config, handle) { + sycl_queue = cldnn::make_unique<::sycl::queue>(::sycl::make_queue<::sycl::backend::opencl>(get_cl_queue().get(), engine.get_sycl_context())); +} + +::sycl::queue& sycl_stream::get_sycl_queue() { + OPENVINO_ASSERT(sycl_queue != nullptr); + return *sycl_queue; +} + +} // namespace ocl +} // namespace cldnn diff --git a/src/plugins/intel_gpu/src/runtime/ocl/sycl_stream.hpp b/src/plugins/intel_gpu/src/runtime/ocl/sycl_stream.hpp new file mode 100644 index 00000000000000..208b155d519dba --- /dev/null +++ b/src/plugins/intel_gpu/src/runtime/ocl/sycl_stream.hpp @@ -0,0 +1,26 @@ +// Copyright (C) 2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "ocl_stream.hpp" +#include "sycl_engine.hpp" + +#include "sycl/sycl.hpp" + + +namespace cldnn { +namespace ocl { + +class sycl_stream : public ocl_stream { +public: + sycl_stream(const sycl_engine& engine, const ExecutionConfig& config); + sycl_stream(const sycl_engine &engine, const ExecutionConfig& config, void *handle); + + ::sycl::queue& get_sycl_queue(); + std::unique_ptr<::sycl::queue> sycl_queue = nullptr; +}; + +} // namespace ocl +} // namespace cldnn diff --git a/src/plugins/intel_gpu/src/runtime/stream.cpp b/src/plugins/intel_gpu/src/runtime/stream.cpp index 0ee63a78b806e5..0fab4d38a1402a 100644 --- a/src/plugins/intel_gpu/src/runtime/stream.cpp +++ b/src/plugins/intel_gpu/src/runtime/stream.cpp @@ -12,7 +12,9 @@ namespace cldnn { QueueTypes stream::detect_queue_type(engine_types engine_type, void* queue_handle) { switch (engine_type) { - case engine_types::ocl: return ocl::ocl_stream::detect_queue_type(queue_handle); + case engine_types::sycl: + case engine_types::ocl: + return ocl::ocl_stream::detect_queue_type(queue_handle); default: throw std::runtime_error("Invalid engine type"); } } diff --git a/src/plugins/intel_gpu/tests/unit/CMakeLists.txt b/src/plugins/intel_gpu/tests/unit/CMakeLists.txt index 8bff8f56c50156..2c7a4b12735cc0 100644 --- a/src/plugins/intel_gpu/tests/unit/CMakeLists.txt +++ b/src/plugins/intel_gpu/tests/unit/CMakeLists.txt @@ -11,6 +11,11 @@ if(CMAKE_CXX_COMPILER_ID STREQUAL "MSVC") ov_add_compiler_flags(/wd4305) endif() +if(OV_COMPILER_IS_INTEL_LLVM) + set(CMAKE_EXE_LINKER_FLAGS "${CMAKE_EXE_LINKER_FLAGS} -fuse-ld=lld") + string(REPLACE "-pie" "" CMAKE_EXE_LINKER_FLAGS_RELEASE "${CMAKE_EXE_LINKER_FLAGS_RELEASE}") +endif() + set(TARGET_NAME "ov_gpu_unit_tests") file(GLOB_RECURSE SOURCES_MAIN diff --git a/src/plugins/intel_npu/src/plugin/CMakeLists.txt b/src/plugins/intel_npu/src/plugin/CMakeLists.txt index f071d001d44994..30e4eae254de90 100644 --- a/src/plugins/intel_npu/src/plugin/CMakeLists.txt +++ b/src/plugins/intel_npu/src/plugin/CMakeLists.txt @@ -66,7 +66,7 @@ target_include_directories(${TARGET_NAME} ${NPU_PLUGIN_SOURCE_DIR}/thirdparty/level-zero-ext ) -if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" OR CMAKE_CXX_COMPILER_ID MATCHES "GNU") +if(CMAKE_CXX_COMPILER_ID MATCHES "Clang" OR CMAKE_CXX_COMPILER_ID MATCHES "GNU" OR OV_COMPILER_IS_INTEL_LLVM) target_compile_options(${TARGET_NAME} PRIVATE -mavx2 -mf16c) elseif(CMAKE_CXX_COMPILER_ID MATCHES "MSVC") target_compile_options(${TARGET_NAME} PRIVATE /arch:AVX2) From b38eb133e9fa0676441e807291a04ed243d639be Mon Sep 17 00:00:00 2001 From: Ilya Lavrenov Date: Fri, 23 Aug 2024 01:45:48 +0400 Subject: [PATCH 24/35] Cmake: generalized THREADING=OMP to work on supported platforms (#26175) ### Details: - *item1* - *...* ### Tickets: - *ticket-id* --- cmake/dependencies.cmake | 94 +++++----- .../developer_package/packaging/archive.cmake | 2 + .../packaging/common-libraries.cmake | 2 + .../packaging/debian/debian.cmake | 2 + cmake/developer_package/packaging/npm.cmake | 2 + cmake/developer_package/packaging/nsis.cmake | 2 + .../developer_package/packaging/rpm/rpm.cmake | 2 + cmake/features.cmake | 14 +- cmake/templates/OpenVINOConfig.cmake.in | 54 +++++- .../OpenVINODeveloperPackageConfig.cmake.in | 3 +- ...DeveloperPackageConfigRelocatable.cmake.in | 4 +- src/bindings/python/CMakeLists.txt | 4 +- src/bindings/python/wheel/CMakeLists.txt | 5 - src/bindings/python/wheel/setup.py | 8 + src/cmake/ov_parallel.cmake | 168 ++++++++++-------- src/common/util/src/file_util.cpp | 4 +- 16 files changed, 230 insertions(+), 140 deletions(-) diff --git a/cmake/dependencies.cmake b/cmake/dependencies.cmake index 2ebfceb8b9612e..a152ff85c9c768 100644 --- a/cmake/dependencies.cmake +++ b/cmake/dependencies.cmake @@ -9,55 +9,55 @@ ov_set_temp_directory(TEMP "${CMAKE_SOURCE_DIR}") ## Intel OMP package if(THREADING STREQUAL "OMP") - reset_deps_cache(OMP) - if(WIN32 AND X86_64) - RESOLVE_DEPENDENCY(OMP - ARCHIVE_WIN "iomp.zip" - TARGET_PATH "${TEMP}/omp" - ENVIRONMENT "OMP" - VERSION_REGEX ".*_([a-z]*_([a-z0-9]+\\.)*[0-9]+).*" - SHA256 "62c68646747fb10f19b53217cb04a1e10ff93606f992e6b35eb8c31187c68fbf" - USE_NEW_LOCATION TRUE) - elseif(LINUX AND X86_64 AND OPENVINO_GNU_LIBC) - RESOLVE_DEPENDENCY(OMP - ARCHIVE_LIN "iomp.tgz" - TARGET_PATH "${TEMP}/omp" - ENVIRONMENT "OMP" - VERSION_REGEX ".*_([a-z]*_([a-z0-9]+\\.)*[0-9]+).*" - SHA256 "7832b16d82513ee880d97c27c7626f9525ebd678decf6a8fe6c38550f73227d9" - USE_NEW_LOCATION TRUE) - elseif(APPLE AND X86_64) - RESOLVE_DEPENDENCY(OMP - ARCHIVE_MAC "iomp_20190130_mac.tgz" - TARGET_PATH "${TEMP}/omp" - ENVIRONMENT "OMP" - VERSION_REGEX ".*_([a-z]*_([a-z0-9]+\\.)*[0-9]+).*" - SHA256 "591ea4a7e08bbe0062648916f42bded71d24c27f00af30a8f31a29b5878ea0cc" - USE_NEW_LOCATION TRUE) - else() - message(WARNING "Pre-built Intel OMP is not available on current platform. System OMP will be used.") - find_package(OpenMP) - if(OpenMP_CXX_FOUND) - foreach(OpenMP_LIB ${OpenMP_CXX_LIBRARIES}) - string(FIND ${OpenMP_LIB} "omp" OpenMP_LIB_OMP_INDEX) - if(NOT OpenMP_LIB_OMP_INDEX EQUAL -1) - cmake_path(GET OpenMP_LIB PARENT_PATH OpenMP_LIB_DIR) - set(OMP_LIB ${OpenMP_LIB} CACHE FILEPATH "Path to OMP library") - set(OMP ${OpenMP_LIB_DIR} CACHE FILEPATH "Path to OMP root folder") - return() - endif() - endforeach() + # check whether the compiler supports OpenMP at all + find_package(OpenMP) + + if(NOT OpenMP_CXX_FOUND) + message(WARNING "Compiler does not support OpenMP standard. Falling back to SEQ threading") + set(THREADING "SEQ") + set(ENABLE_INTEL_OPENMP OFF) + endif() + + if(ENABLE_INTEL_OPENMP) + reset_deps_cache(OMP) + if(WIN32 AND X86_64) + RESOLVE_DEPENDENCY(INTEL_OMP + ARCHIVE_WIN "iomp.zip" + TARGET_PATH "${TEMP}/omp" + ENVIRONMENT "INTEL_OMP" + VERSION_REGEX ".*_([a-z]*_([a-z0-9]+\\.)*[0-9]+).*" + SHA256 "62c68646747fb10f19b53217cb04a1e10ff93606f992e6b35eb8c31187c68fbf" + USE_NEW_LOCATION TRUE) + elseif(LINUX AND X86_64 AND OPENVINO_GNU_LIBC) + RESOLVE_DEPENDENCY(INTEL_OMP + ARCHIVE_LIN "iomp.tgz" + TARGET_PATH "${TEMP}/omp" + ENVIRONMENT "INTEL_OMP" + VERSION_REGEX ".*_([a-z]*_([a-z0-9]+\\.)*[0-9]+).*" + SHA256 "7832b16d82513ee880d97c27c7626f9525ebd678decf6a8fe6c38550f73227d9" + USE_NEW_LOCATION TRUE) + elseif(APPLE AND X86_64) + RESOLVE_DEPENDENCY(INTEL_OMP + ARCHIVE_MAC "iomp_20190130_mac.tgz" + TARGET_PATH "${TEMP}/omp" + ENVIRONMENT "INTEL_OMP" + VERSION_REGEX ".*_([a-z]*_([a-z0-9]+\\.)*[0-9]+).*" + SHA256 "591ea4a7e08bbe0062648916f42bded71d24c27f00af30a8f31a29b5878ea0cc" + USE_NEW_LOCATION TRUE) + endif() + + if(INTEL_OMP) + update_deps_cache(INTEL_OMP "${INTEL_OMP}" "Path to OMP root folder") + debug_message(STATUS "intel_omp=" ${INTEL_OMP}) + + ov_cpack_add_component(omp HIDDEN) + file(GLOB_RECURSE source_list "${INTEL_OMP}/*${CMAKE_SHARED_LIBRARY_SUFFIX}*") + install(FILES ${source_list} + DESTINATION ${OV_CPACK_RUNTIMEDIR} + COMPONENT intel_omp + ${OV_CPACK_COMP_OPENMP_EXCLUDE_ALL}) endif() - message(FATAL_ERROR "System OpenMP has not been found") endif() - update_deps_cache(OMP "${OMP}" "Path to OMP root folder") - debug_message(STATUS "intel_omp=" ${OMP}) - - ov_cpack_add_component(omp HIDDEN) - file(GLOB_RECURSE source_list "${OMP}/*${CMAKE_SHARED_LIBRARY_SUFFIX}*") - install(FILES ${source_list} - DESTINATION ${OV_CPACK_RUNTIMEDIR} - COMPONENT omp) endif() ## TBB package diff --git a/cmake/developer_package/packaging/archive.cmake b/cmake/developer_package/packaging/archive.cmake index da6c676de6c574..6df5145ae2e488 100644 --- a/cmake/developer_package/packaging/archive.cmake +++ b/cmake/developer_package/packaging/archive.cmake @@ -74,6 +74,8 @@ macro(ov_define_component_include_rules) # tbb unset(OV_CPACK_COMP_TBB_EXCLUDE_ALL) unset(OV_CPACK_COMP_TBB_DEV_EXCLUDE_ALL) + # openmp + unset(OV_CPACK_COMP_OPENMP_EXCLUDE_ALL) # licensing unset(OV_CPACK_COMP_LICENSING_EXCLUDE_ALL) # samples diff --git a/cmake/developer_package/packaging/common-libraries.cmake b/cmake/developer_package/packaging/common-libraries.cmake index 4ec96dc28b53e8..581c9144c1b907 100644 --- a/cmake/developer_package/packaging/common-libraries.cmake +++ b/cmake/developer_package/packaging/common-libraries.cmake @@ -79,6 +79,8 @@ macro(ov_define_component_include_rules) # tbb set(OV_CPACK_COMP_TBB_EXCLUDE_ALL EXCLUDE_FROM_ALL) set(OV_CPACK_COMP_TBB_DEV_EXCLUDE_ALL EXCLUDE_FROM_ALL) + # openmp + set(OV_CPACK_COMP_OPENMP_EXCLUDE_ALL EXCLUDE_FROM_ALL) # licensing if(CPACK_GENERATOR STREQUAL "CONAN") unset(OV_CPACK_COMP_LICENSING_EXCLUDE_ALL) diff --git a/cmake/developer_package/packaging/debian/debian.cmake b/cmake/developer_package/packaging/debian/debian.cmake index c7f49419111cea..f133428d66ec74 100644 --- a/cmake/developer_package/packaging/debian/debian.cmake +++ b/cmake/developer_package/packaging/debian/debian.cmake @@ -82,6 +82,8 @@ macro(ov_define_component_include_rules) # tbb set(OV_CPACK_COMP_TBB_EXCLUDE_ALL EXCLUDE_FROM_ALL) set(OV_CPACK_COMP_TBB_DEV_EXCLUDE_ALL EXCLUDE_FROM_ALL) + # openmp + set(OV_CPACK_COMP_OPENMP_EXCLUDE_ALL EXCLUDE_FROM_ALL) # licensing set(OV_CPACK_COMP_LICENSING_EXCLUDE_ALL EXCLUDE_FROM_ALL) # samples diff --git a/cmake/developer_package/packaging/npm.cmake b/cmake/developer_package/packaging/npm.cmake index 24453965125348..2a2509cdcae65a 100644 --- a/cmake/developer_package/packaging/npm.cmake +++ b/cmake/developer_package/packaging/npm.cmake @@ -61,6 +61,8 @@ macro(ov_define_component_include_rules) # tbb unset(OV_CPACK_COMP_TBB_EXCLUDE_ALL) set(OV_CPACK_COMP_TBB_DEV_EXCLUDE_ALL EXCLUDE_FROM_ALL) + # openmp + unset(OV_CPACK_COMP_OPENMP_EXCLUDE_ALL) # licensing unset(OV_CPACK_COMP_LICENSING_EXCLUDE_ALL) # samples diff --git a/cmake/developer_package/packaging/nsis.cmake b/cmake/developer_package/packaging/nsis.cmake index f5f9a233e8b87f..d7d8ed152c49d9 100644 --- a/cmake/developer_package/packaging/nsis.cmake +++ b/cmake/developer_package/packaging/nsis.cmake @@ -116,6 +116,8 @@ macro(ov_define_component_include_rules) # tbb unset(OV_CPACK_COMP_TBB_EXCLUDE_ALL) unset(OV_CPACK_COMP_TBB_DEV_EXCLUDE_ALL) + # openmp + unset(OV_CPACK_COMP_OPENMP_EXCLUDE_ALL) # licensing unset(OV_CPACK_COMP_LICENSING_EXCLUDE_ALL) # samples diff --git a/cmake/developer_package/packaging/rpm/rpm.cmake b/cmake/developer_package/packaging/rpm/rpm.cmake index 7c9fb4f22a372d..b7c482555bd131 100644 --- a/cmake/developer_package/packaging/rpm/rpm.cmake +++ b/cmake/developer_package/packaging/rpm/rpm.cmake @@ -73,6 +73,8 @@ macro(ov_define_component_include_rules) # tbb set(OV_CPACK_COMP_TBB_EXCLUDE_ALL EXCLUDE_FROM_ALL) set(OV_CPACK_COMP_TBB_DEV_EXCLUDE_ALL EXCLUDE_FROM_ALL) + # openmp + set(OV_CPACK_COMP_OPENMP_EXCLUDE_ALL EXCLUDE_FROM_ALL) # licensing set(OV_CPACK_COMP_LICENSING_EXCLUDE_ALL EXCLUDE_FROM_ALL) # samples diff --git a/cmake/features.cmake b/cmake/features.cmake index fa11b1894dfbba..d37c113e866ab8 100644 --- a/cmake/features.cmake +++ b/cmake/features.cmake @@ -85,8 +85,9 @@ ov_dependent_option (ENABLE_PKGCONFIG_GEN "Enable openvino.pc pkg-config file ge # # "OneDNN library based on OMP or TBB or Sequential implementation: TBB|OMP|SEQ" -if(RISCV64) +if(RISCV64 OR ANDROID) # oneDNN does not support non-SEQ for RISC-V architecture + # on Android we experience SEGFAULT during compilation set(THREADING_DEFAULT "SEQ") else() set(THREADING_DEFAULT "TBB") @@ -101,6 +102,17 @@ if(NOT THREADING IN_LIST THREADING_OPTIONS) message(FATAL_ERROR "THREADING should be set to either ${THREADING_OPTIONS}") endif() +if(X86_64 AND (WIN32 OR LINUX)) + # we have a precompiled version of Intel OMP only for this platforms + set(ENABLE_INTEL_OPENMP_DEFAULT ON) + # temporart override to OFF for testing purposes + set(ENABLE_INTEL_OPENMP_DEFAULT OFF) +else() + set(ENABLE_INTEL_OPENMP_DEFAULT OFF) +endif() + +ov_dependent_option (ENABLE_INTEL_OPENMP "Enables usage of Intel OpenMP instead of default compiler one" ${ENABLE_INTEL_OPENMP_DEFAULT} "THREADING STREQUAL SEQ" OFF) + if((THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") AND (BUILD_SHARED_LIBS OR (LINUX AND X86_64))) set(ENABLE_TBBBIND_2_5_DEFAULT ON) diff --git a/cmake/templates/OpenVINOConfig.cmake.in b/cmake/templates/OpenVINOConfig.cmake.in index af8dd1596c4a39..a276cf2c24eb66 100644 --- a/cmake/templates/OpenVINOConfig.cmake.in +++ b/cmake/templates/OpenVINOConfig.cmake.in @@ -10,6 +10,7 @@ # The following components are supported: # # * `Runtime`: OpenVINO C++ and C Core & Inference Runtime, frontend common +# * `Threading`: OpenVINO threading backend for parallel.hpp # * `JAX`: OpenVINO JAX frontend # * `ONNX`: OpenVINO ONNX frontend # * `Paddle`: OpenVINO Paddle frontend @@ -25,6 +26,10 @@ # # find_package(OpenVINO REQUIRED COMPONENTS Runtime ONNX) # +# If OpenVINO's openvino/core/parallel.hpp is required: +# +# find_package(OpenVINO REQUIRED COMPONENTS Runtime Threading) +# # Imported Targets: # ------ # @@ -150,9 +155,25 @@ macro(_ov_find_dependency dep) unset(cmake_fd_quiet_arg) endmacro() +macro(_ov_find_openmp) + set(_ov_threading "@THREADING@") + set(_ov_enable_intel_omp "@ENABLE_INTEL_OPENMP@") + + if(_ov_threading STREQUAL "OMP") + if(_ov_enable_intel_omp) + # TODO: write code to import iomp libraries + else() + _ov_find_dependency(OpenMP) + endif() + endif() + + unset(_ov_enable_intel_omp) + unset(_ov_threading) +endmacro() + macro(_ov_find_tbb) - set(THREADING "@THREADING@") - if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") + set(_ov_threading "@THREADING@") + if(_ov_threading STREQUAL "TBB" OR _ov_threading STREQUAL "TBB_AUTO") set(enable_pkgconfig_tbb "@tbb_FOUND@") # try tbb.pc @@ -224,6 +245,8 @@ macro(_ov_find_tbb) unset(enable_system_tbb) _ov_find_dependency(TBB + # we need to find at least the same version as we compiled with + @TBB_VERSION@ COMPONENTS tbb tbbmalloc ${find_package_tbb_extra_args}) unset(find_package_tbb_extra_args) @@ -245,6 +268,8 @@ macro(_ov_find_tbb) endif() unset(install_tbbbind) endif() + + unset(_ov_threading) endmacro() macro(_ov_find_pugixml) @@ -494,9 +519,14 @@ if(NOT TARGET openvino) endif() endif() +macro(_ov_enable_threading_interface) + _ov_find_tbb() + _ov_find_openmp() +endmacro() + if(NOT _OV_ENABLE_OPENVINO_BUILD_SHARED) # common openvino dependencies - _ov_find_tbb() + _ov_enable_threading_interface() _ov_find_itt() _ov_find_pugixml() @@ -549,6 +579,7 @@ endif() # set(${CMAKE_FIND_PACKAGE_NAME}_Runtime_FOUND ON) +set(${CMAKE_FIND_PACKAGE_NAME}_Threading_FOUND ON) set(${CMAKE_FIND_PACKAGE_NAME}_ONNX_FOUND @ENABLE_OV_ONNX_FRONTEND@) set(${CMAKE_FIND_PACKAGE_NAME}_Paddle_FOUND @ENABLE_OV_PADDLE_FRONTEND@) @@ -571,6 +602,23 @@ if(NOT ${CMAKE_FIND_PACKAGE_NAME}_FIND_COMPONENTS) set(${CMAKE_FIND_PACKAGE_NAME}_FIND_COMPONENTS Runtime) endif() +if(Threading IN_LIST ${CMAKE_FIND_PACKAGE_NAME}_FIND_COMPONENTS) + _ov_enable_threading_interface() + + if(TARGET TBB::tbb) + set(_ov_threading_lib TBB::tbb) + elseif(TARGET IntelOpenMP::OpenMP_CXX) + set(_ov_threading_lib IntelOpenMP::OpenMP_CXX) + elseif(TARGET OpenMP::OpenMP_CXX) + set(_ov_threading_lib OpenMP::OpenMP_CXX) + endif() + + if(_ov_threading_lib) + set_property(TARGET openvino::runtime APPEND PROPERTY INTERFACE_LINK_LIBRARIES ${_ov_threading_lib}) + unset(_ov_threading_lib) + endif() +endif() + # # Apply common functions # diff --git a/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in b/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in index fe3f82a513e08c..22eb432a97400a 100644 --- a/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in +++ b/cmake/templates/OpenVINODeveloperPackageConfig.cmake.in @@ -70,11 +70,12 @@ find_dependency(OpenVINODeveloperScripts NO_DEFAULT_PATH) find_dependency(OpenVINO + COMPONENTS ${${CMAKE_FIND_PACKAGE_NAME}_FIND_COMPONENTS} PATHS "${CMAKE_CURRENT_LIST_DIR}" NO_CMAKE_FIND_ROOT_PATH NO_DEFAULT_PATH) -_ov_find_tbb() +_ov_enable_threading_interface() _ov_find_pugixml() include("${CMAKE_CURRENT_LIST_DIR}/openvino_developer_package_targets.cmake") diff --git a/cmake/templates/OpenVINODeveloperPackageConfigRelocatable.cmake.in b/cmake/templates/OpenVINODeveloperPackageConfigRelocatable.cmake.in index 613058cecc45a7..be8fc1fa802e23 100644 --- a/cmake/templates/OpenVINODeveloperPackageConfigRelocatable.cmake.in +++ b/cmake/templates/OpenVINODeveloperPackageConfigRelocatable.cmake.in @@ -52,14 +52,14 @@ set(CMAKE_COMPILE_WARNING_AS_ERROR OFF) # # OpenVINO_DIR is supposed to be set as an environment variable -find_dependency(OpenVINO) +find_dependency(OpenVINO COMPONENTS ${${CMAKE_FIND_PACKAGE_NAME}_FIND_COMPONENTS}) find_dependency(OpenVINODeveloperScripts PATHS "${CMAKE_CURRENT_LIST_DIR}" NO_CMAKE_FIND_ROOT_PATH NO_DEFAULT_PATH) -_ov_find_tbb() +_ov_enable_threading_interface() _ov_find_pugixml() include("${CMAKE_CURRENT_LIST_DIR}/OpenVINODeveloperPackageTargets.cmake") diff --git a/src/bindings/python/CMakeLists.txt b/src/bindings/python/CMakeLists.txt index dc816d73fa79b1..666b6bcd0d6969 100644 --- a/src/bindings/python/CMakeLists.txt +++ b/src/bindings/python/CMakeLists.txt @@ -251,7 +251,9 @@ endmacro() macro(ov_define_setup_py_dependencies) foreach(_target # Python API dependencies - _pyopenvino py_ov_frontends + pyopenvino py_ov_frontends + # C API + openvino_c # plugins ov_plugins # frontends diff --git a/src/bindings/python/wheel/CMakeLists.txt b/src/bindings/python/wheel/CMakeLists.txt index 97b5795c7a7e2c..b12921280c84c4 100644 --- a/src/bindings/python/wheel/CMakeLists.txt +++ b/src/bindings/python/wheel/CMakeLists.txt @@ -126,11 +126,6 @@ else() --plat-name=${PLATFORM_TAG}) endif() -# dev target includes c headers and we need to include c bindings -if(TARGET openvino_c) - list(APPEND ov_setup_py_deps openvino_c) -endif() - add_custom_command(OUTPUT ${openvino_wheel_path} COMMAND ${setup_py_env} ${wheel_build_command} diff --git a/src/bindings/python/wheel/setup.py b/src/bindings/python/wheel/setup.py index f88d9bd3da7592..b19c64f3d5d811 100644 --- a/src/bindings/python/wheel/setup.py +++ b/src/bindings/python/wheel/setup.py @@ -122,6 +122,12 @@ "rpath": LIBS_RPATH, "binary_dir": OPENVINO_BINARY_DIR, }, + "intel_openmp_libs": { + "name": "intel_omp", + "prefix": f"{BUILD_BASE}/libs.intel_omp", + "install_dir": OV_RUNTIME_LIBS_DIR, + "binary_dir": OPENVINO_BINARY_DIR, + }, "pugixml_libs": { "name": "pugixml", "prefix": f"{BUILD_BASE}/libs.pugixml", @@ -476,6 +482,8 @@ def copy_package_data(self, src_dirs): os.makedirs(package_cmake_dir, exist_ok=True) openvino_replacements = { + # change the location of TBBConfig.cmake (runtime/3rdparty/tbb -> openvino/cmake) + r"(\{PACKAGE_PREFIX_DIR\})\/runtime\/3rdparty\/tbb": rf"\1/{WHEEL_PACKAGE_DIR}/cmake", # change the path where the libraries are installed (runtime/lib/intel64/Release -> openvino/libs) r"(\{_IMPORT_PREFIX\})\/(.*)\/(.+\.[lib|dylib|so|dll])": rf"\1/{WHEEL_LIBS_INSTALL_DIR}/\3", # change the path where the include files are installed (runtime/include -> openvino/include) diff --git a/src/cmake/ov_parallel.cmake b/src/cmake/ov_parallel.cmake index cfb69ce7b1445f..51549b82baa6f6 100644 --- a/src/cmake/ov_parallel.cmake +++ b/src/cmake/ov_parallel.cmake @@ -249,6 +249,80 @@ macro(ov_find_package_tbb) endif() endmacro() +macro(ov_find_package_openmp) + # check whether the compiler supports OpenMP at all + find_package(OpenMP) + + # check if Intel OpenMP is downloaded and override system library + if(THREADING STREQUAL "OMP") + if(INTEL_OMP) + if(WIN32) + set(iomp_lib_name libiomp5md) + else() + set(iomp_lib_name iomp5) + endif() + + set(lib_rel_path ${INTEL_OMP}/lib) + set(lib_dbg_path ${lib_rel_path}) + + find_library(INTEL_OMP_LIBRARIES_RELEASE NAMES ${iomp_lib_name} PATHS ${lib_rel_path} REQUIRED NO_DEFAULT_PATH) + list(APPEND iomp_imported_configurations RELEASE) + + # try to find debug libraries as well + if(WIN32) + set(iomp_link_flags + # avoid linking default OpenMP + # https://learn.microsoft.com/en-us/cpp/parallel/openmp/reference/openmp-library-reference?view=msvc-170 + INTERFACE_LINK_OPTIONS -nodefaultlib:vcomp) + + # location of .lib file + string(REPLACE ".dll" ".lib" INTEL_OMP_IMPLIB_RELEASE "${INTEL_OMP_LIBRARIES_RELEASE}") + set(iomp_implib_location_release + IMPORTED_IMPLIB_RELEASE "${INTEL_OMP_IMPLIB_RELEASE}") + + find_library(INTEL_OMP_LIBRARIES_DEBUG NAMES ${iomp_lib_name} PATHS ${lib_dbg_path} NO_DEFAULT_PATH) + if(INTEL_OMP_LIBRARIES_DEBUG) + string(REPLACE ".dll" ".lib" INTEL_OMP_IMPLIB_DEBUG "${INTEL_OMP_LIBRARIES_DEBUG}") + + list(APPEND iomp_imported_configurations DEBUG) + set(iomp_imported_locations_debug + IMPORTED_LOCATION_DEBUG "${INTEL_OMP_LIBRARIES_DEBUG}" + IMPORTED_IMPLIB_DEBUG "${INTEL_OMP_IMPLIB_DEBUG}") + else() + set(iomp_map_imported_debug_configuration MAP_IMPORTED_CONFIG_DEBUG Release) + message(WARNING "OMP Debug binaries are missed.") + endif() + endif() + + # create imported target + if(NOT TARGET IntelOpenMP::OpenMP_CXX) + add_library(IntelOpenMP::OpenMP_CXX SHARED IMPORTED) + set_property(TARGET IntelOpenMP::OpenMP_CXX APPEND PROPERTY + IMPORTED_CONFIGURATIONS ${iomp_imported_configurations}) + set_target_properties(IntelOpenMP::OpenMP_CXX PROPERTIES + # reuse standard OpenMP compiler flags + INTERFACE_COMPILE_OPTIONS ${OpenMP_CXX_FLAGS} + # location of release library (both .dll and lib.) + IMPORTED_LOCATION_RELEASE "${INTEL_OMP_LIBRARIES_RELEASE}" + ${iomp_implib_location_release} + # the same for debug libs + ${iomp_imported_locations_debug} + # linker flags to override system OpenMP + ${iomp_link_flags} + # map imported configurations if required + ${iomp_map_imported_debug_configuration} + MAP_IMPORTED_CONFIG_MINSIZEREL Release + MAP_IMPORTED_CONFIG_RELWITHDEBINFO Release) + endif() + endif() + + # falling back to system OpenMP then + if(NOT TARGET IntelOpenMP::OpenMP_CXX) + _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} OpenMP::OpenMP_CXX) + endif() + endif() +endmacro() + function(ov_set_threading_interface_for TARGET_NAME) if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO" AND NOT TBB_FOUND) # find TBB @@ -316,95 +390,33 @@ function(ov_set_threading_interface_for TARGET_NAME) set(OV_THREAD_DEFINE "OV_THREAD_SEQ") - if (THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") - if (TBB_FOUND) + if(THREADING STREQUAL "TBB" OR THREADING STREQUAL "TBB_AUTO") + if(TBB_FOUND) set(OV_THREAD_DEFINE "OV_THREAD_TBB") _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} TBB::tbb) target_compile_definitions(${TARGET_NAME} ${COMPILE_DEF_TYPE} TBB_PREVIEW_WAITING_FOR_WORKERS=1) - else () + else() set(THREADING "SEQ" PARENT_SCOPE) message(WARNING "TBB was not found by the configured TBB_DIR path.\ SEQ method will be used for ${TARGET_NAME}") - endif () - elseif (THREADING STREQUAL "OMP") - if (WIN32) - set(omp_lib_name libiomp5md) - elseif (ARM OR AARCH64) - get_filename_component(OpenMP_CXX_LIB_NAME ${OMP_LIB} NAME) - string(REGEX REPLACE "^lib" "" OpenMP_CXX_LIB_NAME ${OpenMP_CXX_LIB_NAME}) - string(REGEX REPLACE "\\.[^.]*$" "" OpenMP_CXX_LIB_NAME ${OpenMP_CXX_LIB_NAME}) - set(omp_lib_name ${OpenMP_CXX_LIB_NAME}) - else () - set(omp_lib_name iomp5) - endif () - - if (NOT OpenVINO_SOURCE_DIR) - # TODO: dead code since ov_parallel.cmake is not used outside of OpenVINO build - if (WIN32) - set(lib_rel_path ${IE_LIB_REL_DIR}) - set(lib_dbg_path ${IE_LIB_DBG_DIR}) - else () - set(lib_rel_path ${IE_EXTERNAL_DIR}/omp/lib) - set(lib_dbg_path ${lib_rel_path}) - endif () - else () - if (ARM OR AARCH64) - set(lib_rel_path ${OMP}) - else() - set(lib_rel_path ${OMP}/lib) - endif () - set(lib_dbg_path ${lib_rel_path}) - endif () - - if (NOT OMP_LIBRARIES_RELEASE) - find_library(OMP_LIBRARIES_RELEASE ${omp_lib_name} ${lib_rel_path} NO_DEFAULT_PATH) - message(STATUS "OMP Release lib: ${OMP_LIBRARIES_RELEASE}") - if (NOT LINUX) - find_library(OMP_LIBRARIES_DEBUG ${omp_lib_name} ${lib_dbg_path} NO_DEFAULT_PATH) - if (OMP_LIBRARIES_DEBUG) - message(STATUS "OMP Debug lib: ${OMP_LIBRARIES_DEBUG}") - else () - message(WARNING "OMP Debug binaries are missed.") - endif () - endif () - endif () + endif() + elseif(THREADING STREQUAL "OMP") + ov_find_package_openmp() - if (NOT OMP_LIBRARIES_RELEASE) - message(WARNING "Intel OpenMP not found. Intel OpenMP support will be disabled. ${OV_THREAD_DEFINE} is defined") - set(THREADING "SEQ" PARENT_SCOPE) - else () + if(TARGET IntelOpenMP::OpenMP_CXX) set(OV_THREAD_DEFINE "OV_THREAD_OMP") - - if (WIN32) - target_compile_options(${TARGET_NAME} ${LINK_TYPE} ${OpenMP_CXX_FLAGS} /openmp) - target_compile_options(${TARGET_NAME} ${LINK_TYPE} ${OpenMP_CXX_FLAGS} /Qopenmp) - _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} "-nodefaultlib:vcomp") - else() - target_compile_options(${TARGET_NAME} ${LINK_TYPE} ${OpenMP_CXX_FLAGS} -fopenmp) - endif () - - # Debug binaries are optional. - if (OMP_LIBRARIES_DEBUG AND NOT LINUX) - if (WIN32) - _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} "$<$:${OMP_LIBRARIES_DEBUG}>;$<$>:${OMP_LIBRARIES_RELEASE}>") - else() - # TODO: handle multi-config generators case - if (CMAKE_BUILD_TYPE STREQUAL "Debug") - _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} ${OMP_LIBRARIES_DEBUG}) - else() - _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} ${OMP_LIBRARIES_RELEASE}) - endif () - endif () - else () - # Link Release library to all configurations. - _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} ${OMP_LIBRARIES_RELEASE}) - endif () - endif () - endif () + _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} IntelOpenMP::OpenMP_CXX) + elseif(TARGET OpenMP::OpenMP_CXX) + set(OV_THREAD_DEFINE "OV_THREAD_OMP") + _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} OpenMP::OpenMP_CXX) + else() + message(FATAL_ERROR "Internal error: OpenMP is not supported by compiler. Switch to SEQ should be performed before") + endif() + endif() target_compile_definitions(${TARGET_NAME} ${COMPILE_DEF_TYPE} -DOV_THREAD=${OV_THREAD_DEFINE}) - if (NOT THREADING STREQUAL "SEQ") + if(NOT THREADING STREQUAL "SEQ") find_package(Threads REQUIRED) _ov_target_link_libraries(${TARGET_NAME} ${LINK_TYPE} Threads::Threads) endif() diff --git a/src/common/util/src/file_util.cpp b/src/common/util/src/file_util.cpp index 2846355e9aeb5f..c9bf331bbfe6d3 100644 --- a/src/common/util/src/file_util.cpp +++ b/src/common/util/src/file_util.cpp @@ -340,7 +340,7 @@ void ov::util::convert_path_win_style(std::string& path) { #ifdef OPENVINO_ENABLE_UNICODE_PATH_SUPPORT -# ifdef __APPLE__ +# ifdef __clang__ # pragma clang diagnostic push # pragma clang diagnostic ignored "-Wdeprecated-declarations" # endif @@ -372,7 +372,7 @@ std::wstring ov::util::string_to_wstring(const std::string& string) { # endif } -# ifdef __APPLE__ +# ifdef __clang__ # pragma clang diagnostic pop # endif From c8839fa5339faa2b7cb1cfac1863717574c85d6b Mon Sep 17 00:00:00 2001 From: Maxim Vafin Date: Thu, 22 Aug 2024 22:50:15 +0200 Subject: [PATCH 25/35] [PT FE] Remove opset usage (#26181) ### Details: - *Remove usage of opset* ### Tickets: - *ticket-id* --- .../frontend/pytorch/node_context.hpp | 12 +- src/frontends/pytorch/src/node_context.cpp | 6 +- src/frontends/pytorch/src/op/log_softmax.cpp | 8 +- .../src/op/native_multi_head_attention.cpp | 181 ++++++------- .../transforms/prim_list_unpack_replacer.cpp | 237 +++++++++--------- src/frontends/pytorch/src/utils.cpp | 130 +++++----- 6 files changed, 303 insertions(+), 271 deletions(-) diff --git a/src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp b/src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp index 110beedb8b2020..d97c2ef694a1eb 100644 --- a/src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp +++ b/src/frontends/pytorch/include/openvino/frontend/pytorch/node_context.hpp @@ -53,13 +53,21 @@ class NodeContext : public frontend::NodeContext { // TODO: int due to base class uses it, but naturally it should be size_t for PT Output get_input(int index) const override { size_t index_ = static_cast(index); - FRONT_END_GENERAL_CHECK(!m_decoder->input_is_none(index_), "Input doesn't exist with index: ", index); + FRONT_END_GENERAL_CHECK(!m_decoder->input_is_none(index_), + "Input doesn't exist with index: ", + index, + " for operation ", + get_op_type()); auto input = m_decoder_inputs.at(index); if (input == 0) { // Case when input can be inlined (possible only for fx decoder) if (m_decoder->is_input_inlined(index_)) { auto inlined_input = m_decoder->inlined_input(index_); - FRONT_END_GENERAL_CHECK(inlined_input.size() == 1, "Incorrect inlined input with index:", index); + FRONT_END_GENERAL_CHECK(inlined_input.size() == 1, + "Incorrect inlined input with index: ", + index, + " for operation ", + get_op_type()); return inlined_input[0]; } } diff --git a/src/frontends/pytorch/src/node_context.cpp b/src/frontends/pytorch/src/node_context.cpp index efc3bb107eb7f0..565b0cdbd39385 100644 --- a/src/frontends/pytorch/src/node_context.cpp +++ b/src/frontends/pytorch/src/node_context.cpp @@ -152,7 +152,11 @@ OutputVector NodeContext::inputs() const { // Case when input can be inlined (possible only for fx decoder) if (m_decoder->is_input_inlined(i)) { auto inlined_input = m_decoder->inlined_input(i); - FRONT_END_GENERAL_CHECK(inlined_input.size() == 1, "Incorrect inlined input with index:", i); + FRONT_END_GENERAL_CHECK(inlined_input.size() == 1, + "Incorrect inlined input with index: ", + i, + " for operation ", + get_op_type()); res.push_back(inlined_input[0]); continue; } diff --git a/src/frontends/pytorch/src/op/log_softmax.cpp b/src/frontends/pytorch/src/op/log_softmax.cpp index 6fb2e1c6be3241..500df2bf36bc20 100644 --- a/src/frontends/pytorch/src/op/log_softmax.cpp +++ b/src/frontends/pytorch/src/op/log_softmax.cpp @@ -2,8 +2,10 @@ // SPDX-License-Identifier: Apache-2.0 // +#include "openvino/op/log_softmax.hpp" + #include "openvino/frontend/pytorch/node_context.hpp" -#include "openvino/opsets/opset10.hpp" +#include "openvino/op/convert.hpp" #include "utils.hpp" namespace ov { @@ -30,11 +32,11 @@ OutputVector translate_log_softmax_common(const NodeContext& context, bool is_fx const auto target_dtype_i64 = context.const_input(2); const auto target_dtype = convert_dtype(target_dtype_i64); if (elem_type != target_dtype) { - input = context.mark_node(std::make_shared(input, target_dtype)); + input = context.mark_node(std::make_shared(input, target_dtype)); } } - const auto log_softmax = context.mark_node(std::make_shared(input, dim)); + const auto log_softmax = context.mark_node(std::make_shared(input, dim)); return {log_softmax}; }; diff --git a/src/frontends/pytorch/src/op/native_multi_head_attention.cpp b/src/frontends/pytorch/src/op/native_multi_head_attention.cpp index 3da7a0e5402669..cb8e33750d9a22 100644 --- a/src/frontends/pytorch/src/op/native_multi_head_attention.cpp +++ b/src/frontends/pytorch/src/op/native_multi_head_attention.cpp @@ -3,7 +3,23 @@ // #include "openvino/frontend/pytorch/node_context.hpp" -#include "openvino/opsets/opset10.hpp" +#include "openvino/op/add.hpp" +#include "openvino/op/broadcast.hpp" +#include "openvino/op/concat.hpp" +#include "openvino/op/divide.hpp" +#include "openvino/op/gather.hpp" +#include "openvino/op/logical_not.hpp" +#include "openvino/op/matmul.hpp" +#include "openvino/op/multiply.hpp" +#include "openvino/op/reduce_sum.hpp" +#include "openvino/op/reshape.hpp" +#include "openvino/op/select.hpp" +#include "openvino/op/shape_of.hpp" +#include "openvino/op/slice.hpp" +#include "openvino/op/softmax.hpp" +#include "openvino/op/sqrt.hpp" +#include "openvino/op/transpose.hpp" +#include "openvino/op/unsqueeze.hpp" #include "pt_framework_node.hpp" #include "utils.hpp" @@ -46,77 +62,69 @@ OutputVector translate_native_multi_head_attention(const NodeContext& context) { const auto average_weights = context.const_input(11); const auto minus_inf = - context.mark_node(opset10::Constant::create(element::f32, Shape{}, {-std::numeric_limits::infinity()})); - const auto embed_dim_i64 = context.mark_node(std::make_shared(embed_dim, element::i64)); - const auto num_head_i64 = context.mark_node(std::make_shared(num_head, element::i64)); - - const auto neg_one_1d = context.mark_node(opset10::Constant::create(element::i64, Shape{1}, {-1})); - const auto zero_1d = context.mark_node(opset10::Constant::create(element::i64, Shape{1}, {0})); - const auto one_1d = context.mark_node(opset10::Constant::create(element::i64, Shape{1}, {1})); - const auto two_1d = context.mark_node(opset10::Constant::create(element::i64, Shape{1}, {2})); - const auto three_1d = context.mark_node(opset10::Constant::create(element::i64, Shape{1}, {3})); - const auto heads_1d = context.mark_node(std::make_shared(num_head_i64, zero_1d)); - - const auto ev_1_slice_1d = context.mark_node(std::make_shared(one_1d, embed_dim_i64)); - const auto ev_2_slice_1d = context.mark_node(std::make_shared(two_1d, embed_dim_i64)); - const auto ev_3_slice_1d = context.mark_node(std::make_shared(three_1d, embed_dim_i64)); - - const auto qkv_shape = context.mark_node(std::make_shared(query)); - const auto batch_size = context.mark_node(std::make_shared(qkv_shape, zero_1d, zero_1d)); - const auto seq_size = context.mark_node(std::make_shared(qkv_shape, one_1d, zero_1d)); - const auto embed_div_heads = context.mark_node(std::make_shared(embed_dim_i64, heads_1d, true)); + context.mark_node(v0::Constant::create(element::f32, Shape{}, {-std::numeric_limits::infinity()})); + const auto embed_dim_i64 = context.mark_node(std::make_shared(embed_dim, element::i64)); + const auto num_head_i64 = context.mark_node(std::make_shared(num_head, element::i64)); + + const auto neg_one_1d = context.mark_node(v0::Constant::create(element::i64, Shape{1}, {-1})); + const auto zero_1d = context.mark_node(v0::Constant::create(element::i64, Shape{1}, {0})); + const auto one_1d = context.mark_node(v0::Constant::create(element::i64, Shape{1}, {1})); + const auto two_1d = context.mark_node(v0::Constant::create(element::i64, Shape{1}, {2})); + const auto three_1d = context.mark_node(v0::Constant::create(element::i64, Shape{1}, {3})); + const auto heads_1d = context.mark_node(std::make_shared(num_head_i64, zero_1d)); + + const auto ev_1_slice_1d = context.mark_node(std::make_shared(one_1d, embed_dim_i64)); + const auto ev_2_slice_1d = context.mark_node(std::make_shared(two_1d, embed_dim_i64)); + const auto ev_3_slice_1d = context.mark_node(std::make_shared(three_1d, embed_dim_i64)); + + const auto qkv_shape = context.mark_node(std::make_shared(query)); + const auto batch_size = context.mark_node(std::make_shared(qkv_shape, zero_1d, zero_1d)); + const auto seq_size = context.mark_node(std::make_shared(qkv_shape, one_1d, zero_1d)); + const auto embed_div_heads = context.mark_node(std::make_shared(embed_dim_i64, heads_1d, true)); const auto query_proj_weight = - context.mark_node(std::make_shared(qkv_weight, zero_1d, ev_1_slice_1d, one_1d, zero_1d)); + context.mark_node(std::make_shared(qkv_weight, zero_1d, ev_1_slice_1d, one_1d, zero_1d)); const auto key_proj_weight = - context.mark_node(std::make_shared(qkv_weight, ev_1_slice_1d, ev_2_slice_1d, one_1d, zero_1d)); + context.mark_node(std::make_shared(qkv_weight, ev_1_slice_1d, ev_2_slice_1d, one_1d, zero_1d)); const auto value_proj_weight = - context.mark_node(std::make_shared(qkv_weight, ev_2_slice_1d, ev_3_slice_1d, one_1d, zero_1d)); + context.mark_node(std::make_shared(qkv_weight, ev_2_slice_1d, ev_3_slice_1d, one_1d, zero_1d)); const auto query_proj_bias = - context.mark_node(std::make_shared(qkv_bias, zero_1d, ev_1_slice_1d, one_1d, zero_1d)); + context.mark_node(std::make_shared(qkv_bias, zero_1d, ev_1_slice_1d, one_1d, zero_1d)); const auto key_proj_bias = - context.mark_node(std::make_shared(qkv_bias, ev_1_slice_1d, ev_2_slice_1d, one_1d, zero_1d)); + context.mark_node(std::make_shared(qkv_bias, ev_1_slice_1d, ev_2_slice_1d, one_1d, zero_1d)); const auto value_proj_bias = - context.mark_node(std::make_shared(qkv_bias, ev_2_slice_1d, ev_3_slice_1d, one_1d, zero_1d)); - - const auto query_weighted = - context.mark_node(std::make_shared(query, query_proj_weight, false, true)); - const auto key_weighted = context.mark_node(std::make_shared(key, key_proj_weight, false, true)); - const auto value_weighted = - context.mark_node(std::make_shared(value, value_proj_weight, false, true)); - - const auto query_biased = context.mark_node(std::make_shared(query_weighted, query_proj_bias)); - const auto key_biased = context.mark_node(std::make_shared(key_weighted, key_proj_bias)); - const auto value_biased = context.mark_node(std::make_shared(value_weighted, value_proj_bias)); - - const auto qkv_reshape_dims = context.mark_node( - std::make_shared(OutputVector{batch_size, seq_size, heads_1d, neg_one_1d}, 0)); - const auto qv_transpose_dims = context.mark_node(opset10::Constant::create(element::i64, Shape{4}, {0, 2, 1, 3})); - const auto k_transpose_dims = context.mark_node(opset10::Constant::create(element::i64, Shape{4}, {0, 2, 3, 1})); - - const auto query_reshaped = - context.mark_node(std::make_shared(query_biased, qkv_reshape_dims, false)); - const auto key_reshaped = - context.mark_node(std::make_shared(key_biased, qkv_reshape_dims, false)); - const auto value_reshaped = - context.mark_node(std::make_shared(value_biased, qkv_reshape_dims, false)); - - const auto query_transposed = - context.mark_node(std::make_shared(query_reshaped, qv_transpose_dims)); - const auto key_transposed = context.mark_node(std::make_shared(key_reshaped, k_transpose_dims)); - const auto value_transposed = - context.mark_node(std::make_shared(value_reshaped, qv_transpose_dims)); - - const auto scale_one = context.mark_node(std::make_shared(one_1d, query_transposed)); - const auto scale_dim = context.mark_node(std::make_shared(embed_div_heads, query_transposed)); - const auto scale_dim_sqrt = context.mark_node(std::make_shared(scale_dim)); - const auto scale = context.mark_node(std::make_shared(scale_one, scale_dim_sqrt)); + context.mark_node(std::make_shared(qkv_bias, ev_2_slice_1d, ev_3_slice_1d, one_1d, zero_1d)); + + const auto query_weighted = context.mark_node(std::make_shared(query, query_proj_weight, false, true)); + const auto key_weighted = context.mark_node(std::make_shared(key, key_proj_weight, false, true)); + const auto value_weighted = context.mark_node(std::make_shared(value, value_proj_weight, false, true)); + + const auto query_biased = context.mark_node(std::make_shared(query_weighted, query_proj_bias)); + const auto key_biased = context.mark_node(std::make_shared(key_weighted, key_proj_bias)); + const auto value_biased = context.mark_node(std::make_shared(value_weighted, value_proj_bias)); + + const auto qkv_reshape_dims = + context.mark_node(std::make_shared(OutputVector{batch_size, seq_size, heads_1d, neg_one_1d}, 0)); + const auto qv_transpose_dims = context.mark_node(v0::Constant::create(element::i64, Shape{4}, {0, 2, 1, 3})); + const auto k_transpose_dims = context.mark_node(v0::Constant::create(element::i64, Shape{4}, {0, 2, 3, 1})); + + const auto query_reshaped = context.mark_node(std::make_shared(query_biased, qkv_reshape_dims, false)); + const auto key_reshaped = context.mark_node(std::make_shared(key_biased, qkv_reshape_dims, false)); + const auto value_reshaped = context.mark_node(std::make_shared(value_biased, qkv_reshape_dims, false)); + + const auto query_transposed = context.mark_node(std::make_shared(query_reshaped, qv_transpose_dims)); + const auto key_transposed = context.mark_node(std::make_shared(key_reshaped, k_transpose_dims)); + const auto value_transposed = context.mark_node(std::make_shared(value_reshaped, qv_transpose_dims)); + + const auto scale_one = context.mark_node(std::make_shared(one_1d, query_transposed)); + const auto scale_dim = context.mark_node(std::make_shared(embed_div_heads, query_transposed)); + const auto scale_dim_sqrt = context.mark_node(std::make_shared(scale_dim)); + const auto scale = context.mark_node(std::make_shared(scale_one, scale_dim_sqrt)); const auto query_key_transpose_dot_product = - context.mark_node(std::make_shared(query_transposed, key_transposed)); + context.mark_node(std::make_shared(query_transposed, key_transposed)); - auto scaled_dot_product = - context.mark_node(std::make_shared(query_key_transpose_dot_product, scale)); + auto scaled_dot_product = context.mark_node(std::make_shared(query_key_transpose_dot_product, scale)); // Mask handling if (!context.input_is_none(9) && !context.input_is_none(12)) { @@ -125,61 +133,58 @@ OutputVector translate_native_multi_head_attention(const NodeContext& context) { // non-boolean to boolean masks if (atten_mask.get_element_type() == element::boolean) { const auto minus_inf_conv = - context.mark_node(std::make_shared(minus_inf, scaled_dot_product)); - const auto mask_inverse = context.mark_node(std::make_shared(atten_mask)); - atten_mask = context.mark_node(std::make_shared(atten_mask, scaled_dot_product)); - atten_mask = context.mark_node(std::make_shared(mask_inverse, atten_mask, minus_inf_conv)); + context.mark_node(std::make_shared(minus_inf, scaled_dot_product)); + const auto mask_inverse = context.mark_node(std::make_shared(atten_mask)); + atten_mask = context.mark_node(std::make_shared(atten_mask, scaled_dot_product)); + atten_mask = context.mark_node(std::make_shared(mask_inverse, atten_mask, minus_inf_conv)); } else { // Once int/float mask type is supported in PyTorch, // remove this assert to allow for such masks in OV PYTORCH_OP_CONVERSION_CHECK(1, "Non-boolean masks are not supported."); - atten_mask = context.mark_node(std::make_shared(atten_mask, scaled_dot_product)); + atten_mask = context.mark_node(std::make_shared(atten_mask, scaled_dot_product)); } // If mask type is 1 (only key-padding) then mask's shape is (N, S). // It must be reshaped to (N, 1, 1, S) to properly broadcast it proper dims in the next step if (context.const_input(12) == 1) { - const auto target_mask_reshape = context.mark_node( - std::make_shared(OutputVector{batch_size, one_1d, one_1d, seq_size}, 0)); - atten_mask = context.mark_node(std::make_shared(atten_mask, target_mask_reshape, false)); + const auto target_mask_reshape = + context.mark_node(std::make_shared(OutputVector{batch_size, one_1d, one_1d, seq_size}, 0)); + atten_mask = context.mark_node(std::make_shared(atten_mask, target_mask_reshape, false)); } // All mask types should be broadcast to this shape, // Except for type 2 which already has this shape if (context.const_input(12) != 2) { const auto target_mask_shape = context.mark_node( - std::make_shared(OutputVector{batch_size, heads_1d, seq_size, seq_size}, 0)); - atten_mask = context.mark_node(std::make_shared(atten_mask, target_mask_shape)); + std::make_shared(OutputVector{batch_size, heads_1d, seq_size, seq_size}, 0)); + atten_mask = context.mark_node(std::make_shared(atten_mask, target_mask_shape)); } - scaled_dot_product = context.mark_node(std::make_shared(scaled_dot_product, atten_mask)); + scaled_dot_product = context.mark_node(std::make_shared(scaled_dot_product, atten_mask)); } - const auto scaled_dot_product_softmax = - context.mark_node(std::make_shared(scaled_dot_product, -1)); + const auto scaled_dot_product_softmax = context.mark_node(std::make_shared(scaled_dot_product, -1)); const auto scaled_dot_product_attention = - context.mark_node(std::make_shared(scaled_dot_product_softmax, value_transposed)); + context.mark_node(std::make_shared(scaled_dot_product_softmax, value_transposed)); const auto sdp_reshape_dims = - context.mark_node(std::make_shared(OutputVector{batch_size, seq_size, neg_one_1d}, 0)); + context.mark_node(std::make_shared(OutputVector{batch_size, seq_size, neg_one_1d}, 0)); // Undo transpose (transpose back to original qv shape) const auto scaled_dot_product_attention_transposed = - context.mark_node(std::make_shared(scaled_dot_product_attention, qv_transpose_dims)); + context.mark_node(std::make_shared(scaled_dot_product_attention, qv_transpose_dims)); const auto scaled_dot_product_attention_reshaped = context.mark_node( - std::make_shared(scaled_dot_product_attention_transposed, sdp_reshape_dims, false)); + std::make_shared(scaled_dot_product_attention_transposed, sdp_reshape_dims, false)); const auto scaled_dot_product_attention_weighted = context.mark_node( - std::make_shared(scaled_dot_product_attention_reshaped, proj_weight, false, true)); + std::make_shared(scaled_dot_product_attention_reshaped, proj_weight, false, true)); const auto scaled_dot_product_attention_biased = - context.mark_node(std::make_shared(scaled_dot_product_attention_weighted, proj_bias)); + context.mark_node(std::make_shared(scaled_dot_product_attention_weighted, proj_bias)); if (average_weights) { - const auto target_div_shape = context.mark_node(std::make_shared(scaled_dot_product)); - const auto heads_div = context.mark_node(std::make_shared(heads_1d, target_div_shape)); - const auto heads_div_conv = - context.mark_node(std::make_shared(heads_div, scaled_dot_product)); - scaled_dot_product = - context.mark_node(std::make_shared(scaled_dot_product, heads_div_conv, false)); - scaled_dot_product = context.mark_node(std::make_shared(scaled_dot_product, one_1d)); + const auto target_div_shape = context.mark_node(std::make_shared(scaled_dot_product)); + const auto heads_div = context.mark_node(std::make_shared(heads_1d, target_div_shape)); + const auto heads_div_conv = context.mark_node(std::make_shared(heads_div, scaled_dot_product)); + scaled_dot_product = context.mark_node(std::make_shared(scaled_dot_product, heads_div_conv, false)); + scaled_dot_product = context.mark_node(std::make_shared(scaled_dot_product, one_1d)); } if (need_weights) { diff --git a/src/frontends/pytorch/src/transforms/prim_list_unpack_replacer.cpp b/src/frontends/pytorch/src/transforms/prim_list_unpack_replacer.cpp index 27d1245d475c56..5560c9ff225e9d 100644 --- a/src/frontends/pytorch/src/transforms/prim_list_unpack_replacer.cpp +++ b/src/frontends/pytorch/src/transforms/prim_list_unpack_replacer.cpp @@ -8,8 +8,25 @@ #include #include "openvino/core/rt_info.hpp" +#include "openvino/op/add.hpp" +#include "openvino/op/broadcast.hpp" +#include "openvino/op/concat.hpp" +#include "openvino/op/divide.hpp" +#include "openvino/op/gather.hpp" +#include "openvino/op/greater.hpp" +#include "openvino/op/mod.hpp" +#include "openvino/op/non_zero.hpp" +#include "openvino/op/range.hpp" +#include "openvino/op/reshape.hpp" +#include "openvino/op/shape_of.hpp" +#include "openvino/op/slice.hpp" +#include "openvino/op/split.hpp" +#include "openvino/op/squeeze.hpp" +#include "openvino/op/subtract.hpp" +#include "openvino/op/tile.hpp" +#include "openvino/op/unsqueeze.hpp" #include "openvino/op/util/framework_node.hpp" -#include "openvino/opsets/opset10.hpp" +#include "openvino/op/variadic_split.hpp" #include "openvino/pass/pattern/matcher.hpp" #include "openvino/pass/pattern/op/wrap_type.hpp" #include "utils.hpp" @@ -19,6 +36,8 @@ namespace frontend { namespace pytorch { namespace pass { +using namespace ov::op; + PrimListUnpackReplacer::PrimListUnpackReplacer() { auto list_unpack = ov::pass::pattern::wrap_type(); @@ -41,213 +60,195 @@ PrimListUnpackReplacer::PrimListUnpackReplacer() { // allow for last chunk to be smaller if data is not equally divisible. auto split_size = torch_split->get_input_source_output(1); // Using number of ListUnpack outputs. - auto num_out_m_1 = opset10::Constant::create(split_size.get_element_type(), - Shape{1}, - {list_unpack->get_output_size() - 1}); - auto const_neg_1 = opset10::Constant::create(split_size.get_element_type(), Shape{1}, {-1}); - auto split_lenghts_m_1 = rg.make(split_size, num_out_m_1); + auto num_out_m_1 = + v0::Constant::create(split_size.get_element_type(), Shape{1}, {list_unpack->get_output_size() - 1}); + auto const_neg_1 = v0::Constant::create(split_size.get_element_type(), Shape{1}, {-1}); + auto split_lenghts_m_1 = rg.make(split_size, num_out_m_1); NodeVector concat_inputs{split_lenghts_m_1, const_neg_1}; - auto split_lenghts = rg.make(concat_inputs, 0); - split = rg.make(torch_split->get_input_source_output(0), - torch_split->get_input_source_output(2), - split_lenghts); + auto split_lenghts = rg.make(concat_inputs, 0); + split = rg.make(torch_split->get_input_source_output(0), + torch_split->get_input_source_output(2), + split_lenghts); } else { - split = rg.make(torch_split->get_input_source_output(0), - torch_split->get_input_source_output(2), - torch_split->get_input_source_output(1)); + split = rg.make(torch_split->get_input_source_output(0), + torch_split->get_input_source_output(2), + torch_split->get_input_source_output(1)); } copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, split); return true; - } - - if (auto split_with_sizes = cast_fw_node(input_node, "aten::split_with_sizes")) { + } else if (auto split_with_sizes = cast_fw_node(input_node, "aten::split_with_sizes")) { auto split_lengths = concat_list_construct(split_with_sizes->get_input_source_output(1)); - auto split = rg.make(split_with_sizes->get_input_source_output(0), - split_with_sizes->get_input_source_output(2), - split_lengths); + auto split = rg.make(split_with_sizes->get_input_source_output(0), + split_with_sizes->get_input_source_output(2), + split_lengths); copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, split); return true; - } - - if (auto chunk = cast_fw_node(input_node, "aten::chunk")) { + } else if (auto chunk = cast_fw_node(input_node, "aten::chunk")) { if (list_unpack->get_output_size() == 1) { list_unpack->output(0).replace(input_node->input_value(0)); return true; } auto input_tensor = chunk->get_input_source_output(0); auto chunks = chunk->get_input_source_output(1); - chunks = rg.make(chunks, element::i32); + chunks = rg.make(chunks, element::i32); auto dim = chunk->get_input_source_output(2); - auto tensor_0 = opset10::Constant::create(element::i32, Shape{1}, {0}); - auto tensor_neg_1 = opset10::Constant::create(element::i32, Shape{1}, {-1}); + auto tensor_0 = v0::Constant::create(element::i32, Shape{1}, {0}); + auto tensor_neg_1 = v0::Constant::create(element::i32, Shape{1}, {-1}); - auto input_shape = rg.make(input_tensor, element::i32); - auto input_dimension = rg.make(input_shape, dim, tensor_0); + auto input_shape = rg.make(input_tensor, element::i32); + auto input_dimension = rg.make(input_shape, dim, tensor_0); - auto init_chunk_size = rg.make(input_dimension, chunks, true); + auto init_chunk_size = rg.make(input_dimension, chunks, true); // Add 1 if input is not evenly divisible by chunks - auto last_chunk_size = rg.make(input_dimension, chunks); - auto is_last_nonzero = rg.make(last_chunk_size, tensor_0); - auto is_last_nonzero_int = rg.make(is_last_nonzero, element::i32); + auto last_chunk_size = rg.make(input_dimension, chunks); + auto is_last_nonzero = rg.make(last_chunk_size, tensor_0); + auto is_last_nonzero_int = rg.make(is_last_nonzero, element::i32); - auto chunk_size = rg.make(init_chunk_size, is_last_nonzero_int); + auto chunk_size = rg.make(init_chunk_size, is_last_nonzero_int); auto split_lengths_even_size = - opset10::Constant::create(element::i32, Shape{1}, {list_unpack->get_output_size() - 1}); - auto split_lengths_even = rg.make(chunk_size, split_lengths_even_size); + v0::Constant::create(element::i32, Shape{1}, {list_unpack->get_output_size() - 1}); + auto split_lengths_even = rg.make(chunk_size, split_lengths_even_size); - auto split_lengths = rg.make(OutputVector{split_lengths_even, tensor_neg_1}, 0); - auto sliced_chunks = rg.make(input_tensor, dim, split_lengths); + auto split_lengths = rg.make(OutputVector{split_lengths_even, tensor_neg_1}, 0); + auto sliced_chunks = rg.make(input_tensor, dim, split_lengths); copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, sliced_chunks); return true; - } - - if (auto tensor_split = cast_fw_node(input_node, "aten::tensor_split")) { + } else if (auto tensor_split = cast_fw_node(input_node, "aten::tensor_split")) { auto rank = tensor_split->input(1).get_partial_shape().rank(); if (rank.is_dynamic()) { add_exception_to_fw_node(tensor_split, "aten::tensor_split: dynamic rank is not supported."); return false; } - auto const_0 = opset10::Constant::create(element::i32, Shape{1}, {0}); - auto const_1 = opset10::Constant::create(element::i32, Shape{1}, {1}); - auto const_0_scalar = opset10::Constant::create(element::i32, Shape{}, {0}); - auto const_1_scalar = opset10::Constant::create(element::i32, Shape{}, {1}); - auto const_max = opset10::Constant::create(element::i32, Shape{1}, {std::numeric_limits::max()}); - auto const_neg_1 = opset10::Constant::create(element::i32, Shape{1}, {-1}); + auto const_0 = v0::Constant::create(element::i32, Shape{1}, {0}); + auto const_1 = v0::Constant::create(element::i32, Shape{1}, {1}); + auto const_0_scalar = v0::Constant::create(element::i32, Shape{}, {0}); + auto const_1_scalar = v0::Constant::create(element::i32, Shape{}, {1}); + auto const_max = v0::Constant::create(element::i32, Shape{1}, {std::numeric_limits::max()}); + auto const_neg_1 = v0::Constant::create(element::i32, Shape{1}, {-1}); auto input = tensor_split->get_input_source_output(0); auto indices_or_sections = tensor_split->get_input_source_output(1); - indices_or_sections = std::make_shared(indices_or_sections, element::i32); - auto dim = rg.make(tensor_split->get_input_source_output(2), const_0); - auto list_num_outs = opset10::Constant::create(element::i32, Shape{1}, {list_unpack->get_output_size()}); - auto list_num_outs_scalar = - opset10::Constant::create(element::i32, Shape{}, {list_unpack->get_output_size()}); + indices_or_sections = std::make_shared(indices_or_sections, element::i32); + auto dim = rg.make(tensor_split->get_input_source_output(2), const_0); + auto list_num_outs = v0::Constant::create(element::i32, Shape{1}, {list_unpack->get_output_size()}); + auto list_num_outs_scalar = v0::Constant::create(element::i32, Shape{}, {list_unpack->get_output_size()}); if (rank.get_length() == 0) { - auto input_shape = rg.make(input, element::i32); - auto axis_size = rg.make(input_shape, dim, const_0); - auto minimum_split_size = rg.make(axis_size, indices_or_sections); - auto maximum_split_size = rg.make(minimum_split_size, const_1); - auto num_splits_with_max_size = rg.make(axis_size, indices_or_sections); - auto num_splits_with_min_size = - rg.make(indices_or_sections, num_splits_with_max_size); - auto splits_max_size = rg.make(maximum_split_size, num_splits_with_max_size); - auto splits_min_size = rg.make(minimum_split_size, num_splits_with_min_size); - - auto split_sizes = rg.make(OutputVector{splits_max_size, splits_min_size}, 0); + auto input_shape = rg.make(input, element::i32); + auto axis_size = rg.make(input_shape, dim, const_0); + auto minimum_split_size = rg.make(axis_size, indices_or_sections); + auto maximum_split_size = rg.make(minimum_split_size, const_1); + auto num_splits_with_max_size = rg.make(axis_size, indices_or_sections); + auto num_splits_with_min_size = rg.make(indices_or_sections, num_splits_with_max_size); + auto splits_max_size = rg.make(maximum_split_size, num_splits_with_max_size); + auto splits_min_size = rg.make(minimum_split_size, num_splits_with_min_size); + + auto split_sizes = rg.make(OutputVector{splits_max_size, splits_min_size}, 0); // Reshape is used to make number of outputs static. - auto split_sizes_known_length = rg.make(split_sizes, list_num_outs, false); - auto splits = rg.make(input, dim, split_sizes_known_length); + auto split_sizes_known_length = rg.make(split_sizes, list_num_outs, false); + auto splits = rg.make(input, dim, split_sizes_known_length); copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, splits->outputs()); return true; } else { - auto range = - rg.make(const_0_scalar, list_num_outs_scalar, const_1_scalar, element::i32); - auto range_plus_1 = rg.make(range, const_1); + auto range = rg.make(const_0_scalar, list_num_outs_scalar, const_1_scalar, element::i32); + auto range_plus_1 = rg.make(range, const_1); auto sections = - rg.make(OutputVector{const_0, std::move(indices_or_sections), const_max}, 0); + rg.make(OutputVector{const_0, std::move(indices_or_sections), const_max}, 0); - auto starts_tensor = rg.make(sections, const_0, const_neg_1, const_1, const_0); + auto starts_tensor = rg.make(sections, const_0, const_neg_1, const_1, const_0); auto starts = - rg.make(starts_tensor, const_0_scalar, list_unpack->get_output_size())->outputs(); - auto stops_tensor = rg.make(sections, const_1, const_max, const_1, const_0); + rg.make(starts_tensor, const_0_scalar, list_unpack->get_output_size())->outputs(); + auto stops_tensor = rg.make(sections, const_1, const_max, const_1, const_0); auto stops = - rg.make(stops_tensor, const_0_scalar, list_unpack->get_output_size())->outputs(); + rg.make(stops_tensor, const_0_scalar, list_unpack->get_output_size())->outputs(); OutputVector outputs{}; for (size_t i = 0; i < list_unpack->get_output_size(); i++) { - auto slice = rg.make(input, starts[i], stops[i], const_1, dim); + auto slice = rg.make(input, starts[i], stops[i], const_1, dim); outputs.push_back(slice); } copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, outputs); return true; } - } - - if (auto broadcast_tensors = cast_fw_node(input_node, "aten::broadcast_tensors")) { + } else if (auto broadcast_tensors = cast_fw_node(input_node, "aten::broadcast_tensors")) { auto tensors = cast_fw_node(broadcast_tensors->input_value(0).get_node_shared_ptr(), "prim::ListConstruct"); if (!tensors) { add_exception_to_fw_node(input_node, "aten::broadcast_tensors: only prim::ListConstruct supported as input."); return false; } - Output final_shape_t = opset10::Constant::create(element::i32, Shape{}, {0}); + Output final_shape_t = v0::Constant::create(element::i32, Shape{}, {0}); for (auto& input : tensors->inputs()) { - auto tensor_shape = rg.make(input.get_source_output(), element::i32); + auto tensor_shape = rg.make(input.get_source_output(), element::i32); final_shape_t = - rg.make(final_shape_t, tensor_shape, ov::op::BroadcastType::BIDIRECTIONAL); + rg.make(final_shape_t, tensor_shape, ov::op::BroadcastType::BIDIRECTIONAL); } - auto final_shape = rg.make(final_shape_t, element::i32); + auto final_shape = rg.make(final_shape_t, element::i32); OutputVector outputs; for (auto& input : tensors->inputs()) { - outputs.push_back(rg.make(input.get_source_output(), final_shape)); + outputs.push_back(rg.make(input.get_source_output(), final_shape)); } copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, outputs); return true; - } - - if (auto unbind = cast_fw_node(input_node, "aten::unbind")) { + } else if (auto unbind = cast_fw_node(input_node, "aten::unbind")) { const auto input = unbind->get_input_source_output(0); const auto axis = unbind->get_input_source_output(1); const auto num_splits = list_unpack->get_output_size(); - auto split = rg.make(input, axis, num_splits); + auto split = rg.make(input, axis, num_splits); OutputVector outputs; for (auto& output : split->outputs()) { - const auto squeeze = rg.make(output, axis); + const auto squeeze = rg.make(output, axis); outputs.push_back(squeeze); } copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, outputs); return true; - } - if (auto where = cast_fw_node(input_node, "aten::where")) { + } else if (auto where = cast_fw_node(input_node, "aten::where")) { const auto input = where->get_input_source_output(0); - auto non_zero = rg.make(input); - auto axis = opset10::Constant::create(element::i32, Shape{}, {0}); + auto non_zero = rg.make(input); + auto axis = v0::Constant::create(element::i32, Shape{}, {0}); const auto num_splits = list_unpack->get_output_size(); - auto split = rg.make(non_zero, axis, num_splits); + auto split = rg.make(non_zero, axis, num_splits); OutputVector outputs; for (auto& output : split->outputs()) { - const auto squeeze = rg.make(output, axis); + const auto squeeze = rg.make(output, axis); outputs.push_back(squeeze); } copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, outputs); return true; - } - if (auto nonzero_numpy = cast_fw_node(input_node, "aten::nonzero_numpy")) { + } else if (auto nonzero_numpy = cast_fw_node(input_node, "aten::nonzero_numpy")) { const auto input = nonzero_numpy->get_input_source_output(0); - auto non_zero = rg.make(input); - auto axis = opset10::Constant::create(element::i32, Shape{}, {0}); + auto non_zero = rg.make(input); + auto axis = v0::Constant::create(element::i32, Shape{}, {0}); const auto num_splits = list_unpack->get_output_size(); - auto split = rg.make(non_zero, axis, num_splits); + auto split = rg.make(non_zero, axis, num_splits); OutputVector outputs; for (auto& output : split->outputs()) { - const auto squeeze = rg.make(output, axis); + const auto squeeze = rg.make(output, axis); outputs.push_back(squeeze); } copy_runtime_info_and_name(list_unpack, rg.get(), {input_node}); replace_node(list_unpack, outputs); return true; - } - - if (auto meshgrid = cast_fw_node(input_node, "aten::meshgrid")) { + } else if (auto meshgrid = cast_fw_node(input_node, "aten::meshgrid")) { // Input - ListConstruct auto meshgrid_input_node = cast_fw_node(meshgrid->input_value(0).get_node_shared_ptr(), "prim::ListConstruct"); @@ -278,24 +279,24 @@ PrimListUnpackReplacer::PrimListUnpackReplacer() { } NodeVector cat_shapes{}; NodeVector reshapes{}; - auto const_neg_1 = opset10::Constant::create(element::i32, Shape{1}, {-1}); - auto const_1 = opset10::Constant::create(element::i32, Shape{1}, {1}); + auto const_neg_1 = v0::Constant::create(element::i32, Shape{1}, {-1}); + auto const_1 = v0::Constant::create(element::i32, Shape{1}, {1}); int input_idx = 0; for (auto& input : meshgrid_inputs) { - auto reshaped_input = rg.make(input, const_neg_1, false); - auto shape = rg.make(reshaped_input, element::i32); + auto reshaped_input = rg.make(input, const_neg_1, false); + auto shape = rg.make(reshaped_input, element::i32); cat_shapes.push_back(shape); NodeVector cat_inputs(meshgrid_inputs.size(), const_1); cat_inputs[input_idx] = shape; input_idx++; - auto input_cat = rg.make(cat_inputs, 0); - auto reshape_cat = rg.make(reshaped_input, input_cat, false); + auto input_cat = rg.make(cat_inputs, 0); + auto reshape_cat = rg.make(reshaped_input, input_cat, false); reshapes.push_back(reshape_cat); } - auto cat = rg.make(cat_shapes, 0); + auto cat = rg.make(cat_shapes, 0); OutputVector outputs{}; for (auto& reshape : reshapes) { - auto out = rg.make(reshape, cat, ov::op::BroadcastType::BIDIRECTIONAL); + auto out = rg.make(reshape, cat, ov::op::BroadcastType::BIDIRECTIONAL); outputs.push_back(out); } if (indexing == "xy" && outputs.size() >= 2) { @@ -304,17 +305,15 @@ PrimListUnpackReplacer::PrimListUnpackReplacer() { copy_runtime_info_and_name(list_unpack, rg.get(), {input_node, meshgrid_input_node}); replace_node(list_unpack, outputs); return true; - } - - if (auto shape_of = std::dynamic_pointer_cast(input_node)) { + } else if (auto shape_of = std::dynamic_pointer_cast(input_node)) { // case aten::size as input // Number of ListUnpack outputs should be equal to rank of input shape. - auto axis_0 = opset10::Constant::create(element::i32, Shape{}, {0}); - auto split = rg.make(shape_of, axis_0, list_unpack->get_output_size()); + auto axis_0 = v0::Constant::create(element::i32, Shape{}, {0}); + auto split = rg.make(shape_of, axis_0, list_unpack->get_output_size()); OutputVector res; for (auto& output : split->outputs()) { - auto squeeze = rg.make(output, axis_0); + auto squeeze = rg.make(output, axis_0); res.push_back(squeeze); } @@ -322,17 +321,15 @@ PrimListUnpackReplacer::PrimListUnpackReplacer() { replace_node(list_unpack, res); return true; - } - - if (auto slice = std::dynamic_pointer_cast(input_node)) { + } else if (auto slice = std::dynamic_pointer_cast(input_node)) { // case aten::slice as input // Number of ListUnpack outputs should be equal to rank of input shape. - auto axis_0 = opset10::Constant::create(element::i32, Shape{}, {0}); - auto split = rg.make(slice, axis_0, list_unpack->get_output_size()); + auto axis_0 = v0::Constant::create(element::i32, Shape{}, {0}); + auto split = rg.make(slice, axis_0, list_unpack->get_output_size()); OutputVector res; for (auto& output : split->outputs()) { - auto squeeze = rg.make(output, axis_0); + auto squeeze = rg.make(output, axis_0); res.push_back(squeeze); } diff --git a/src/frontends/pytorch/src/utils.cpp b/src/frontends/pytorch/src/utils.cpp index b096da45f25437..df35f11fee1d75 100644 --- a/src/frontends/pytorch/src/utils.cpp +++ b/src/frontends/pytorch/src/utils.cpp @@ -7,8 +7,22 @@ #include "op_table.hpp" #include "openvino/core/rt_info.hpp" #include "openvino/frontend/pytorch/decoder.hpp" +#include "openvino/op/add.hpp" +#include "openvino/op/broadcast.hpp" +#include "openvino/op/concat.hpp" #include "openvino/op/convert_promote_types.hpp" -#include "openvino/opsets/opset10.hpp" +#include "openvino/op/divide.hpp" +#include "openvino/op/gather.hpp" +#include "openvino/op/mod.hpp" +#include "openvino/op/range.hpp" +#include "openvino/op/reduce_prod.hpp" +#include "openvino/op/reshape.hpp" +#include "openvino/op/select.hpp" +#include "openvino/op/shape_of.hpp" +#include "openvino/op/slice.hpp" +#include "openvino/op/squeeze.hpp" +#include "openvino/op/subtract.hpp" +#include "openvino/op/unsqueeze.hpp" #include "openvino/util/log.hpp" #include "pt_framework_node.hpp" #include "translate_session.hpp" @@ -17,6 +31,8 @@ namespace ov { namespace frontend { namespace pytorch { +using namespace ov::op; + void num_inputs_check(const NodeContext& context, size_t min_inputs, size_t max_inputs) { auto num_inputs = context.get_input_size(); FRONT_END_OP_CONVERSION_CHECK(num_inputs >= min_inputs, "Got less inputs than expected"); @@ -38,12 +54,12 @@ Output make_optional_bias(const Output& base_op, if (!context.input_is_none(bias_input_idx)) { auto bias = context.get_input(bias_input_idx); if (!unsqueeze_dims.empty()) { - auto indices = opset10::Constant::create(element::i32, {unsqueeze_dims.size()}, unsqueeze_dims); + auto indices = v0::Constant::create(element::i32, {unsqueeze_dims.size()}, unsqueeze_dims); context.mark_node(indices); - bias = make_shared(bias, indices); + bias = make_shared(bias, indices); context.mark_output(bias); } - return make_shared(context.mark_output(base_op), bias); + return make_shared(context.mark_output(base_op), bias); } else { return base_op; } @@ -52,28 +68,28 @@ Output make_optional_bias(const Output& base_op, Output reshape_channelwise(const NodeContext& context, const Output& data, const Output& shape_source) { - auto input_shape = context.mark_node(std::make_shared(shape_source, element::i32)); - auto input_rank = context.mark_node(std::make_shared(input_shape, element::i32)); - auto one_const = context.mark_node(opset10::Constant::create(element::i32, Shape{1}, {1})); - auto two_const = context.mark_node(opset10::Constant::create(element::i32, Shape{1}, {2})); - auto tail_shape_rank = context.mark_node(std::make_shared(input_rank, two_const)); - auto tail_shape = context.mark_node(std::make_shared(one_const, tail_shape_rank)); - auto channels_dim = context.mark_node(std::make_shared(data, element::i32)); + auto input_shape = context.mark_node(std::make_shared(shape_source, element::i32)); + auto input_rank = context.mark_node(std::make_shared(input_shape, element::i32)); + auto one_const = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {1})); + auto two_const = context.mark_node(v0::Constant::create(element::i32, Shape{1}, {2})); + auto tail_shape_rank = context.mark_node(std::make_shared(input_rank, two_const)); + auto tail_shape = context.mark_node(std::make_shared(one_const, tail_shape_rank)); + auto channels_dim = context.mark_node(std::make_shared(data, element::i32)); auto new_shape = - context.mark_node(std::make_shared(OutputVector{one_const, channels_dim, tail_shape}, 0)); + context.mark_node(std::make_shared(OutputVector{one_const, channels_dim, tail_shape}, 0)); - return context.mark_node(std::make_shared(data, new_shape, false)); + return context.mark_node(std::make_shared(data, new_shape, false)); } std::tuple, Output> get_shape_rank(const NodeContext& context, const Output& x, bool as_scalar, element::Type output_type) { - auto shape = context.mark_node(std::make_shared(x, output_type)); - Output rank = context.mark_node(std::make_shared(shape, output_type)); + auto shape = context.mark_node(std::make_shared(x, output_type)); + Output rank = context.mark_node(std::make_shared(shape, output_type)); if (as_scalar) { - auto axis_0 = context.mark_node(opset10::Constant::create(output_type, Shape{}, {0})); - rank = context.mark_node(std::make_shared(rank, axis_0)); + auto axis_0 = context.mark_node(v0::Constant::create(output_type, Shape{}, {0})); + rank = context.mark_node(std::make_shared(rank, axis_0)); } return std::make_tuple(shape, rank); } @@ -81,24 +97,24 @@ std::tuple, Output> get_shape_rank(const NodeContext& context Output reshape_kernel_for_group(const NodeContext& context, const Output& kernel, int64_t groups) { using std::make_shared; - auto axis_0 = opset10::Constant::create(element::i32, Shape{}, {0}); - auto groups_const = opset10::Constant::create(element::i32, Shape{1}, {groups}); - auto neg_1_const = opset10::Constant::create(element::i32, Shape{1}, {-1}); + auto axis_0 = v0::Constant::create(element::i32, Shape{}, {0}); + auto groups_const = v0::Constant::create(element::i32, Shape{1}, {groups}); + auto neg_1_const = v0::Constant::create(element::i32, Shape{1}, {-1}); - auto kernel_shape = std::make_shared(kernel, element::i32); - auto c_out_idx = opset10::Constant::create(element::i32, Shape{}, {0}); - auto kernel_shape_0 = make_shared(kernel_shape, c_out_idx, axis_0); - auto kernel_shape_0_uns = make_shared(kernel_shape_0, axis_0); - auto c_out_value = make_shared(kernel_shape_0_uns, groups_const); + auto kernel_shape = std::make_shared(kernel, element::i32); + auto c_out_idx = v0::Constant::create(element::i32, Shape{}, {0}); + auto kernel_shape_0 = make_shared(kernel_shape, c_out_idx, axis_0); + auto kernel_shape_0_uns = make_shared(kernel_shape_0, axis_0); + auto c_out_value = make_shared(kernel_shape_0_uns, groups_const); - auto start = opset10::Constant::create(element::i32, Shape{1}, {2}); - auto stop = opset10::Constant::create(element::i32, Shape{1}, {std::numeric_limits::max()}); - auto step = opset10::Constant::create(element::i32, Shape{1}, {1}); - auto remaining_shape = make_shared(kernel_shape, start, stop, step); + auto start = v0::Constant::create(element::i32, Shape{1}, {2}); + auto stop = v0::Constant::create(element::i32, Shape{1}, {std::numeric_limits::max()}); + auto step = v0::Constant::create(element::i32, Shape{1}, {1}); + auto remaining_shape = make_shared(kernel_shape, start, stop, step); auto new_kernel_shape = - make_shared(OutputVector{groups_const, c_out_value, neg_1_const, remaining_shape}, 0); - auto res = make_shared(kernel, new_kernel_shape, false); + make_shared(OutputVector{groups_const, c_out_value, neg_1_const, remaining_shape}, 0); + auto res = make_shared(kernel, new_kernel_shape, false); context.mark_nodes({axis_0, groups_const, kernel_shape, @@ -121,22 +137,22 @@ std::shared_ptr get_axes_range(const NodeContext& context, int input_id) { }; std::shared_ptr get_node_axes_range(const NodeContext& context, const Output& x) { - auto start = std::make_shared(element::i32, Shape{}, 0); - auto step = std::make_shared(element::i32, Shape{}, 1); + auto start = std::make_shared(element::i32, Shape{}, 0); + auto step = std::make_shared(element::i32, Shape{}, 1); Output reduced_rank; std::tie(std::ignore, reduced_rank) = get_shape_rank(context, x, true); - return context.mark_node(std::make_shared(start, reduced_rank, step, element::i32)); + return context.mark_node(std::make_shared(start, reduced_rank, step, element::i32)); }; Output normalize_axis(const NodeContext& context, const Output& axis, const Output& rank) { - auto axis_rank = context.mark_node(std::make_shared(axis, rank)); - return context.mark_node(std::make_shared(axis_rank, rank)); + auto axis_rank = context.mark_node(std::make_shared(axis, rank)); + return context.mark_node(std::make_shared(axis_rank, rank)); } std::shared_ptr numel(const NodeContext& context, const Output& x, element::Type output_type) { - auto input_shape = context.mark_node(std::make_shared(x, output_type)); - auto axes = context.mark_node(opset10::Constant::create(output_type, Shape({1}), {0})); - return context.mark_node(std::make_shared(input_shape, axes, false)); + auto input_shape = context.mark_node(std::make_shared(x, output_type)); + auto axes = context.mark_node(v0::Constant::create(output_type, Shape({1}), {0})); + return context.mark_node(std::make_shared(input_shape, axes, false)); }; namespace { @@ -166,14 +182,14 @@ element::Type convert_dtype(int64_t pt_type) { }; Output apply_dtype(const NodeContext& context, size_t dtype_port, const Output& input_tensor) { - if (std::dynamic_pointer_cast( + if (std::dynamic_pointer_cast( context.get_input_from_visible_context(dtype_port).get_node_shared_ptr())) { auto dtype = convert_dtype(context.const_input(dtype_port)); - return context.mark_node(std::make_shared(input_tensor, dtype)); + return context.mark_node(std::make_shared(input_tensor, dtype)); } else if (const auto& fw_node = cast_fw_node(context.get_input(static_cast(dtype_port)).get_node_shared_ptr(), "prim::dtype")) { auto out_tensor = fw_node->input_value(0); - return context.mark_node(std::make_shared(input_tensor, out_tensor)); + return context.mark_node(std::make_shared(input_tensor, out_tensor)); } else { FRONT_END_OP_CONVERSION_CHECK(false, "Couldn't get dtype input"); } @@ -189,13 +205,13 @@ Output concat_list_construct(const Output& input) { if (auto list_construct = cast_fw_node(input.get_node_shared_ptr(), "prim::ListConstruct")) { auto list_inputs = list_construct->input_values(); OutputVector node_vector; - auto zero = opset10::Constant::create(element::i32, Shape{}, {0}); + auto zero = v0::Constant::create(element::i32, Shape{}, {0}); for (size_t i = 0; i < list_inputs.size(); i++) { auto node = concat_list_construct(list_inputs[i]); - auto unsqueezed_node = std::make_shared(node, zero); + auto unsqueezed_node = std::make_shared(node, zero); node_vector.push_back(unsqueezed_node); } - return std::make_shared(node_vector, 0); + return std::make_shared(node_vector, 0); } return input; } @@ -409,11 +425,11 @@ void align_eltwise_input_types(const NodeContext& context, // If only one input is PyScalar, replace it with const to mitigate issues with dynamic type caused by dynamic // shape. if (is_lhs_python_scalar && !is_rhs_python_scalar) { - tmp_lhs = context.mark_node(std::make_shared(const_0, lhs)); - tmp_rhs = context.mark_node(std::make_shared(const_1, rhs)); + tmp_lhs = context.mark_node(std::make_shared(const_0, lhs)); + tmp_rhs = context.mark_node(std::make_shared(const_1, rhs)); } else if (!is_lhs_python_scalar && is_rhs_python_scalar) { - tmp_lhs = context.mark_node(std::make_shared(const_1, lhs)); - tmp_rhs = context.mark_node(std::make_shared(const_0, rhs)); + tmp_lhs = context.mark_node(std::make_shared(const_1, lhs)); + tmp_rhs = context.mark_node(std::make_shared(const_0, rhs)); } auto at = context.mark_node( @@ -422,15 +438,15 @@ void align_eltwise_input_types(const NodeContext& context, if (dst_type.is_dynamic()) { // Add ConvertLike on original node to not remove changes to shape done to differentiate between tensors and // scalars. - lhs = context.mark_node(std::make_shared(lhs, at->output(0))); - rhs = context.mark_node(std::make_shared(rhs, at->output(1))); + lhs = context.mark_node(std::make_shared(lhs, at->output(0))); + rhs = context.mark_node(std::make_shared(rhs, at->output(1))); } else { // Cast to destination type if (dst_type != lhs_type) { - lhs = context.mark_node(std::make_shared(lhs, dst_type)); + lhs = context.mark_node(std::make_shared(lhs, dst_type)); } if (dst_type != rhs_type) { - rhs = context.mark_node(std::make_shared(rhs, dst_type)); + rhs = context.mark_node(std::make_shared(rhs, dst_type)); } } return; @@ -442,7 +458,7 @@ void align_output_types(const NodeContext& context, OutputVector& outputs) { if (dtype_any.is()) { auto dtype = dtype_any.as(); if (dtype.is_static() && dtype != outputs[i].get_element_type()) { - outputs[i] = std::make_shared(outputs[i], dtype); + outputs[i] = std::make_shared(outputs[i], dtype); } } } @@ -547,9 +563,9 @@ Output masked_fill(ov::pass::NodeRegistry& rg, const Output& data, const Output& mask, const Output& value) { - auto _value = rg.make(value, data); - auto bool_mask = rg.make(mask, element::boolean); - return rg.make(bool_mask, _value, data); + auto _value = rg.make(value, data); + auto bool_mask = rg.make(mask, element::boolean); + return rg.make(bool_mask, _value, data); } Output concat_list_from_inputs(const NodeContext& context, size_t begin, size_t end) { From c85902d9b5b2f73b44c08b14e2133c201b7a91cc Mon Sep 17 00:00:00 2001 From: Egor Duplenskii Date: Thu, 22 Aug 2024 23:47:20 +0200 Subject: [PATCH 26/35] [INSTALL] Fix setupvars.sh corrupted installation directory (#26166) Both 'cd' and 'pwd -P' print the directory name Installation directory should be printed only once --- scripts/setupvars/setupvars.sh | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/scripts/setupvars/setupvars.sh b/scripts/setupvars/setupvars.sh index 54eefc25714400..dd67bf330630c1 100755 --- a/scripts/setupvars/setupvars.sh +++ b/scripts/setupvars/setupvars.sh @@ -6,7 +6,7 @@ abs_path () { script_path=$(eval echo "$1") directory=$(dirname "$script_path") - builtin cd "$directory" || exit + builtin cd "$directory" >/dev/null 2>&1 || exit pwd -P } From eb16f7fbd0d450d51894e07ef1888c71505b4b0a Mon Sep 17 00:00:00 2001 From: Taylor Yeonbok Lee Date: Thu, 22 Aug 2024 14:47:54 -0700 Subject: [PATCH 27/35] [GPU] Optimize fc_bf_tiled kernel for large K + small N case (#26054) ### Details: - Optimize fc_bf_tiled kernel for large K + small N case by setting K_TILE_SIZE 4 - Perf gain on MTL (U7 155H + 32GB RAM + driver 31.0.101.5333) ![image](https://github.com/user-attachments/assets/2e6537fe-90d4-47e1-8d56-f816d3471559) (No regression and on par on llama3 INT4 default and phi-3 mini INT4 default) ### Tickets: - 149212 --- .../fully_connected_gpu_bf_tiled.cl | 23 ++++++-- .../include/batch_headers/fetch_weights.cl | 10 ++++ .../include/batch_headers/int4_utils.cl | 13 +++++ .../cl_kernels/reorder_weights_int4.cl | 24 ++++++++ .../fully_connected_kernel_bf_tiled.cpp | 55 ++++++++++++++++--- .../kernels/reorder/reorder_weights_int4.cpp | 5 ++ 6 files changed, 115 insertions(+), 15 deletions(-) diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl index 0366f602d69982..92be3f31f97f3f 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl +++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/fully_connected_gpu_bf_tiled.cl @@ -108,7 +108,7 @@ KERNEL(quantize_input)( #define OUTPUT_BLOCK_WRITE(ptr, offset, val) BLOCK_WRITEN(OUTPUT_TYPE, TILE_OFM, ptr, offset, val) #define SLM_FILTER_VEC MAKE_VECTOR_TYPE(ACCUMULATOR_TYPE, TILE_OFM) -#define SLM_FILTER_PACKED_VEC MAKE_VECTOR_TYPE(FILTER_TYPE, FILTER_LOAD_BLOCK_SIZE) +#define SLM_FILTER_PACKED_VEC MAKE_VECTOR_TYPE(FILTER_TYPE, FILTER_ACTUAL_LOAD_BLOCK_SIZE) #define SLM_FILTER_UNPACKED_VEC MAKE_VECTOR_TYPE(ACCUMULATOR_TYPE, FILTER_ELEMENTS_PER_LOAD) @@ -311,6 +311,9 @@ inline void FUNC(fc_bf_tiled_kernel_default)( #if TILE_OFM != 2 #error "FC bf_tiled kernel: can't use SLM optimization with TILE_OFM != 2" #endif + #if FILTER_LAYOUT_OS_IYX_OSV16 && TILE_K != 4 + #error "FC bf_tiled kernel: can't use SLM optimization with TILE_K != 2 && OS_IYX_OSV16 layout" + #endif // Skip first barrier synchronization if there is only single outer loop iteration. #if MAIN_LOOP_ELEMENTS_COUNT / (TILE_IFM * SIMD) > 1 @@ -319,12 +322,19 @@ inline void FUNC(fc_bf_tiled_kernel_default)( __local SLM_FILTER_VEC* slm_wei_vec = (__local SLM_FILTER_VEC*)wei_local_mem; - uint weights_idx = weights_offset + local_id * SIMD * FILTER_LOAD_ITERS * FILTER_LOAD_BLOCK_SIZE; + uint weights_idx = weights_offset + local_id * SIMD * FILTER_LOAD_ITERS * FILTER_ACTUAL_LOAD_BLOCK_SIZE; uint wei_local_idx = local_id * SIMD * FILTER_LOAD_ITERS * FILTER_LOAD_BLOCK_SIZE + sglid; - unroll_for(uint load_iter = 0; load_iter < FILTER_LOAD_ITERS; ++load_iter) { - SLM_FILTER_PACKED_VEC wei_packed = BLOCK_READN(FILTER_TYPE, FILTER_LOAD_BLOCK_SIZE, weights, weights_idx); + #if FILTER_LAYOUT_OS_IYX_OSV16 + SLM_FILTER_PACKED_VEC wei_packed0 = BLOCK_READN(FILTER_TYPE, FILTER_ACTUAL_LOAD_BLOCK_SIZE, weights, weights_idx); + SLM_FILTER_PACKED_VEC wei_packed1 = BLOCK_READN(FILTER_TYPE, FILTER_ACTUAL_LOAD_BLOCK_SIZE, weights, (weights_idx + ((IFM_SIZE / 2) * 16))); + SLM_FILTER_UNPACKED_VEC wei_unpacked; + wei_unpacked.s0123 = UNPACK_INT4(ACCUMULATOR_TYPE, *((INT4_PACKED_TYPE_PRELOAD*)&wei_packed0)); + wei_unpacked.s4567 = UNPACK_INT4(ACCUMULATOR_TYPE, *((INT4_PACKED_TYPE_PRELOAD*)&wei_packed1)); + #else + SLM_FILTER_PACKED_VEC wei_packed = BLOCK_READN(FILTER_TYPE, FILTER_LOAD_BLOCK_SIZE/*4*/, weights, weights_idx); SLM_FILTER_UNPACKED_VEC wei_unpacked = UNPACK_INT4(ACCUMULATOR_TYPE, *((INT4_PACKED_TYPE_PRELOAD*)&wei_packed)); + #endif ACCUMULATOR_TYPE* w = (ACCUMULATOR_TYPE*)(&wei_unpacked); unroll_for(uint fi = 0; fi < TILE_OFM; ++fi) { unroll_for(uint kii = 0; kii < FILTER_LOAD_BLOCK_SIZE; ++kii) { @@ -383,8 +393,7 @@ inline void FUNC(fc_bf_tiled_kernel_default)( #endif #undef STORE_TO_SLM - - weights_idx += SIMD * FILTER_LOAD_BLOCK_SIZE; + weights_idx += SIMD * FILTER_ACTUAL_LOAD_BLOCK_SIZE; } wei_local_idx = sglid; @@ -478,6 +487,8 @@ inline void FUNC(fc_bf_tiled_kernel_default)( } #if TILE_OFM == 1 && FILTER_LAYOUT_OS_IS_YX_OSV32_ISV2 weights_offset += TILE_K_OFM_PACKED * 2 * SIMD; + #elif FILTER_LAYOUT_OS_IYX_OSV16 && TILE_OFM == 2 && USE_SLM == 1 + weights_offset += TILE_K_OFM_PACKED / 2 * SIMD; #else weights_offset += TILE_K_OFM_PACKED * SIMD; #endif diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/fetch_weights.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/fetch_weights.cl index 531bd0838a3aa0..7135673c8c5dc8 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/fetch_weights.cl +++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/fetch_weights.cl @@ -405,6 +405,16 @@ inline uint get_os_zyxi_osv16_index(uint o, uint i, uint z, uint y, uint x, uint ((o) / (sub_group_size))*CAT(prefix, _OFM_PITCH) \ ) +#define GET_FILTER_OS_IYX_OSV_INDEX_INT4_PACKED(prefix, o, i, y, x, sub_group_size) \ + CAT(prefix, _OFFSET) + \ + ((o) % (sub_group_size)) + \ + (sub_group_size)*( \ + (x)*CAT(prefix, _X_PITCH) + \ + (y)*CAT(prefix, _Y_PITCH) + \ + (i)*CAT(prefix, _IFM_PITCH) + \ + ((o) / (sub_group_size))*(CAT(prefix, _OFM_PITCH)/2) \ + ) + #define GET_FILTER_OS_IS_YX_OSV_ISV_INDEX_INT4_PACKED(prefix, o, i, y, x, sub_group_size) \ CAT(prefix, _OFFSET) + \ ((o) % (sub_group_size)) + \ diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/int4_utils.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/int4_utils.cl index fb257169a8b78c..d919d1ce1104ab 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/int4_utils.cl +++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/include/batch_headers/int4_utils.cl @@ -143,12 +143,24 @@ inline half4 unpack_to_half(uint4x4_t v) __attribute__((overloadable)) { return (half4)(f0.s0, f0.s1, f1.s0, f1.s1); } +inline half4 unpack_to_half_osv32_isv2(uint4x4_t v) __attribute__((overloadable)) { + half2 f0 = unpack_to_half(v.s0); + half2 f1 = unpack_to_half(v.s1); + return (half4)(f0.s0, f0.s1, f1.s0, f1.s1); +} + inline half4 unpack_to_half(int4x4_t v) __attribute__((overloadable)) { half2 f0 = unpack_to_half(v.s0); half2 f1 = unpack_to_half(v.s1); return (half4)(f0.s0, f0.s1, f1.s0, f1.s1); } +inline half4 unpack_to_half_osv32_isv2(int4x4_t v) __attribute__((overloadable)) { + half2 f0 = unpack_to_half(v.s0); + half2 f1 = unpack_to_half(v.s1); + return (half4)(f0.s0, f0.s1, f1.s0, f1.s1); +} + inline half8 unpack_to_half(uint4x8_t v) __attribute__((overloadable)) { half2 f0 = unpack_to_half(v.s0); half2 f1 = unpack_to_half(v.s1); @@ -211,4 +223,5 @@ inline uchar8 unpack_to_uchar_osv32_isv2(uint4x8_t v) __attribute__((overloadabl #define UNPACK_INT4x2(target_type, value) CAT(unpack_to_, target_type)(value) #define UNPACK_INT4x2_OSV32_ISV2(target_type, value) CAT(CAT(unpack_to_, target_type), _osv32_isv2)(value) +#define UNPACK_INT4x4_OSV32_ISV2(target_type, value) CAT(CAT(unpack_to_, target_type), _osv32_isv2)(value) #define UNPACK_TRANSPOSED_INT4x2(target_type, value) CAT(unpack_transposed_to_, target_type)(value) diff --git a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights_int4.cl b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights_int4.cl index 1b3239d082ec2a..90a7e8bd8feecc 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights_int4.cl +++ b/src/plugins/intel_gpu/src/kernel_selector/cl_kernels/reorder_weights_int4.cl @@ -28,6 +28,30 @@ KERNEL(reorder_weights_int4)(const __global INPUT0_TYPE* input, __global OUTPUT_ OUTPUT_TYPE out = in0 | (in1 << 4); output[out_byte_offset] = out; +#elif defined(OUTPUT_LAYOUT_OS_IYX_OSV16) + // osv32_isv2 layout for int4 packed weight + // f0_k0k1 | f1_k0k1 | .... | f15_k0k1 + // f0_k2k3 | f1_k2k3 | .... | f15_k2k3 + // f0_k3k4 | f1_k3k4 | .... | f15_k3k4 + // ... + // f0_k(K/2-2)k(K/2-1) | f1_k(K/2-2)k(K/2-1) | ....f15_k(K/2-2)k(K/2-1) + // ------------------------------------- + // f16_k2k3 | f17_k2k3 | ... | f31_k2k3 + // ... + const unsigned o = (uint)get_global_id(0); + const unsigned i = (uint)get_global_id(1) * 2; + + const uint input0_offset = GET_FILTER_INDEX(INPUT0, 0, o, i, 0, 0); + + INPUT0_TYPE in1 = input[input0_offset / 2] & 0xFF; + + INPUT0_TYPE packed_out_channels = in1; + + const uint output_idx = GET_FILTER_OS_IYX_OSV_INDEX_INT4_PACKED(OUTPUT, o, i/2, 0, 0, 16); // Calculate offset as osv16 due to packing + output[output_idx] = packed_out_channels; + + + #elif defined(OUTPUT_LAYOUT_OS_IYX_OSV32) // os_iyx osv32 layout for int4 packed weight // k0_f0f16 | k0_f1f17 | .... | k0_f15f31 || k1_f0f16 | k1_f1f17 | ... | k1_f15f31 diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp index 61919c10b816ff..db42bc969b4a32 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp +++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/fully_connected/fully_connected_kernel_bf_tiled.cpp @@ -97,6 +97,19 @@ static bool should_dynamic_quantize(const fully_connected_params& params) { return false; } +static bool is_weight_with_small_ofm(const fully_connected_params& params, size_t output_f) { + size_t min_num_threads = params.engineInfo.computeUnitsCount * simd; + GPU_DEBUG_TRACE_DETAIL << "out_ofm (== weight N dim) size " << output_f << " is small compared to the available threads. " + << "(computeUnitsCount : " << params.engineInfo.computeUnitsCount + << " min_num_threads : " << min_num_threads << ")" << std::endl; + GPU_DEBUG_TRACE_DETAIL << "Use ofm_tile size 1 if the batch size is 1." << std::endl; + return (output_f / 2 /*most frequently used tile_ofm*/ <= min_num_threads); +} + +static bool is_weight_with_large_ifm(const fully_connected_params& fc_params) { + return (fc_params.weights.IFM().v >= fc_params.weights.OFM().v * 3 && fc_params.weights.OFM().v <= 4096); +} + FullyConnected_bf_tiled::FullyConnected_bf_tiled() : FullyConnectedKernelBase("fully_connected_gpu_bf_tiled") { for (unsigned tile_b = 1; tile_b <= 32; ++tile_b) for (unsigned tile_ofm = 1; tile_ofm <= 4; tile_ofm *= 2) @@ -324,14 +337,18 @@ FullyConnected_bf_tiled::GetAutoTuneParams(const fully_connected_params& params, if (params.weights.GetDType() == WeightsType::UINT4 || params.weights.GetDType() == WeightsType::INT4) { if (!params.is_shape_agnostic && batch == 1) { // Tuning for Meteor Lake - size_t min_num_threads = params.engineInfo.computeUnitsCount * simd; - if (output_f / 2 <= min_num_threads && params.weights.GetLayout() == WeightsLayout::os_is_yx_osv32_isv2) { - GPU_DEBUG_TRACE_DETAIL << "FC bf tiled: Set ofm_tile 1. (output_f : " << output_f - << ", computeUnitsCount : " << params.engineInfo.computeUnitsCount - << " min_num_threads : " << min_num_threads << ")" << std::endl; - return selector.Default(tune_params(1, 1, 4, 2, 1, 1, EXE_MODE_DEFAULT)); + if (is_weight_with_small_ofm(params, output_f)) { + if (params.weights.GetLayout() == WeightsLayout::os_is_yx_osv32_isv2) { + return selector.Default(tune_params(1, 1, 4, 2, 1, 1, EXE_MODE_DEFAULT)); + } else if (params.weights.GetLayout() == WeightsLayout::os_iyx_osv16) { + return selector.Default(tune_params(1, 1, 4, 4, 1, 1, EXE_MODE_DEFAULT)); + } } else { - return selector.Default(tune_params(1, 2, 4, 2, 1, 1, EXE_MODE_DEFAULT)); + if (params.weights.GetLayout() == WeightsLayout::os_iyx_osv16) { + return selector.Default(tune_params(1, 1, 4, 4, 1, 1, EXE_MODE_DEFAULT)); + } else { + return selector.Default(tune_params(1, 2, 4, 2, 1, 1, EXE_MODE_DEFAULT)); + } } } else { // Try to use SLM kernels if possible @@ -343,7 +360,10 @@ FullyConnected_bf_tiled::GetAutoTuneParams(const fully_connected_params& params, selector.Case(tune_params(8, 2, 2, 4, 1, 1, EXE_MODE_DEFAULT, KernelType::SLM)) .Case(tune_params(8, 2, 1, 4, 1, 1, EXE_MODE_DEFAULT, KernelType::SLM)); } - return selector.Default(tune_params(8, 2, 1, 4, 1, 1, EXE_MODE_DEFAULT)); + if (params.weights.GetLayout() == WeightsLayout::os_iyx_osv16) + return selector.Default(tune_params(8, 1, 1, 4, 1, 1, EXE_MODE_DEFAULT)); + else + return selector.Default(tune_params(8, 2, 1, 4, 1, 1, EXE_MODE_DEFAULT)); } } else if (params.compressed && params.engineInfo.supports_immad) { return selector.Default(tune_params(1, 1, 1, 4, 1, 1, EXE_MODE_DEFAULT)); @@ -480,6 +500,8 @@ JitConstants FullyConnected_bf_tiled::GetJitConstants(const fully_connected_para } if (params.weights.GetLayout() == WeightsLayout::os_is_yx_osv32_isv2) jit.AddConstant(MakeJitConstant("W_IDX", "fi * TILE_K + kii")); + else if (params.weights.GetLayout() == WeightsLayout::os_iyx_osv16) + jit.AddConstant(MakeJitConstant("W_IDX", "fi * TILE_K + kii")); else jit.AddConstant(MakeJitConstant("W_IDX", "kii * TILE_OFM + fi")); @@ -512,9 +534,17 @@ JitConstants FullyConnected_bf_tiled::GetJitConstants(const fully_connected_para jit.AddConstant(MakeJitConstant("USE_SLM", 1)); jit.AddConstant(MakeJitConstant("LWS_BATCHES", lws_batches)); jit.AddConstant(MakeJitConstant("FILTER_LOAD_ITERS", weights_load_iters)); + + if (params.weights.GetLayout() == WeightsLayout::os_iyx_osv16) { + jit.AddConstant(MakeJitConstant("FILTER_ACTUAL_LOAD_BLOCK_SIZE", block_read_size / 2)); + jit.Merge(make_int4_packed_type_jit_constant("INT4_PACKED_TYPE_PRELOAD", params.weights.GetDType(), weights_elements_per_load / 2)); + } else { + jit.AddConstant(MakeJitConstant("FILTER_ACTUAL_LOAD_BLOCK_SIZE", block_read_size)); + jit.Merge(make_int4_packed_type_jit_constant("INT4_PACKED_TYPE_PRELOAD", params.weights.GetDType(), weights_elements_per_load)); + } + jit.AddConstant(MakeJitConstant("FILTER_LOAD_BLOCK_SIZE", block_read_size)); jit.AddConstant(MakeJitConstant("FILTER_ELEMENTS_PER_LOAD", weights_elements_per_load)); - jit.Merge(make_int4_packed_type_jit_constant("INT4_PACKED_TYPE_PRELOAD", params.weights.GetDType(), weights_elements_per_load)); } else { jit.AddConstant(MakeJitConstant("USE_SLM", 0)); } @@ -675,9 +705,16 @@ KernelsData FullyConnected_bf_tiled::GetTunedKernelsDataByIndex(const Params &pa return {}; tune_params tparams = GetAutoTuneParams(fc_params, KernelType::ANY, autoTuneIndex); + auto output_f = get_output_aligned_bf_size(fc_params, false).second; WeightsLayout weights_layout = WeightsLayout::os_iyx_osv16; if (fc_params.compressed && fc_params.inputs[0].GetDType() == Datatype::F16 + && (fc_params.weights.GetDType() == WeightsType::INT4 || fc_params.weights.GetDType() == WeightsType::UINT4) + && is_weight_with_small_ofm(fc_params, output_f) && is_weight_with_large_ifm(fc_params) + && (fc_params.weights.GetLayout() == WeightsLayout::oiyx || fc_params.weights.GetLayout() == WeightsLayout::os_iyx_osv16)) { + // Large K + Small N case to use [osv16 + TILE_K 4] + TILE_OFM 1 for batch 1 + weights_layout = WeightsLayout::os_iyx_osv16; + } else if (fc_params.compressed && fc_params.inputs[0].GetDType() == Datatype::F16 // ioyx => os_is_yx_osv32_isv2 is not supported yet && (fc_params.weights.GetLayout() == WeightsLayout::oiyx || fc_params.weights.GetLayout() == WeightsLayout::os_is_yx_osv32_isv2) && (fc_params.weights.GetDType() == WeightsType::INT4 || fc_params.weights.GetDType() == WeightsType::UINT4)) { diff --git a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_int4.cpp b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_int4.cpp index 0a8a2e168fe3bd..cd55dacd4c9c7a 100644 --- a/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_int4.cpp +++ b/src/plugins/intel_gpu/src/kernel_selector/kernels/reorder/reorder_weights_int4.cpp @@ -6,6 +6,7 @@ #include "kernel_selector_common.h" #include "kernel_selector_params.h" #include "kernel_selector_utils.h" +#include "common_types.h" namespace kernel_selector { @@ -17,6 +18,7 @@ ParamsKey ReorderWeightsKernelInt4::GetSupportedKey() const { k.EnableOutputWeightsType(WeightsType::INT4); k.EnableInputWeightsLayout(WeightsLayout::oiyx); k.EnableInputWeightsLayout(WeightsLayout::ioyx); + k.EnableOutputWeightsLayout(WeightsLayout::os_iyx_osv16); k.EnableOutputWeightsLayout(WeightsLayout::os_iyx_osv32); k.EnableOutputWeightsLayout(WeightsLayout::os_is_yx_osv32_isv2); k.EnableOutputWeightsLayout(WeightsLayout::oiyx); @@ -40,6 +42,8 @@ ReorderWeightsKernelInt4::DispatchData ReorderWeightsKernelInt4::SetDefault(cons dispatchData.gws = { Align(output.OFM().v, 32) / 2, output.IFM().v, 1 }; } else if (output.GetLayout() == WeightsLayout::os_is_yx_osv32_isv2) { dispatchData.gws = { Align(output.OFM().v, 32), output.IFM().v / 2, 1 }; + } else if (output.GetLayout() == WeightsLayout::os_iyx_osv16) { + dispatchData.gws = { Align(output.OFM().v, 16), output.IFM().v / 2, 1 }; } else { dispatchData.gws = { CeilDiv(output.LogicalSize(), 2), 1, 1 }; } @@ -60,6 +64,7 @@ bool ReorderWeightsKernelInt4::Validate(const Params& params) const { bool supported_case = input.GetLayout() == WeightsLayout::oiyx && output.GetLayout() == WeightsLayout::os_iyx_osv32; supported_case |= input.GetLayout() == WeightsLayout::oiyx && output.GetLayout() == WeightsLayout::os_is_yx_osv32_isv2; + supported_case |= input.GetLayout() == WeightsLayout::oiyx && output.GetLayout() == WeightsLayout::os_iyx_osv16; supported_case |= input.GetLayout() == WeightsLayout::ioyx && output.GetLayout() == WeightsLayout::oiyx; supported_case |= input.GetLayout() == WeightsLayout::ioyx && output.GetLayout() == WeightsLayout::os_iyx_osv32; return supported_case; From f6d4b4014e00c2513a1143eb3253e9890c97c6e6 Mon Sep 17 00:00:00 2001 From: "dependabot[bot]" <49699333+dependabot[bot]@users.noreply.github.com> Date: Fri, 23 Aug 2024 04:47:53 +0000 Subject: [PATCH 28/35] Bump idna from 3.4 to 3.7 in /docs (#26189) Bumps [idna](https://github.com/kjd/idna) from 3.4 to 3.7.

Release notes

Sourced from idna's releases.

v3.7

What's Changed

  • Fix issue where specially crafted inputs to encode() could take exceptionally long amount of time to process. [CVE-2024-3651]

Thanks to Guido Vranken for reporting the issue.

Full Changelog: https://github.com/kjd/idna/compare/v3.6...v3.7

Changelog

Sourced from idna's changelog.

3.7 (2024-04-11) ++++++++++++++++

  • Fix issue where specially crafted inputs to encode() could take exceptionally long amount of time to process. [CVE-2024-3651]

Thanks to Guido Vranken for reporting the issue.

3.6 (2023-11-25) ++++++++++++++++

  • Fix regression to include tests in source distribution.

3.5 (2023-11-24) ++++++++++++++++

  • Update to Unicode 15.1.0
  • String codec name is now "idna2008" as overriding the system codec "idna" was not working.
  • Fix typing error for codec encoding
  • "setup.cfg" has been added for this release due to some downstream lack of adherence to PEP 517. Should be removed in a future release so please prepare accordingly.
  • Removed reliance on a symlink for the "idna-data" tool to comport with PEP 517 and the Python Packaging User Guide for sdist archives.
  • Added security reporting protocol for project

Thanks Jon Ribbens, Diogo Teles Sant'Anna, Wu Tingfeng for contributions to this release.

Commits
  • 1d365e1 Release v3.7
  • c1b3154 Merge pull request #172 from kjd/optimize-contextj
  • 0394ec7 Merge branch 'master' into optimize-contextj
  • cd58a23 Merge pull request #152 from elliotwutingfeng/dev
  • 5beb28b More efficient resolution of joiner contexts
  • 1b12148 Update ossf/scorecard-action to v2.3.1
  • d516b87 Update Github actions/checkout to v4
  • c095c75 Merge branch 'master' into dev
  • 60a0a4c Fix typo in GitHub Actions workflow key
  • 5918a0e Merge branch 'master' into dev
  • Additional commits viewable in compare view

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Signed-off-by: dependabot[bot] Co-authored-by: dependabot[bot] <49699333+dependabot[bot]@users.noreply.github.com> --- docs/requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/requirements.txt b/docs/requirements.txt index a3873d9e77c27e..84813255ac7694 100644 --- a/docs/requirements.txt +++ b/docs/requirements.txt @@ -8,7 +8,7 @@ certifi==2024.7.4 colorama==0.4.6 Cython==0.29.33 docutils==0.20 -idna==3.4 +idna==3.7 imagesize==1.3.0 importlib-metadata==4.8.0 iniconfig==1.1.1 From e4bcb7a8385d0902747450f54ed82902abc01fef Mon Sep 17 00:00:00 2001 From: hyunback kim Date: Fri, 23 Aug 2024 14:43:05 +0900 Subject: [PATCH 29/35] [GPU] Fix to get bad groups size bug in compressed_fc static shape. (#26145) SD3.int4 static bad accuracy problem, fixed it. ### Tickets: - *146778* --------- Signed-off-by: hyunback --- .../graph_optimizer/prepare_quantization.cpp | 3 +- .../test_cases/fully_connected_gpu_test.cpp | 90 +++++++++++++++++++ 2 files changed, 91 insertions(+), 2 deletions(-) diff --git a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_quantization.cpp b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_quantization.cpp index c17490488b629e..9563a1531cb886 100644 --- a/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_quantization.cpp +++ b/src/plugins/intel_gpu/src/graph/graph_optimizer/prepare_quantization.cpp @@ -511,8 +511,7 @@ static void optimize_weights_decompression_parameters(fully_connected_node& fc_n auto need_reorder = [&](size_t dep_id) { auto dep_layout = fc_node.get_input_layout(dep_id); auto dep_pshape = dep_layout.get_partial_shape(); - - auto groups_count = dep_pshape[dep_pshape.size() - 1].get_length(); + auto groups_count = dep_pshape[1].get_length(); return groups_count > 1; }; diff --git a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp index 3f0b43a861873b..7a9519ba48c05a 100644 --- a/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp +++ b/src/plugins/intel_gpu/tests/unit/test_cases/fully_connected_gpu_test.cpp @@ -3451,6 +3451,96 @@ TEST(fully_connected_3d_onednn_gpu, no_biases_int8) { ASSERT_EQ(-52.0f, output_ptr[b * output_f + 3]); } } + +TEST(fully_connected_3d_onednn_gpu, compressed_int4_scale_static) { + tests::random_generator rg(GET_SUITE_NAME); + + auto& engine = get_test_engine(); + if (!engine.get_device_info().supports_immad) + return; + + long int batch_num = 2; + long int ifm_num = 64; + long int ofm_num = 4; + long int scales_group_size = 32; + + auto input_mem = engine.allocate_memory({ { 1, batch_num, ifm_num, 1}, data_types::f16, format::bfyx }); + auto weights_mem = engine.allocate_memory({ {ofm_num, ifm_num, 1, 1}, data_types::u4, format::bfyx }); + auto scale_mem = engine.allocate_memory({ {ofm_num, ifm_num / scales_group_size, 1, 1}, data_types::f16, format::bfyx }); + auto dcomp_zp_mem = engine.allocate_memory({ {1, 1, 1, 1}, data_types::u8, format::bfyx }); + + set_values(dcomp_zp_mem, {8}); + + auto input_data = rg.generate_random_1d(batch_num * ifm_num, -2.0f, 2.0f); + set_values(input_mem, input_data); + + auto weigths_data = rg.generate_random_1d(ofm_num * ifm_num / 2, 0, 10); + set_values(weights_mem, weigths_data); + + auto scale_data = rg.generate_random_1d(ofm_num * ifm_num / scales_group_size, -4.0f, 4.0f); + set_values(scale_mem, scale_data); + + auto in_layout = layout{ {1, batch_num, ifm_num, 1}, data_types::f16, format::bfyx }; + + auto fc_prim = fully_connected("fc_prim", input_info("input"), "weights", "", "scale", "dcomp_zp", data_types::f16, 3, 4); + + fc_prim.decompression_zero_point_scalar = 8; + + auto get_ref_results = [&]() { + topology topology( + input_layout("input", in_layout), + data("weights", weights_mem), + data("scale", scale_mem), + data("dcomp_zp", dcomp_zp_mem), + fc_prim + ); + + auto config = get_test_default_config(engine); + ov::intel_gpu::ImplementationDesc fc_impl_desc = { format::bfyx, "fully_connected_gpu_bfyx_ref", impl_types::ocl }; + config.set_property(ov::intel_gpu::force_implementations(ov::intel_gpu::ImplForcingMap{ {"fc_prim", fc_impl_desc} })); + + network network(engine, topology, config); + network.set_input_data("input", input_mem); + + auto outputs = network.execute(); + OPENVINO_ASSERT(outputs.size() == 1); + OPENVINO_ASSERT(outputs.begin()->first == "fc_prim"); + + auto output_layout = outputs.begin()->second.get_layout(); + auto output_mem = outputs.begin()->second.get_memory(); + + return engine.reinterpret_buffer(*output_mem, output_layout); + }; + + topology topology( + input_layout("input", in_layout), + data("weights", weights_mem), + data("scale", scale_mem), + data("dcomp_zp", dcomp_zp_mem), + fc_prim + ); + + auto config = get_test_default_config(engine); + config.set_property(ov::intel_gpu::optimize_data(true)); + + network::ptr network = get_network(engine, topology, config, get_test_stream_ptr(), false); + + network->set_input_data("input", input_mem); + + auto outputs = network->execute(); + ASSERT_EQ(outputs.size(), size_t(1)); + ASSERT_EQ(outputs.begin()->first, "fc_prim"); + + auto output_mem = outputs.begin()->second.get_memory(); + cldnn::mem_lock output_ptr (output_mem, get_test_stream()); + + auto ref_output_mem = get_ref_results(); + cldnn::mem_lock output_ptr_ref (ref_output_mem, get_test_stream()); + + for (size_t i = 0; i < output_ptr_ref.size(); i++) { + ASSERT_NEAR(output_ptr_ref[i], output_ptr[i], 9.0) << "i = " << i << std::endl; + } +} #endif TEST_F(fully_connected_gpu_tests, compressed_scale_zp_bias) { From 1f3edda195cba4f46d3cbbf0d9f0f5d867e80a6b Mon Sep 17 00:00:00 2001 From: Mateusz Mikolajczyk Date: Fri, 23 Aug 2024 08:08:42 +0200 Subject: [PATCH 30/35] [Transformations] Add downgrade transformation for ScatterNDUpdate15 with Reduction::None (#26113) ### Details: - *Add downgrade transformation from ScatterNDUpdate15 with Reduction::None to ScatterNDUpdate3* - *...* ### Tickets: - *CVS-149830* --------- Co-authored-by: Roman Kazantsev --- .../convert_scatter_nd_update15_downgrade.hpp | 24 +++++++++ .../common_optimizations.cpp | 2 + .../common_optimizations/nop_elimination.cpp | 6 ++- .../convert_scatter_nd_update15_downgrade.cpp | 39 ++++++++++++++ .../common_optimizations/nop_elimination.cpp | 22 ++++++++ ...ert_scatter_nd_update15_downgrade_test.cpp | 54 +++++++++++++++++++ .../transformation_pipeline.cpp | 2 + .../test_tf_SparseTensorDenseMatMul.py | 2 - 8 files changed, 147 insertions(+), 4 deletions(-) create mode 100644 src/common/transformations/include/transformations/op_conversions/convert_scatter_nd_update15_downgrade.hpp create mode 100644 src/common/transformations/src/transformations/op_conversions/convert_scatter_nd_update15_downgrade.cpp create mode 100644 src/common/transformations/tests/op_conversions/convert_scatter_nd_update15_downgrade_test.cpp diff --git a/src/common/transformations/include/transformations/op_conversions/convert_scatter_nd_update15_downgrade.hpp b/src/common/transformations/include/transformations/op_conversions/convert_scatter_nd_update15_downgrade.hpp new file mode 100644 index 00000000000000..dfaab66e22501c --- /dev/null +++ b/src/common/transformations/include/transformations/op_conversions/convert_scatter_nd_update15_downgrade.hpp @@ -0,0 +1,24 @@ +// Copyright (C) 2018-2023 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#pragma once + +#include "openvino/pass/graph_rewrite.hpp" +#include "transformations_visibility.hpp" + +namespace ov { +namespace pass { +/** + * @ingroup ov_transformation_common_api + * @brief Converts ScatterNDUpdate version 15 to ScatterNDUpdate version 3 if ScatterNDUpdate reduction attribute is set + * to None. + */ +class TRANSFORMATIONS_API ConvertScatterNDUpdate15ToScatterNDUpdate3 : public MatcherPass { +public: + OPENVINO_RTTI("ConvertScatterNDUpdate15ToScatterNDUpdate3", "0"); + ConvertScatterNDUpdate15ToScatterNDUpdate3(); +}; + +} // namespace pass +} // namespace ov diff --git a/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp index d7ca44e7ddad34..500d003bd4642e 100644 --- a/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp +++ b/src/common/transformations/src/transformations/common_optimizations/common_optimizations.cpp @@ -92,6 +92,7 @@ #include "transformations/op_conversions/convert_roi_align_v3_to_v9.hpp" #include "transformations/op_conversions/convert_roi_align_v9_to_v3.hpp" #include "transformations/op_conversions/convert_scatter_elements_update12_downgrade.hpp" +#include "transformations/op_conversions/convert_scatter_nd_update15_downgrade.hpp" #include "transformations/op_conversions/convert_slice_to_strided_slice.hpp" #include "transformations/op_conversions/convert_softmax_downgrade.hpp" #include "transformations/op_conversions/convert_softmax_upgrade.hpp" @@ -231,6 +232,7 @@ bool ov::pass::CommonOptimizations::run_on_model(const std::shared_ptr(); ADD_MATCHER(fq_fusions, FakeQuantizeMulFusion) diff --git a/src/common/transformations/src/transformations/common_optimizations/nop_elimination.cpp b/src/common/transformations/src/transformations/common_optimizations/nop_elimination.cpp index 13da1c4d9082fc..782c25c2bf03ae 100644 --- a/src/common/transformations/src/transformations/common_optimizations/nop_elimination.cpp +++ b/src/common/transformations/src/transformations/common_optimizations/nop_elimination.cpp @@ -789,8 +789,10 @@ pass::EliminateEltwise::EliminateEltwise() { pass::EliminateScatterUpdate::EliminateScatterUpdate() { MATCHER_SCOPE(EliminateScatterUpdate); - auto scatter_pattern = - pattern::wrap_type(); + auto scatter_pattern = pattern::wrap_type(); matcher_pass_callback callback = [=](pattern::Matcher& m) { auto scatter = m.get_match_root(); diff --git a/src/common/transformations/src/transformations/op_conversions/convert_scatter_nd_update15_downgrade.cpp b/src/common/transformations/src/transformations/op_conversions/convert_scatter_nd_update15_downgrade.cpp new file mode 100644 index 00000000000000..94b2955ee818cc --- /dev/null +++ b/src/common/transformations/src/transformations/op_conversions/convert_scatter_nd_update15_downgrade.cpp @@ -0,0 +1,39 @@ +// Copyright (C) 2018-2023 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "transformations/op_conversions/convert_scatter_nd_update15_downgrade.hpp" + +#include "itt.hpp" +#include "openvino/core/rt_info.hpp" +#include "openvino/op/scatter_nd_update.hpp" +#include "openvino/pass/pattern/op/wrap_type.hpp" +#include "transformations/utils/utils.hpp" + +ov::pass::ConvertScatterNDUpdate15ToScatterNDUpdate3::ConvertScatterNDUpdate15ToScatterNDUpdate3() { + MATCHER_SCOPE(ConvertScatterNDUpdate15ToScatterNDUpdate3); + + const auto scatter_v15_pattern = pattern::wrap_type(); + + const matcher_pass_callback callback = [OV_CAPTURE_CPY_AND_THIS](pattern::Matcher& m) { + const auto scatter_v15 = std::dynamic_pointer_cast(m.get_match_root()); + if (!scatter_v15 || transformation_callback(scatter_v15)) { + return false; + } + if (scatter_v15->get_reduction() != ov::op::v15::ScatterNDUpdate::Reduction::NONE) { + return false; + } + const auto scatter_v3 = std::make_shared(scatter_v15->input_value(0), + scatter_v15->input_value(1), + scatter_v15->input_value(2)); + + scatter_v3->set_friendly_name(scatter_v15->get_friendly_name()); + copy_runtime_info(scatter_v15, scatter_v3); + replace_node(scatter_v15, scatter_v3); + + return true; + }; + + auto m = std::make_shared(scatter_v15_pattern, matcher_name); + register_matcher(m, callback); +} diff --git a/src/common/transformations/tests/common_optimizations/nop_elimination.cpp b/src/common/transformations/tests/common_optimizations/nop_elimination.cpp index e9b4bf3d8be3d1..9ee362104b24d0 100644 --- a/src/common/transformations/tests/common_optimizations/nop_elimination.cpp +++ b/src/common/transformations/tests/common_optimizations/nop_elimination.cpp @@ -1746,3 +1746,25 @@ TEST_F(TransformationTestsF, TransposeElimination) { model_ref = std::make_shared(OutputVector{result}, ParameterVector{data}); } } + +TEST_F(TransformationTestsF, ScatterNDUpdates15Elimination) { + { + auto data = std::make_shared(element::f32, PartialShape{100, 256, 10, 15}); + auto indices = std::make_shared(element::i32, PartialShape{25, 0, 3}); + auto updates = std::make_shared(element::f32, PartialShape{25, 0, 15}); + auto relu = std::make_shared(data); + auto scatter = std::make_shared(relu, indices, updates); + + auto result = std::make_shared(scatter); + model = std::make_shared(OutputVector{result}, ParameterVector{data, indices, updates}); + manager.register_pass(); + } + { + auto data = std::make_shared(element::f32, PartialShape{100, 256, 10, 15}); + auto indices = std::make_shared(element::i32, PartialShape{25, 0, 3}); + auto updates = std::make_shared(element::f32, PartialShape{25, 0, 15}); + auto relu = std::make_shared(data); + auto result = std::make_shared(relu); + model_ref = std::make_shared(OutputVector{result}, ParameterVector{data, indices, updates}); + } +} diff --git a/src/common/transformations/tests/op_conversions/convert_scatter_nd_update15_downgrade_test.cpp b/src/common/transformations/tests/op_conversions/convert_scatter_nd_update15_downgrade_test.cpp new file mode 100644 index 00000000000000..b2a126efe7803e --- /dev/null +++ b/src/common/transformations/tests/op_conversions/convert_scatter_nd_update15_downgrade_test.cpp @@ -0,0 +1,54 @@ +// Copyright (C) 2018-2024 Intel Corporation +// SPDX-License-Identifier: Apache-2.0 +// + +#include "transformations/op_conversions/convert_scatter_nd_update15_downgrade.hpp" + +#include + +#include + +#include "common_test_utils/ov_test_utils.hpp" +#include "openvino/opsets/opset15.hpp" +#include "openvino/opsets/opset4.hpp" +#include "openvino/pass/manager.hpp" +#include "transformations/utils/utils.hpp" +using namespace ov; +using namespace testing; + +namespace { +using Reduction = ov::opset15::ScatterNDUpdate::Reduction; + +std::shared_ptr create_v15_model(const Reduction reduction_type) { + const auto data = std::make_shared(ov::element::f32, ov::Shape{1000, 256, 10, 15}); + const auto indices = std::make_shared(ov::element::i32, ov::Shape{25, 125, 3}); + const auto updates = std::make_shared(ov::element::f32, ov::Shape{25, 125, 15}); + const auto scatter_nd = std::make_shared(data, indices, updates, reduction_type); + scatter_nd->set_friendly_name("scatter_nd15"); + return std::make_shared(scatter_nd->outputs(), ov::ParameterVector{data, indices, updates}); +} + +std::shared_ptr create_v3_model() { + const auto data = std::make_shared(ov::element::f32, ov::Shape{1000, 256, 10, 15}); + const auto indices = std::make_shared(ov::element::i32, ov::Shape{25, 125, 3}); + const auto updates = std::make_shared(ov::element::f32, ov::Shape{25, 125, 15}); + const auto scatter_nd = std::make_shared(data, indices, updates); + scatter_nd->set_friendly_name("scatter_nd15"); + + return std::make_shared(scatter_nd->outputs(), ov::ParameterVector{data, indices, updates}); +} + +} // namespace + +TEST_F(TransformationTestsF, ConvertScatterNDUpdate15ToScatterNDUpdate3_no_reduction) { + manager.register_pass(); + model = create_v15_model(Reduction::NONE); + model_ref = create_v3_model(); + comparator.enable(FunctionsComparator::CmpValues::CONST_VALUES); + comparator.enable(FunctionsComparator::CmpValues::ATTRIBUTES); +} + +TEST_F(TransformationTestsF, ConvertScatterNDUpdate15ToScatterNDUpdate3_reduction) { + manager.register_pass(); + model = create_v15_model(Reduction::PROD); +} diff --git a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp index 43862022462ada..54a038c9492db6 100644 --- a/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp +++ b/src/plugins/intel_cpu/src/transformations/transformation_pipeline.cpp @@ -58,6 +58,7 @@ #include "transformations/op_conversions/convert_reduce_to_pooling.hpp" #include "transformations/op_conversions/convert_roi_align_v3_to_v9.hpp" #include "transformations/op_conversions/convert_roi_align_v9_to_v3.hpp" +#include "transformations/op_conversions/convert_scatter_nd_update15_downgrade.hpp" #include "transformations/op_conversions/convert_sequences_to_tensor_iterator.hpp" #include "transformations/op_conversions/convert_shuffle_channels3.hpp" #include "transformations/op_conversions/convert_slice_to_strided_slice.hpp" @@ -636,6 +637,7 @@ void Transformations::PreLpt(const std::vector& defaultPrecis CPU_DISABLE_PASS_COMMON(manager, ov::pass::ConvertTopK11ToTopK3); CPU_DISABLE_PASS_COMMON(manager, ov::pass::HSwishDecomposition); CPU_DISABLE_PASS_COMMON(manager, ov::pass::MatMulConstTransposesExtraction); + CPU_DISABLE_PASS_COMMON(manager, ov::pass::ConvertScatterNDUpdate15ToScatterNDUpdate3); CPU_DISABLE_PASS_X64(manager, ov::pass::HSigmoidDecomposition); CPU_DISABLE_PASS_X64(manager, ov::pass::ReduceL1Decomposition); diff --git a/tests/layer_tests/tensorflow_tests/test_tf_SparseTensorDenseMatMul.py b/tests/layer_tests/tensorflow_tests/test_tf_SparseTensorDenseMatMul.py index 1eb31a753b6bfd..49df4226a7fcb2 100644 --- a/tests/layer_tests/tensorflow_tests/test_tf_SparseTensorDenseMatMul.py +++ b/tests/layer_tests/tensorflow_tests/test_tf_SparseTensorDenseMatMul.py @@ -91,8 +91,6 @@ def test_sparse_tensor_dense_mat_mul(self, data_type, indices_type, a_shape, b_shape, nnz, ie_device, precision, ir_version, temp_dir, use_legacy_frontend): - if ie_device == 'GPU': - pytest.skip("149830: ScatterNDUpdate-15 is not supported on GPU") self._test(*self.create_sparse_tensor_dense_mat_mul_net(data_type, indices_type, adjoint_a, adjoint_b, a_shape, b_shape, nnz), From 0ced13225dcdb83833c89e24b3fb1fcdd395a948 Mon Sep 17 00:00:00 2001 From: Xiake Sun Date: Fri, 23 Aug 2024 14:25:34 +0800 Subject: [PATCH 31/35] Fix performance profiling on GPU with oneDNN support (#26174) Fix performance profiling on GPU with oneDNN support, so that benchmark_app can get performance counter and dump exec graph on DG2 and further platform with oneDNN support. CVS-150346 --------- Co-authored-by: Sergey Shlyapnikov --- .../src/graph/impls/onednn/primitive_onednn_base.h | 8 ++++++-- 1 file changed, 6 insertions(+), 2 deletions(-) diff --git a/src/plugins/intel_gpu/src/graph/impls/onednn/primitive_onednn_base.h b/src/plugins/intel_gpu/src/graph/impls/onednn/primitive_onednn_base.h index 573c29b115842e..57fd4afbe933d6 100644 --- a/src/plugins/intel_gpu/src/graph/impls/onednn/primitive_onednn_base.h +++ b/src/plugins/intel_gpu/src/graph/impls/onednn/primitive_onednn_base.h @@ -560,10 +560,14 @@ struct typed_primitive_onednn_impl : public typed_primitive_impl { stream.wait(); std::vector duration = dnnl::get_profiling_data(stream.get_onednn_stream(), dnnl::profiling_data_kind::time); - OPENVINO_ASSERT(duration.size() == 1, "[GPU] oneDNN profiling data is expected to have info only for single primitive ", + if (duration.empty()) { + event = std::make_shared(0); + } else { + OPENVINO_ASSERT(duration.size() == 1, "[GPU] oneDNN profiling data is expected to have info only for single primitive ", "actual number is ", duration.size()); + event = std::make_shared(duration[0]); + } - event = std::make_shared(duration[0]); } else { // If oneDNN primitive is the output primitive or it's user is CPU implementation, then enqueue marker // with empty events wait list (which will trigger wait for all previously enqueued tasks) and From 2fd35c95acd379a41601208258e4056bed7377e3 Mon Sep 17 00:00:00 2001 From: David Pava Date: Fri, 23 Aug 2024 09:29:48 +0300 Subject: [PATCH 32/35] [NPU] Removing a ref to fix workload type testing errors (#26186) ### Details: - Removing a ref to fix workload type testing errors ### Tickets: --- src/plugins/intel_npu/src/plugin/src/compiled_model.cpp | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/plugins/intel_npu/src/plugin/src/compiled_model.cpp b/src/plugins/intel_npu/src/plugin/src/compiled_model.cpp index 54942d1416208e..d584c62704fbf9 100644 --- a/src/plugins/intel_npu/src/plugin/src/compiled_model.cpp +++ b/src/plugins/intel_npu/src/plugin/src/compiled_model.cpp @@ -214,7 +214,7 @@ void CompiledModel::configure_stream_executors() { } void CompiledModel::initialize_properties() { - const auto& pluginSupportedProperties = + const auto pluginSupportedProperties = get_plugin()->get_property(ov::supported_properties.name(), {}).as>(); const auto isPropertySupported = [&pluginSupportedProperties](const std::string& name) { return std::any_of(pluginSupportedProperties.begin(), From 36e7b30b4fc8a3ec9c9112fba72b9c3c9b9c8d24 Mon Sep 17 00:00:00 2001 From: Oleg Pipikin Date: Fri, 23 Aug 2024 08:50:35 +0200 Subject: [PATCH 33/35] Optimization of raw meta data serialization (#26115) ### Details: - Optimization of raw meta data serialization - Fix legacy meta data creation ### Tickets: - CVS-123381 --- src/core/dev_api/openvino/core/meta_data.hpp | 14 ++++ src/core/src/pass/serialize.cpp | 22 ++++-- .../serialization/rt_info_serialization.cpp | 78 +++++++++++++++++++ src/frontends/ir/src/ir_deserializer.cpp | 67 ++++++++++------ 4 files changed, 153 insertions(+), 28 deletions(-) diff --git a/src/core/dev_api/openvino/core/meta_data.hpp b/src/core/dev_api/openvino/core/meta_data.hpp index 3b0debaff3763e..221aca96080d65 100644 --- a/src/core/dev_api/openvino/core/meta_data.hpp +++ b/src/core/dev_api/openvino/core/meta_data.hpp @@ -6,6 +6,10 @@ #include "openvino/core/any.hpp" +namespace pugi { +class xml_node; +} + namespace ov { /** @@ -32,4 +36,14 @@ class OPENVINO_API Meta { virtual ~Meta() = default; }; +class MetaDataWithPugixml : public Meta { +public: + /** + * @brief Returns meta unchanged meta information. Throws ov::Exception if the meta was potentially changed + * + * @return const pugi::xml_node& with meta information + */ + virtual const pugi::xml_node& get_pugi_node() const = 0; +}; + } // namespace ov diff --git a/src/core/src/pass/serialize.cpp b/src/core/src/pass/serialize.cpp index 17da51b5831c99..1d5e295e8dbe23 100644 --- a/src/core/src/pass/serialize.cpp +++ b/src/core/src/pass/serialize.cpp @@ -916,11 +916,23 @@ void serialize_rt_info(pugi::xml_node& root, const std::string& name, const ov:: child.append_attribute("name").set_value(name.c_str()); } if (data.is>()) { - std::shared_ptr meta = data.as>(); - ov::AnyMap& map = *meta; - for (const auto& it : map) { - serialize_rt_info(child, it.first, it.second); - } + auto meta = data.as>(); + do { + if (auto meta_with_pugixml_node = std::dynamic_pointer_cast(meta)) { + if (auto pugi_node = meta_with_pugixml_node->get_pugi_node()) { + root.remove_child(child); + auto added_node = root.append_copy(pugi_node); + OPENVINO_ASSERT(added_node, "Cannot add pugixml node with name: ", name); + added_node.set_name(name.c_str()); + break; + } + } + // Meta in ov::Meta cannot be accessed by MetaDataWithPugixml::get_pugi_node. Read it as ov::AnyMap + ov::AnyMap& map = *meta; + for (const auto& it : map) { + serialize_rt_info(child, it.first, it.second); + } + } while (false); } else if (data.is()) { const ov::AnyMap& any_map = data.as(); for (const auto& it : any_map) { diff --git a/src/core/tests/pass/serialization/rt_info_serialization.cpp b/src/core/tests/pass/serialization/rt_info_serialization.cpp index 2664ab5edca18a..412625d45d53d9 100644 --- a/src/core/tests/pass/serialization/rt_info_serialization.cpp +++ b/src/core/tests/pass/serialization/rt_info_serialization.cpp @@ -10,6 +10,7 @@ #include "openvino/frontend/manager.hpp" #include "openvino/opsets/opset8.hpp" #include "openvino/pass/manager.hpp" +#include "openvino/runtime/core.hpp" #include "transformations/rt_info/attributes.hpp" class RTInfoSerializationTest : public ov::test::TestsCommon { @@ -221,3 +222,80 @@ TEST_F(RTInfoSerializationTest, tag_names_verification) { ASSERT_EQ(model_rt_info[item.first], item.second); }); } + +TEST(OvSerializationTests, SerializeRawMeta) { + std::string ir_with_rt_info = R"V0G0N( + + + + + + + 3 + 20 + 20 + + + + + + + 3 + 20 + 20 + + + + + + + + + + + + + + + + +)V0G0N"; + ov::Core core; + { + // Don't read meta data. Copy raw pugixml::node from MetaDataWithPugixml to serialized model + auto model = core.read_model(ir_with_rt_info, ov::Tensor()); + + std::stringstream model_ss, weights_ss; + ov::pass::Serialize(model_ss, weights_ss).run_on_model(model); + + auto serialized_model = model_ss.str(); + EXPECT_EQ(0, serialized_model.compare(ir_with_rt_info)); + } + + { + // Don't read meta data. Fully serialize AnyMap with meta + auto model = core.read_model(ir_with_rt_info, ov::Tensor()); + auto custom_rt_info1_value = model->get_rt_info("custom_rt_info1", "item0"); + EXPECT_EQ(0, custom_rt_info1_value.compare("testvalue1")); + auto custom_rt_info2_value = model->get_rt_info("custom_rt_info2", "item0"); + EXPECT_EQ(0, custom_rt_info2_value.compare("testvalue2")); + + std::stringstream model_ss, weights_ss; + ov::pass::Serialize(model_ss, weights_ss).run_on_model(model); + + auto serialized_model = model_ss.str(); + EXPECT_EQ(0, serialized_model.compare(ir_with_rt_info)); + } + + { + auto model = core.read_model(ir_with_rt_info, ov::Tensor()); + auto custom_rt_info1_value = model->get_rt_info("custom_rt_info1", "item0"); + EXPECT_EQ(0, custom_rt_info1_value.compare("testvalue1")); + + std::stringstream model_ss, weights_ss; + ov::pass::Serialize(model_ss, weights_ss).run_on_model(model); + + auto serialized_model = model_ss.str(); + EXPECT_EQ(0, serialized_model.compare(ir_with_rt_info)); + } +} diff --git a/src/frontends/ir/src/ir_deserializer.cpp b/src/frontends/ir/src/ir_deserializer.cpp index 8c8aec333e107e..68900b150514bc 100644 --- a/src/frontends/ir/src/ir_deserializer.cpp +++ b/src/frontends/ir/src/ir_deserializer.cpp @@ -584,10 +584,17 @@ std::shared_ptr ov::XmlDeserializer::parse_function(const pugi::xml_n return function; } -class MetaDataParser : public ov::Meta { +class MetaDataParser : public ov::MetaDataWithPugixml { public: - MetaDataParser(const std::string& name, const pugi::xml_node& meta) : m_name(name) { + MetaDataParser(const std::string& name, const pugi::xml_node& meta, bool accessible_by_pugixml_node = true) + : m_name(name), + m_accessible_by_pugixml_node(accessible_by_pugixml_node) { m_meta.append_copy(meta); + if (accessible_by_pugixml_node) { + m_meta_node = m_meta.child(m_name.c_str()); + } else { + m_meta_node = pugi::xml_node(); + } } operator const ov::AnyMap&() const override { @@ -600,6 +607,14 @@ class MetaDataParser : public ov::Meta { return m_parsed_map; } + const pugi::xml_node& get_pugi_node() const override { + if (!m_meta_node.empty() && !m_accessible_by_pugixml_node) { + // Meta cannot be accessed by pugixml node. Return empty node + m_meta_node = pugi::xml_node(); + } + return m_meta_node; + }; + private: bool has_attr(const pugi::xml_node& node, const std::string& name = "value") const { auto attr = node.attribute(name.c_str()); @@ -646,14 +661,18 @@ class MetaDataParser : public ov::Meta { void parse() const { std::call_once(m_oc, [this]() { + m_accessible_by_pugixml_node = false; const pugi::xml_node& node = m_meta.child(m_name.c_str()); m_parsed_map = parse_node(node); }); } + pugi::xml_document m_meta; const std::string m_name; mutable ov::AnyMap m_parsed_map; mutable std::once_flag m_oc; + mutable std::atomic_bool m_accessible_by_pugixml_node; + mutable pugi::xml_node m_meta_node; }; void ov::XmlDeserializer::read_meta_data(const std::shared_ptr& model, const pugi::xml_node& meta_section) { @@ -681,29 +700,31 @@ void ov::XmlDeserializer::read_meta_data(const std::shared_ptr& model void ov::XmlDeserializer::read_legacy_meta_data(const std::shared_ptr& model, const std::unordered_set& names, const pugi::xml_node& root_section) { - const auto& read_meta = [](const std::shared_ptr& model, - const std::string& name, - const pugi::xml_node& meta_section) { - auto& rt_info = model->get_rt_info(); - if (name == "meta_data") { - for (const auto& data : meta_section.children()) { - const std::string& section_name = data.name(); - // Rename cli_parameters to conversion_parameters - if (section_name == "cli_parameters") { - std::shared_ptr meta = std::make_shared("cli_parameters", data); - rt_info["conversion_parameters"] = meta; - } else if (!data.attribute("value").empty()) { - rt_info[data.name()] = pugixml::get_str_attr(data, "value"); - } else { - OPENVINO_THROW("Unsupported legacy argument: ", data.name()); + const auto& read_meta = + [](const std::shared_ptr& model, const std::string& name, const pugi::xml_node& meta_section) { + auto& rt_info = model->get_rt_info(); + if (name == "meta_data") { + for (const auto& data : meta_section.children()) { + const std::string& section_name = data.name(); + // Rename cli_parameters to conversion_parameters + if (section_name == "cli_parameters") { + std::shared_ptr meta = std::make_shared("cli_parameters", data); + rt_info["conversion_parameters"] = meta; + } else if (!data.attribute("value").empty()) { + rt_info[data.name()] = pugixml::get_str_attr(data, "value"); + } else { + OPENVINO_THROW("Unsupported legacy argument: ", data.name()); + } } + } else if (name == "quantization_parameters") { + // Rename quantization_parameters to optimization + // Legacy implementation. Have to be parsed inside MetaDataParser. Do not allow to serialize it as raw + // pugi::xml_node. + std::shared_ptr meta = + std::make_shared("quantization_parameters", meta_section, false); + rt_info["optimization"] = meta; } - } else if (name == "quantization_parameters") { - // Rename quantization_parameters to optimization - std::shared_ptr meta = std::make_shared("quantization_parameters", meta_section); - rt_info["optimization"] = meta; - } - }; + }; for (const auto& it : names) read_meta(model, it, root_section.child(it.c_str())); } From 8a08bb9c5f901da89596070d189d2aa67879de34 Mon Sep 17 00:00:00 2001 From: tadamczx <156996781+tadamczx@users.noreply.github.com> Date: Fri, 23 Aug 2024 08:51:47 +0200 Subject: [PATCH 34/35] [DOCS] Simplified snippets setting to work in every environment (#26193) ### Details: - *item1* - *...* ### Tickets: - *ticket-id* --- docs/sphinx_setup/conf.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/sphinx_setup/conf.py b/docs/sphinx_setup/conf.py index 5b679d10213774..ea144ca84d6154 100644 --- a/docs/sphinx_setup/conf.py +++ b/docs/sphinx_setup/conf.py @@ -113,8 +113,6 @@ "show_prev_next": False, } -snippet_root = os.getenv("SNIPPET_ROOT", "") - html_sidebars = { "**": ["search-field.html", "sidebar-nav-bs.html", "sidebar-ethical-ads.html"] } @@ -123,7 +121,8 @@ 'current_language': 'English', #'languages': (('English', '/latest'), ('Chinese', '/cn/latest')), 'doxygen_mapping_file': '@DOXYGEN_MAPPING_FILE@', - 'doxygen_snippet_root': snippet_root, + # go back fours 3 steps down in directory to reach openvino dir + 'doxygen_snippet_root': os.path.abspath(os.path.join(os.path.dirname(__file__), '../../../')), 'default_mode': 'light' } From 6d0d7716ac193e77ee1e63013c3072e3984f746e Mon Sep 17 00:00:00 2001 From: Roman Kazantsev Date: Fri, 23 Aug 2024 16:20:36 +0400 Subject: [PATCH 35/35] [MO Tests] Switch off legacy test_reduce_lp test (#26196) **Details:** Switch off legacy test_reduce_lp test **Ticket:** 150423 --------- Signed-off-by: Kazantsev, Roman --- tools/mo/unit_tests/mo/ops/ReduceOps_test.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tools/mo/unit_tests/mo/ops/ReduceOps_test.py b/tools/mo/unit_tests/mo/ops/ReduceOps_test.py index f1cbc19d331a3a..947fc95c38ab9f 100644 --- a/tools/mo/unit_tests/mo/ops/ReduceOps_test.py +++ b/tools/mo/unit_tests/mo/ops/ReduceOps_test.py @@ -2,6 +2,7 @@ # SPDX-License-Identifier: Apache-2.0 import pytest +import unittest import numpy as np @@ -21,6 +22,7 @@ class TestReduceLpTest(): + @unittest.skip("Skipped due to function array_equal failure") @pytest.mark.parametrize("shape, axes, keepdims, p",[ ([3, 2, 2], [0], True, 1), ([3, 2, 2], [0], True, 2),