diff --git a/paddle/fluid/framework/distributed_strategy.proto b/paddle/fluid/framework/distributed_strategy.proto index 2974295f72fed19d25919c9a4b30d484bc2f1f6e..e792d2a38dc7e0767b82f96b59ff09729bcb5cd8 100755 --- a/paddle/fluid/framework/distributed_strategy.proto +++ b/paddle/fluid/framework/distributed_strategy.proto @@ -123,6 +123,7 @@ message BuildStrategy { optional bool allow_cuda_graph_capture = 14 [ default = false ]; optional int32 reduce_strategy = 15 [ default = 0 ]; optional bool fuse_gemm_epilogue = 16 [ default = false ]; + optional string debug_graphviz_path = 17; } message ExecutionStrategy { diff --git a/paddle/fluid/operators/fused/cudnn_norm_conv.cu.h b/paddle/fluid/operators/fused/cudnn_norm_conv.cu.h index cde4ed061423e8325001e6a244805eaad9d666aa..6eb71442c6a3ca8375808415d662d16a31666925 100644 --- a/paddle/fluid/operators/fused/cudnn_norm_conv.cu.h +++ b/paddle/fluid/operators/fused/cudnn_norm_conv.cu.h @@ -45,6 +45,14 @@ struct NormConvolutionArgs { int stride, int dilation, int group) { + PADDLE_ENFORCE_LT( + ctx.GetComputeCapability(), + 90, + phi::errors::PreconditionNotMet( + "Expect compute compatiblity to be less than 90, but got %d. " + "CUDNN FusedOps is no longer available on H100 and later " + "devices.", + ctx.GetComputeCapability())); PADDLE_ENFORCE_EQ( input_shape.size(), 4U, diff --git a/paddle/fluid/operators/fused/cudnn_norm_conv_test.cc b/paddle/fluid/operators/fused/cudnn_norm_conv_test.cc index ef93612ffce39ae81ece6c33c038a3714f755424..52a0efc225fc4a059f405a02ef3f93b832234c4e 100644 --- a/paddle/fluid/operators/fused/cudnn_norm_conv_test.cc +++ b/paddle/fluid/operators/fused/cudnn_norm_conv_test.cc @@ -442,7 +442,7 @@ TEST(CudnnNormConvFp16, K1S1) { phi::GPUContext *ctx = static_cast( platform::DeviceContextPool::Instance().Get(platform::CUDAPlace(0))); - if (ctx->GetComputeCapability() < 70) { + if (ctx->GetComputeCapability() < 70 || ctx->GetComputeCapability() >= 90) { ASSERT_THROW(test.CheckForward(1e-3, true), paddle::platform::EnforceNotMet); ASSERT_THROW(test.CheckBackward(1e-3, true), @@ -472,7 +472,7 @@ TEST(CudnnNormConvFp16, K3S1) { phi::GPUContext *ctx = static_cast( platform::DeviceContextPool::Instance().Get(platform::CUDAPlace(0))); - if (ctx->GetComputeCapability() < 70) { + if (ctx->GetComputeCapability() < 70 || ctx->GetComputeCapability() >= 90) { ASSERT_THROW(test.CheckForward(1e-3, true), paddle::platform::EnforceNotMet); ASSERT_THROW(test.CheckBackward(1e-3, true), @@ -502,7 +502,7 @@ TEST(CudnnNormConvFp16, K1S1O4) { phi::GPUContext *ctx = static_cast( platform::DeviceContextPool::Instance().Get(platform::CUDAPlace(0))); - if (ctx->GetComputeCapability() < 70) { + if (ctx->GetComputeCapability() < 70 || ctx->GetComputeCapability() >= 90) { ASSERT_THROW(test.CheckForward(1e-3, true), paddle::platform::EnforceNotMet); ASSERT_THROW(test.CheckBackward(1e-3, true), @@ -532,7 +532,7 @@ TEST(CudnnNormConvFp16, K1S2O4) { phi::GPUContext *ctx = static_cast( platform::DeviceContextPool::Instance().Get(platform::CUDAPlace(0))); - if (ctx->GetComputeCapability() <= 70) { + if (ctx->GetComputeCapability() <= 70 || ctx->GetComputeCapability() >= 90) { ASSERT_THROW(test.CheckForward(1e-3, true), paddle::platform::EnforceNotMet); ASSERT_THROW(test.CheckBackward(1e-3), paddle::platform::EnforceNotMet); diff --git a/paddle/fluid/operators/fused/fused_dropout_act_bias.h b/paddle/fluid/operators/fused/fused_dropout_act_bias.h index 6b2cdfb6a8d2f70bcaab2907e1c7ebae37e77ff4..e3e19d9ea6ebcbea48b83a54b0edb817cbec4f8c 100644 --- a/paddle/fluid/operators/fused/fused_dropout_act_bias.h +++ b/paddle/fluid/operators/fused/fused_dropout_act_bias.h @@ -256,17 +256,19 @@ template -__global__ void FusedDropoutActBiasGrad(Functor act_grad, - const T *dout, - const MaskType *mask, - const T *src, - const T *bias, - const T factor, - const int64_t rows, - const int64_t cols, - T *dx, - T *dbias) { + typename Functor, + int THREADS_PER_CTA = BlockSizeX *BlockSizeY> +__global__ __launch_bounds__(THREADS_PER_CTA) void FusedDropoutActBiasGrad( + Functor act_grad, + const T *dout, + const MaskType *mask, + const T *src, + const T *bias, + const T factor, + const int64_t rows, + const int64_t cols, + T *dx, + T *dbias) { int64_t col_id = blockIdx.x * blockDim.x + threadIdx.x; using LoadT = phi::AlignedVector; diff --git a/python/paddle/fluid/core.py b/python/paddle/fluid/core.py index bdef1104e62491d7ec3312320df0dc0506379340..6c642dba67a69522dadd84193ac47c7299b05f1b 100644 --- a/python/paddle/fluid/core.py +++ b/python/paddle/fluid/core.py @@ -35,9 +35,9 @@ try: if os.name == 'nt': third_lib_path = current_path + os.sep + '..' + os.sep + 'libs' # Will load shared library from 'path' on windows - os.environ[ - 'path'] = current_path + ';' + third_lib_path + ';' + os.environ[ - 'path'] + os.environ['path'] = ( + current_path + ';' + third_lib_path + ';' + os.environ['path'] + ) sys.path.insert(0, third_lib_path) # Note: from python3.8, PATH will not take effect # https://github.com/python/cpython/pull/12302 @@ -47,20 +47,24 @@ try: except ImportError as e: from .. import compat as cpt + if os.name == 'nt': executable_path = os.path.abspath(os.path.dirname(sys.executable)) raise ImportError( """NOTE: You may need to run \"set PATH=%s;%%PATH%%\" if you encounters \"DLL load failed\" errors. If you have python installed in other directory, replace \"%s\" with your own - directory. The original error is: \n %s""" % - (executable_path, executable_path, cpt.get_exception_message(e))) + directory. The original error is: \n %s""" + % (executable_path, executable_path, cpt.get_exception_message(e)) + ) else: raise ImportError( """NOTE: You may need to run \"export LD_LIBRARY_PATH=/usr/local/lib:$LD_LIBRARY_PATH\" if you encounters \"libmkldnn.so not found\" errors. If you have python installed in other directory, replace \"/usr/local/lib\" with your own - directory. The original error is: \n""" + cpt.get_exception_message(e)) + directory. The original error is: \n""" + + cpt.get_exception_message(e) + ) except Exception as e: raise e @@ -70,36 +74,45 @@ def avx_supported(): Whether current system(Linux, MacOS, Windows) is supported with AVX. """ from .. import compat as cpt + sysstr = platform.system().lower() has_avx = False if sysstr == 'linux': try: - has_avx = os.popen('cat /proc/cpuinfo | grep -i avx').read() != '' + pipe = os.popen('cat /proc/cpuinfo | grep -i avx') + has_avx = pipe.read() != '' + pipe.close() except Exception as e: - sys.stderr.write('Can not get the AVX flag from /proc/cpuinfo.\n' - 'The original error is: %s\n' % - cpt.get_exception_message(e)) + sys.stderr.write( + 'Can not get the AVX flag from /proc/cpuinfo.\n' + 'The original error is: %s\n' % cpt.get_exception_message(e) + ) return has_avx elif sysstr == 'darwin': try: - has_avx = os.popen( - 'sysctl machdep.cpu.features | grep -i avx').read() != '' + pipe = os.popen('sysctl machdep.cpu.features | grep -i avx') + has_avx = pipe.read() != '' + pipe.close() except Exception as e: sys.stderr.write( 'Can not get the AVX flag from machdep.cpu.features.\n' - 'The original error is: %s\n' % cpt.get_exception_message(e)) + 'The original error is: %s\n' % cpt.get_exception_message(e) + ) if not has_avx: import subprocess + pipe = subprocess.Popen( 'sysctl machdep.cpu.leaf7_features | grep -i avx', shell=True, stdout=subprocess.PIPE, - stderr=subprocess.PIPE) + stderr=subprocess.PIPE, + ) _ = pipe.communicate() has_avx = True if pipe.returncode == 0 else False return has_avx elif sysstr == 'windows': import ctypes + ONE_PAGE = ctypes.c_size_t(0x1000) def asm_func(code_str, restype=ctypes.c_uint32, argtypes=()): @@ -109,24 +122,31 @@ def avx_supported(): pfnVirtualAlloc.restype = ctypes.c_void_p MEM_COMMIT = ctypes.c_ulong(0x1000) PAGE_READWRITE = ctypes.c_ulong(0x4) - address = pfnVirtualAlloc(None, ONE_PAGE, MEM_COMMIT, - PAGE_READWRITE) + address = pfnVirtualAlloc( + None, ONE_PAGE, MEM_COMMIT, PAGE_READWRITE + ) if not address: raise Exception("Failed to VirtualAlloc") # Copy the code into the memory segment - memmove = ctypes.CFUNCTYPE(ctypes.c_void_p, ctypes.c_void_p, - ctypes.c_void_p, - ctypes.c_size_t)(ctypes._memmove_addr) + memmove = ctypes.CFUNCTYPE( + ctypes.c_void_p, + ctypes.c_void_p, + ctypes.c_void_p, + ctypes.c_size_t, + )(ctypes._memmove_addr) if memmove(address, code_str, len(code_str)) < 0: raise Exception("Failed to memmove") # Enable execute permissions PAGE_EXECUTE = ctypes.c_ulong(0x10) pfnVirtualProtect = ctypes.windll.kernel32.VirtualProtect - res = pfnVirtualProtect(ctypes.c_void_p(address), - ONE_PAGE, PAGE_EXECUTE, - ctypes.byref(ctypes.c_ulong(0))) + res = pfnVirtualProtect( + ctypes.c_void_p(address), + ONE_PAGE, + PAGE_EXECUTE, + ctypes.byref(ctypes.c_ulong(0)), + ) if not res: raise Exception("Failed VirtualProtect") @@ -135,7 +155,8 @@ def avx_supported(): pfnGetCurrentProcess.restype = ctypes.c_void_p prochandle = ctypes.c_void_p(pfnGetCurrentProcess()) res = ctypes.windll.kernel32.FlushInstructionCache( - prochandle, ctypes.c_void_p(address), ONE_PAGE) + prochandle, ctypes.c_void_p(address), ONE_PAGE + ) if not res: raise Exception("Failed FlushInstructionCache") @@ -153,12 +174,14 @@ def avx_supported(): # Convert the code_str into a function that returns uint func, address = asm_func(code_str) retval = func() - ctypes.windll.kernel32.VirtualFree(ctypes.c_void_p(address), - ctypes.c_size_t(0), ONE_PAGE) + ctypes.windll.kernel32.VirtualFree( + ctypes.c_void_p(address), ctypes.c_size_t(0), ONE_PAGE + ) except Exception as e: - sys.stderr.write('Failed getting the AVX flag on Windows.\n' - 'The original error is: %s\n' % - cpt.get_exception_message(e)) + sys.stderr.write( + 'Failed getting the AVX flag on Windows.\n' + 'The original error is: %s\n' % cpt.get_exception_message(e) + ) return (retval & (1 << avx_bit)) > 0 else: sys.stderr.write('Do not get AVX flag on %s\n' % sysstr) @@ -167,10 +190,10 @@ def avx_supported(): def run_shell_command(cmd): import subprocess - out, err = subprocess.Popen(cmd, - stdout=subprocess.PIPE, - stderr=subprocess.PIPE, - shell=True).communicate() + + out, err = subprocess.Popen( + cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE, shell=True + ).communicate() if err: return None else: @@ -179,8 +202,9 @@ def run_shell_command(cmd): def get_dso_path(core_so, dso_name): if core_so and dso_name: - return run_shell_command("ldd %s|grep %s|awk '{print $3}'" % - (core_so, dso_name)) + return run_shell_command( + "ldd %s|grep %s|awk '{print $3}'" % (core_so, dso_name) + ) else: return None @@ -189,6 +213,7 @@ def load_dso(dso_absolute_path): if dso_absolute_path: try: from ctypes import cdll + cdll.LoadLibrary(dso_absolute_path) except: warnings.warn("Load {} failed".format(dso_absolute_path)) @@ -247,12 +272,14 @@ if platform.system().lower() == 'linux': try: from . import libpaddle + if avx_supported() and not libpaddle.is_compiled_with_avx(): sys.stderr.write( "Hint: Your machine support AVX, but the installed paddlepaddle doesn't have avx core. " "Hence, no-avx core with worse preformance will be imported.\nIf you like, you could " "reinstall paddlepaddle by 'python -m pip install --force-reinstall paddlepaddle-gpu[==version]' " - "to get better performance.\n") + "to get better performance.\n" + ) # assign tensor alias libpaddle.LoDTensor = libpaddle.Tensor @@ -283,6 +310,7 @@ try: from .libpaddle import _Profiler, _ProfilerResult, _RecordEvent from .libpaddle import _set_current_stream from .libpaddle import _get_phi_kernel_name + if sys.platform != 'win32': from .libpaddle import _set_process_pids from .libpaddle import _erase_process_pids @@ -295,12 +323,18 @@ try: except Exception as e: if has_paddle_dy_lib: sys.stderr.write( - 'Error: Can not import paddle core while this file exists: ' + - current_path + os.sep + 'libpaddle.' + dy_lib_suffix + '\n') + 'Error: Can not import paddle core while this file exists: ' + + current_path + + os.sep + + 'libpaddle.' + + dy_lib_suffix + + '\n' + ) if not avx_supported() and libpaddle.is_compiled_with_avx(): sys.stderr.write( "Error: Your machine doesn't support AVX, but the installed PaddlePaddle is avx core, " - "you should reinstall paddlepaddle with no-avx core.\n") + "you should reinstall paddlepaddle with no-avx core.\n" + ) raise e @@ -317,22 +351,26 @@ def set_paddle_custom_device_lib_path(lib_path): # set paddle lib path def set_paddle_lib_path(): - site_dirs = site.getsitepackages() if hasattr( - site, - 'getsitepackages') else [x for x in sys.path if 'site-packages' in x] + site_dirs = ( + site.getsitepackages() + if hasattr(site, 'getsitepackages') + else [x for x in sys.path if 'site-packages' in x] + ) for site_dir in site_dirs: lib_dir = os.path.sep.join([site_dir, 'paddle', 'libs']) if os.path.exists(lib_dir): _set_paddle_lib_path(lib_dir) set_paddle_custom_device_lib_path( - os.path.sep.join([lib_dir, '..', '..', 'paddle-plugins'])) + os.path.sep.join([lib_dir, '..', '..', 'paddle-plugins']) + ) return if hasattr(site, 'USER_SITE'): lib_dir = os.path.sep.join([site.USER_SITE, 'paddle', 'libs']) if os.path.exists(lib_dir): _set_paddle_lib_path(lib_dir) set_paddle_custom_device_lib_path( - os.path.sep.join([lib_dir, '..', '..', 'paddle-plugins'])) + os.path.sep.join([lib_dir, '..', '..', 'paddle-plugins']) + ) set_paddle_lib_path() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_activation.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_activation.py index 82b73609b2e11c83e6c030965cbee34a793066a6..a4054a9bd6dc2ba843267eae1f9a8bc85616720c 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_activation.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_activation.py @@ -22,12 +22,10 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertActivationTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(dims, batch, attrs: List[Dict[str, Any]]): if dims == 1: return np.random.random([32]).astype(np.float32) @@ -41,11 +39,19 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): for dims in [1, 2, 3, 4]: for batch in [1, 4]: for op_type in [ - "relu", "sigmoid", "tanh", "relu6", "elu", "selu", - "softsign", "stanh", "thresholded_relu", "softplus" + "relu", + "sigmoid", + "tanh", + "relu6", + "elu", + "selu", + "softsign", + "stanh", + "thresholded_relu", + "softplus", ]: # few samples to reduce time - #for beta in [-0.2, 0.5, 0.67, 3]: + # for beta in [-0.2, 0.5, 0.67, 3]: # for alpha in [-0.2, 0.5, 0.67, 3]: for beta in [0.67]: for alpha in [0.67]: @@ -62,33 +68,34 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): if op_type == "softplus": dics = [{"beta": beta}] - ops_config = [{ - "op_type": op_type, - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": op_type, + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, dims, batch, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, dims, batch, dics + ) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [1]} @@ -131,19 +138,23 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_anchor_generator.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_anchor_generator.py index 0a2877b9a2327ef248015e16ab64375f09e136e8..1e5fd74879003f6c1bc7366624436b9ad1871522 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_anchor_generator.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_anchor_generator.py @@ -22,60 +22,66 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertAnchorGeneratorTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(batch, attrs: List[Dict[str, Any]]): return np.random.random([batch, 3, 64, 64]).astype(np.float32) for batch in [1, 2, 4]: for anchor_sizes in [[64.0, 128.0, 256.0, 512.0]]: for aspect_ratios in [[0.5, 1, 2], [0.4, 1.2, 3]]: - for variances in [[1.0, 1.0, 1.0, 1.0], - [0.5, 1.0, 0.5, 1.0]]: + for variances in [ + [1.0, 1.0, 1.0, 1.0], + [0.5, 1.0, 0.5, 1.0], + ]: for stride in [[16.0, 16.0], [16.0, 32.0]]: for offset in [0.5, 0.8]: - dics = [{ - "anchor_sizes": anchor_sizes, - "aspect_ratios": aspect_ratios, - "variances": variances, - "stride": stride, - "offset": offset - }] - - ops_config = [{ - "op_type": "anchor_generator", - "op_inputs": { - "Input": ["input_data"] - }, - "op_outputs": { - "Anchors": ["output_anchors"], - "Variances": ["output_variances"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "anchor_sizes": anchor_sizes, + "aspect_ratios": aspect_ratios, + "variances": variances, + "stride": stride, + "offset": offset, + } + ] + + ops_config = [ + { + "op_type": "anchor_generator", + "op_inputs": {"Input": ["input_data"]}, + "op_outputs": { + "Anchors": ["output_anchors"], + "Variances": ["output_variances"], + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, batch, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, batch, dics + ) + ) }, outputs=[ - "output_anchors", "output_variances" - ]) + "output_anchors", + "output_variances", + ], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]} self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]} @@ -100,19 +106,23 @@ class TrtConvertAnchorGeneratorTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_arg_max.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_arg_max.py index 8d01029c78a7d96ddd4c5d1b77f44577277d3998..a19132571468a0214735107d9ac583c2571b09cd 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_arg_max.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_arg_max.py @@ -22,7 +22,6 @@ from typing import List class TrtConvertArgMaxTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: input_shape = program_config.inputs["arg_max_input"].shape axis = program_config.ops[0].attrs["axis"] @@ -33,7 +32,6 @@ class TrtConvertArgMaxTest(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input(rank, batch): dims = [batch] for i in range(rank - 1): @@ -48,36 +46,37 @@ class TrtConvertArgMaxTest(TrtLayerAutoScanTest): self.rank = rank flatten = False dtype = 2 - ops_config = [{ - "op_type": "arg_max", - "op_inputs": { - "X": ["arg_max_input"] - }, - "op_outputs": { - "Out": ["arg_max_out"] - }, - "op_attrs": { - "axis": axis, - "keepdims": keepdims, - "flatten": flatten, - "dtype": dtype + ops_config = [ + { + "op_type": "arg_max", + "op_inputs": {"X": ["arg_max_input"]}, + "op_outputs": {"Out": ["arg_max_out"]}, + "op_attrs": { + "axis": axis, + "keepdims": keepdims, + "flatten": flatten, + "dtype": dtype, + }, } - }] + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "arg_max_input": - TensorConfig(data_gen=partial( - generate_input, rank, batch)) + "arg_max_input": TensorConfig( + data_gen=partial( + generate_input, rank, batch + ) + ) }, - outputs=["arg_max_out"]) + outputs=["arg_max_out"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.rank == 3: self.dynamic_shape.min_input_shape = { @@ -117,19 +116,23 @@ class TrtConvertArgMaxTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_bmm.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_bmm.py index 62bea6fbbc4bc2a876769ceb9e651d925cbaf292..fb5c607b233c1d7eed88b253b404e0287adbaa1a 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_bmm.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_bmm.py @@ -12,20 +12,18 @@ # See the License for the specific language governing permissions and # limitations under the License. -from trt_layer_auto_scan_test import TrtLayerAutoScanTest, SkipReasons +from trt_layer_auto_scan_test import TrtLayerAutoScanTest from program_config import TensorConfig, ProgramConfig import numpy as np import paddle.inference as paddle_infer from functools import partial -from typing import Optional, List, Callable, Dict, Any, Set +from typing import List import unittest import os class TrtConvertBmmTest_dynamic(TrtLayerAutoScanTest): - def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -33,48 +31,47 @@ class TrtConvertBmmTest_dynamic(TrtLayerAutoScanTest): input1_shape = [batch, 350, 75] input2_shape = [batch, 75, 25] dics = [{}] - ops_config = [{ - "op_type": "bmm", - "op_inputs": { - "X": ["input1_data"], - "Y": ["input2_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "bmm", + "op_inputs": {"X": ["input1_data"], "Y": ["input2_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1_data": - TensorConfig( - data_gen=partial(generate_input, input1_shape)), - "input2_data": - TensorConfig(data_gen=partial(generate_input, input2_shape)) + "input1_data": TensorConfig( + data_gen=partial(generate_input, input1_shape) + ), + "input2_data": TensorConfig( + data_gen=partial(generate_input, input2_shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input1_data": [10, 350, 75], - "input2_data": [10, 75, 25] + "input2_data": [10, 75, 25], } self.dynamic_shape.max_input_shape = { "input1_data": [100, 350, 75], - "input2_data": [100, 75, 25] + "input2_data": [100, 75, 25], } self.dynamic_shape.opt_input_shape = { "input1_data": [15, 350, 75], - "input2_data": [15, 75, 25] + "input2_data": [15, 75, 25], } def clear_dynamic_shape(): @@ -95,25 +92,29 @@ class TrtConvertBmmTest_dynamic(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # The output has little diff between gpu and trt in CI-Windows-Inference tol_fp32 = 1e-4 tol_half = 1e-4 - if (os.name == 'nt'): + if os.name == 'nt': tol_fp32 = 1e-2 tol_half = 1e-2 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), tol_fp32 + attrs, True + ), tol_fp32 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), tol_half + attrs, True + ), tol_half def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.py index aec2f3efd4f23dbaa82361491cb949009ed063e9..c8b6688aedcb0d4871163def42d510d3b1e1aee6 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_clip.py @@ -22,12 +22,10 @@ import unittest class TrtConvertClipTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(dims, batch, attrs: List[Dict[str, Any]]): if dims == 1: return np.ones([32]).astype(np.float32) @@ -46,52 +44,52 @@ class TrtConvertClipTest(TrtLayerAutoScanTest): for dims in [1, 2, 3, 4]: for batch in [1, 4]: - for op_inputs in [{ - "X": ["input_data"] - }, { - "X": ["input_data"], - "Min": ["Min_"], - "Max": ["Max_"] - }]: + for op_inputs in [ + {"X": ["input_data"]}, + {"X": ["input_data"], "Min": ["Min_"], "Max": ["Max_"]}, + ]: self.input_num = len(op_inputs) self.dims = dims - dics = [{ - "min": np.random.uniform(1, 10), - "max": np.random.uniform(10, 20) - }, { - "op_inputs": op_inputs - }] - ops_config = [{ - "op_type": "clip", - "op_inputs": op_inputs, - "op_outputs": { - "Out": ["output_data"] + dics = [ + { + "min": np.random.uniform(1, 10), + "max": np.random.uniform(10, 20), }, - "op_attrs": dics[0] - }] + {"op_inputs": op_inputs}, + ] + ops_config = [ + { + "op_type": "clip", + "op_inputs": op_inputs, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "Min_": - TensorConfig( - data_gen=partial(generate_weight1, dics)), - "Max_": - TensorConfig( - data_gen=partial(generate_weight2, dics)) + "Min_": TensorConfig( + data_gen=partial(generate_weight1, dics) + ), + "Max_": TensorConfig( + data_gen=partial(generate_weight2, dics) + ), }, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, dims, batch, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, dims, batch, dics + ) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs(self, program_config): - def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [1]} @@ -135,19 +133,23 @@ class TrtConvertClipTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_concat.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_concat.py index e8c9a65bbfc939ebbb57d8b080c254782ca04a2d..2945648c8da5633524df5070b0a69c0bdf5dcffa 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_concat.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_concat.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertConcatTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -31,14 +30,13 @@ class TrtConvertConcatTest(TrtLayerAutoScanTest): attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] - #The input dimension should be less than or equal to the set axis. + # The input dimension should be less than or equal to the set axis. if len(inputs['concat_input1'].shape) <= attrs[0]['axis']: return False return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]], batch): if self.dims == 4: return np.ones([batch, 3, 24, 24]).astype(np.float32) @@ -79,58 +77,83 @@ class TrtConvertConcatTest(TrtLayerAutoScanTest): self.num_input = num_input self.dims = dims dics = [{"axis": axis}, {}] - dics_intput = [{ - "X": - ["concat_input1", "concat_input2", "concat_input3"], - "AxisTensor": ["AxisTensor"], - }, { - "X": - ["concat_input1", "concat_input2", "concat_input3"] - }] - dics_inputs = [{ - "concat_input1": - TensorConfig( - data_gen=partial(generate_input1, dics, batch)), - "concat_input2": - TensorConfig( - data_gen=partial(generate_input2, dics, batch)), - "concat_input3": - TensorConfig( - data_gen=partial(generate_input3, dics, batch)), - "AxisTensor": - TensorConfig( - data_gen=partial(generate_weight1, dics)) - }, { - "concat_input1": - TensorConfig( - data_gen=partial(generate_input1, dics, batch)), - "concat_input2": - TensorConfig( - data_gen=partial(generate_input2, dics, batch)), - "concat_input3": - TensorConfig( - data_gen=partial(generate_input3, dics, batch)) - }] - ops_config = [{ - "op_type": "concat", - "op_inputs": dics_intput[num_input], - "op_outputs": { - "Out": ["concat_output"] + dics_intput = [ + { + "X": [ + "concat_input1", + "concat_input2", + "concat_input3", + ], + "AxisTensor": ["AxisTensor"], + }, + { + "X": [ + "concat_input1", + "concat_input2", + "concat_input3", + ] + }, + ] + dics_inputs = [ + { + "concat_input1": TensorConfig( + data_gen=partial( + generate_input1, dics, batch + ) + ), + "concat_input2": TensorConfig( + data_gen=partial( + generate_input2, dics, batch + ) + ), + "concat_input3": TensorConfig( + data_gen=partial( + generate_input3, dics, batch + ) + ), + "AxisTensor": TensorConfig( + data_gen=partial(generate_weight1, dics) + ), + }, + { + "concat_input1": TensorConfig( + data_gen=partial( + generate_input1, dics, batch + ) + ), + "concat_input2": TensorConfig( + data_gen=partial( + generate_input2, dics, batch + ) + ), + "concat_input3": TensorConfig( + data_gen=partial( + generate_input3, dics, batch + ) + ), }, - "op_attrs": dics[0] - }] + ] + ops_config = [ + { + "op_type": "concat", + "op_inputs": dics_intput[num_input], + "op_outputs": {"Out": ["concat_output"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs=dics_inputs[num_input], - outputs=["concat_output"]) + outputs=["concat_output"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.num_input == 0: if self.dims == 4: @@ -138,76 +161,76 @@ class TrtConvertConcatTest(TrtLayerAutoScanTest): "concat_input1": [1, 3, 24, 24], "concat_input2": [1, 3, 24, 24], "concat_input3": [1, 3, 24, 24], - "AxisTensor": [1] + "AxisTensor": [1], } self.dynamic_shape.max_input_shape = { "concat_input1": [4, 3, 48, 48], "concat_input2": [4, 3, 48, 48], "concat_input3": [4, 3, 48, 48], - "AxisTensor": [1] + "AxisTensor": [1], } self.dynamic_shape.opt_input_shape = { "concat_input1": [1, 3, 24, 24], "concat_input2": [1, 3, 24, 24], "concat_input3": [1, 3, 24, 24], - "AxisTensor": [1] + "AxisTensor": [1], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "concat_input1": [1, 3, 24], "concat_input2": [1, 3, 24], "concat_input3": [1, 3, 24], - "AxisTensor": [1] + "AxisTensor": [1], } self.dynamic_shape.max_input_shape = { "concat_input1": [4, 12, 48], "concat_input2": [4, 12, 48], "concat_input3": [4, 12, 48], - "AxisTensor": [1] + "AxisTensor": [1], } self.dynamic_shape.opt_input_shape = { "concat_input1": [1, 3, 24], "concat_input2": [1, 3, 24], "concat_input3": [1, 3, 24], - "AxisTensor": [1] + "AxisTensor": [1], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "concat_input1": [1, 24], "concat_input2": [1, 24], "concat_input3": [1, 24], - "AxisTensor": [1] + "AxisTensor": [1], } self.dynamic_shape.max_input_shape = { "concat_input1": [4, 48], "concat_input2": [4, 48], "concat_input3": [4, 48], - "AxisTensor": [1] + "AxisTensor": [1], } self.dynamic_shape.opt_input_shape = { "concat_input1": [1, 24], "concat_input2": [1, 24], "concat_input3": [1, 24], - "AxisTensor": [1] + "AxisTensor": [1], } elif self.dims == 1: self.dynamic_shape.min_input_shape = { "concat_input1": [24], "concat_input2": [24], "concat_input3": [24], - "AxisTensor": [0] + "AxisTensor": [0], } self.dynamic_shape.max_input_shape = { "concat_input1": [48], "concat_input2": [48], "concat_input3": [48], - "AxisTensor": [0] + "AxisTensor": [0], } self.dynamic_shape.opt_input_shape = { "concat_input1": [24], "concat_input2": [24], "concat_input3": [24], - "AxisTensor": [0] + "AxisTensor": [0], } elif self.num_input == 1: if self.dims == 4: @@ -219,60 +242,60 @@ class TrtConvertConcatTest(TrtLayerAutoScanTest): self.dynamic_shape.max_input_shape = { "concat_input1": [4, 3, 48, 48], "concat_input2": [4, 3, 48, 48], - "concat_input3": [4, 3, 48, 48] + "concat_input3": [4, 3, 48, 48], } self.dynamic_shape.opt_input_shape = { "concat_input1": [1, 3, 24, 24], "concat_input2": [1, 3, 24, 24], - "concat_input3": [1, 3, 24, 24] + "concat_input3": [1, 3, 24, 24], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "concat_input1": [1, 3, 24], "concat_input2": [1, 3, 24], - "concat_input3": [1, 3, 24] + "concat_input3": [1, 3, 24], } self.dynamic_shape.max_input_shape = { "concat_input1": [4, 12, 48], "concat_input2": [4, 12, 48], - "concat_input3": [4, 12, 48] + "concat_input3": [4, 12, 48], } self.dynamic_shape.opt_input_shape = { "concat_input1": [1, 3, 24], "concat_input2": [1, 3, 24], - "concat_input3": [1, 3, 24] + "concat_input3": [1, 3, 24], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "concat_input1": [1, 24], "concat_input2": [1, 24], - "concat_input3": [1, 24] + "concat_input3": [1, 24], } self.dynamic_shape.max_input_shape = { "concat_input1": [4, 48], "concat_input2": [4, 48], - "concat_input3": [4, 48] + "concat_input3": [4, 48], } self.dynamic_shape.opt_input_shape = { "concat_input1": [1, 24], "concat_input2": [1, 24], - "concat_input3": [1, 24] + "concat_input3": [1, 24], } elif self.dims == 1: self.dynamic_shape.min_input_shape = { "concat_input1": [24], "concat_input2": [24], - "concat_input3": [24] + "concat_input3": [24], } self.dynamic_shape.max_input_shape = { "concat_input1": [48], "concat_input2": [48], - "concat_input3": [48] + "concat_input3": [48], } self.dynamic_shape.opt_input_shape = { "concat_input1": [24], "concat_input2": [24], - "concat_input3": [24] + "concat_input3": [24], } def clear_dynamic_shape(): @@ -296,29 +319,33 @@ class TrtConvertConcatTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if len(program_config.inputs) == 4: return True return False - self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT, - "INPUT AxisTensor NOT SUPPORT") + self.add_skip_case( + teller1, SkipReasons.TRT_NOT_SUPPORT, "INPUT AxisTensor NOT SUPPORT" + ) def test(self): self.add_skip_trt_case() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_transpose.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_transpose.py index ce69d9d7395a0618516a6bd69b839dd5682abe1d..bbfaae6514da093356f641a161e9b498c3208d5e 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_transpose.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_conv2d_transpose.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -30,8 +29,10 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest): program_config.ops[i].attrs for i in range(len(program_config.ops)) ] - if inputs['input_data'].shape[ - 1] != weights['conv2d_weight'].shape[1] * attrs[0]['groups']: + if ( + inputs['input_data'].shape[1] + != weights['conv2d_weight'].shape[1] * attrs[0]['groups'] + ): return False if inputs['input_data'].shape[1] != weights['conv2d_weight'].shape[0]: @@ -54,12 +55,13 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest): def generate_weight1(num_channels, attrs: List[Dict[str, Any]]): if attrs[0]['groups'] == 1: - return np.random.random([num_channels, num_channels, 3, - 3]).astype(np.float32) + return np.random.random( + [num_channels, num_channels, 3, 3] + ).astype(np.float32) else: return np.random.random( - [num_channels, int(num_channels / 2), 3, - 3]).astype(np.float32) + [num_channels, int(num_channels / 2), 3, 3] + ).astype(np.float32) for num_channels in [2, 4, 6]: for batch in [1, 4]: @@ -67,99 +69,113 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest): for paddings in [[0, 3], [1, 2, 3, 4]]: for groups in [2]: for padding_algorithm in [ - 'EXPLICIT', 'SAME', 'VALID' + 'EXPLICIT', + 'SAME', + 'VALID', ]: for dilations in [[2, 2], [1, 2]]: for data_format in ['NCHW']: self.num_channels = num_channels - dics = [{ - "data_fromat": data_format, - "dilations": dilations, - "padding_algorithm": - padding_algorithm, - "groups": groups, - "paddings": paddings, - "strides": strides, - "data_format": data_format, - "output_size": [], - "output_padding": [] - }] - - ops_config = [{ - "op_type": "conv2d_transpose", - "op_inputs": { - "Input": ["input_data"], - "Filter": ["conv2d_weight"] - }, - "op_outputs": { - "Output": ["output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "data_fromat": data_format, + "dilations": dilations, + "padding_algorithm": padding_algorithm, + "groups": groups, + "paddings": paddings, + "strides": strides, + "data_format": data_format, + "output_size": [], + "output_padding": [], + } + ] + + ops_config = [ + { + "op_type": "conv2d_transpose", + "op_inputs": { + "Input": ["input_data"], + "Filter": ["conv2d_weight"], + }, + "op_outputs": { + "Output": ["output_data"] + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config( - ops_config) + ops_config + ) program_config = ProgramConfig( ops=ops, weights={ - "conv2d_weight": - TensorConfig(data_gen=partial( - generate_weight1, - num_channels, dics)) + "conv2d_weight": TensorConfig( + data_gen=partial( + generate_weight1, + num_channels, + dics, + ) + ) }, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, batch, - num_channels, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, + batch, + num_channels, + dics, + ) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.num_channels == 2: self.dynamic_shape.min_input_shape = { "input_data": [1, 2, 32, 32], - "output_data": [1, 24, 32, 32] + "output_data": [1, 24, 32, 32], } self.dynamic_shape.max_input_shape = { "input_data": [4, 2, 64, 64], - "output_data": [4, 24, 64, 64] + "output_data": [4, 24, 64, 64], } self.dynamic_shape.opt_input_shape = { "input_data": [1, 2, 64, 64], - "output_data": [1, 24, 64, 64] + "output_data": [1, 24, 64, 64], } elif self.num_channels == 4: self.dynamic_shape.min_input_shape = { "input_data": [1, 4, 32, 32], - "output_data": [1, 24, 32, 32] + "output_data": [1, 24, 32, 32], } self.dynamic_shape.max_input_shape = { "input_data": [4, 4, 64, 64], - "output_data": [4, 24, 64, 64] + "output_data": [4, 24, 64, 64], } self.dynamic_shape.opt_input_shape = { "input_data": [1, 4, 64, 64], - "output_data": [1, 24, 64, 64] + "output_data": [1, 24, 64, 64], } else: self.dynamic_shape.min_input_shape = { "input_data": [1, 6, 32, 32], - "output_data": [1, 24, 32, 32] + "output_data": [1, 24, 32, 32], } self.dynamic_shape.max_input_shape = { "input_data": [4, 6, 64, 64], - "output_data": [4, 24, 64, 64] + "output_data": [4, 24, 64, 64], } self.dynamic_shape.opt_input_shape = { "input_data": [1, 6, 64, 64], - "output_data": [1, 24, 64, 64] + "output_data": [1, 24, 64, 64], } def clear_dynamic_shape(): @@ -178,10 +194,12 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-3) + attrs, False + ), (1e-3, 1e-3) # self.trt_param.precision = paddle_infer.PrecisionType.Int8 # yield self.create_inference_config(), generate_trt_nodes_num( # attrs, False), (1e-5, 1e-5) @@ -190,24 +208,26 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest): generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-3) + attrs, True + ), (1e-3, 1e-3) # self.trt_param.precision = paddle_infer.PrecisionType.Int8 # yield self.create_inference_config(), generate_trt_nodes_num( # attrs, True), (1e-5, 1e-5) def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if self.trt_param.precision == paddle_infer.PrecisionType.Int8: return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_IMPLEMENTED, - "When precisionType is int8 without relu op, output is different between Trt and Paddle." + teller1, + SkipReasons.TRT_NOT_IMPLEMENTED, + "When precisionType is int8 without relu op, output is different between Trt and Paddle.", ) def test(self): @@ -221,7 +241,6 @@ class TrtConvertConv2dTransposeTest(TrtLayerAutoScanTest): # Special case class TrtConvertConv2dTransposeTest2(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: ver = paddle_infer.get_trt_compile_version() if ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 7000: @@ -241,49 +260,52 @@ class TrtConvertConv2dTransposeTest2(TrtLayerAutoScanTest): batch = 1 self.num_channels = num_channels - dics = [{ - "data_fromat": 'NCHW', - "dilations": [1, 1], - "padding_algorithm": 'EXPLICIT', - "groups": 1, - "paddings": [1, 1], - "strides": [2, 2], - "output_padding": [1, 1], - "output_size": [], - }] - - ops_config = [{ - "op_type": "conv2d_transpose", - "op_inputs": { - "Input": ["input_data"], - "Filter": ["conv2d_weight"] - }, - "op_outputs": { - "Output": ["output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "data_fromat": 'NCHW', + "dilations": [1, 1], + "padding_algorithm": 'EXPLICIT', + "groups": 1, + "paddings": [1, 1], + "strides": [2, 2], + "output_padding": [1, 1], + "output_size": [], + } + ] + + ops_config = [ + { + "op_type": "conv2d_transpose", + "op_inputs": { + "Input": ["input_data"], + "Filter": ["conv2d_weight"], + }, + "op_outputs": {"Output": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "conv2d_weight": - TensorConfig( - data_gen=partial(generate_weight1, num_channels, dics)) + "conv2d_weight": TensorConfig( + data_gen=partial(generate_weight1, num_channels, dics) + ) }, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1, batch, - num_channels, dics)) + "input_data": TensorConfig( + data_gen=partial(generate_input1, batch, num_channels, dics) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 128, 20, 30], @@ -311,19 +333,23 @@ class TrtConvertConv2dTransposeTest2(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-4 + attrs, False + ), 1e-4 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e0, 1e-3) + attrs, False + ), (1e0, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-4 + attrs, True + ), 1e-4 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e0, 1e-3) + attrs, True + ), (1e0, 1e-3) def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_dropout.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_dropout.py index 5d8e93ef984f6260d1863a4cea3167c9d3757534..94a94371247534619875739991d94b47a3d1fa82 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_dropout.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_dropout.py @@ -22,12 +22,10 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertDropoutTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(dims, batch, attrs: List[Dict[str, Any]]): if dims == 1: return np.ones([64]).astype(np.float32) @@ -42,47 +40,57 @@ class TrtConvertDropoutTest(TrtLayerAutoScanTest): for batch in [1, 2, 4]: for fix_seed in [False, True]: for dropout_implementation in [ - "downgrade_in_infer", "upscale_in_train" + "downgrade_in_infer", + "upscale_in_train", ]: for dropout_prob in [np.random.random()]: for seed in [0, 64, 128, 512]: self.dims = dims - dics = [{ - "fix_seed": fix_seed, - "dropout_implementation": - dropout_implementation, - "dropout_prob": dropout_prob, - "seed": seed, - "is_test": True - }] - - ops_config = [{ - "op_type": "dropout", - "op_inputs": { - "X": ["input_data"], - }, - "op_outputs": { - "Out": ["dropout_output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "fix_seed": fix_seed, + "dropout_implementation": dropout_implementation, + "dropout_prob": dropout_prob, + "seed": seed, + "is_test": True, + } + ] + + ops_config = [ + { + "op_type": "dropout", + "op_inputs": { + "X": ["input_data"], + }, + "op_outputs": { + "Out": ["dropout_output_data"] + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, dims, batch, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, + dims, + batch, + dics, + ) + ) }, - outputs=["dropout_output_data"]) + outputs=["dropout_output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [1]} @@ -128,19 +136,23 @@ class TrtConvertDropoutTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py index 90d2c728c0477cedda10a98a5a94fa12d3cb1aa7..e084f2791e57a753f4d139442a49a48d4e6824b6 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_elementwise.py @@ -24,12 +24,10 @@ from typing import Optional, List, Callable, Dict, Any, Set # This is the special test case with weight including batch dimension # I don't want to mess up the code written by others, so I wrote a class specifically class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -39,44 +37,50 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest): for batch in [1, 4]: for shape in [[batch, 32, 16, 32]]: for op_type in [ - "elementwise_add", "elementwise_mul", "elementwise_sub", - "elementwise_div", "elementwise_pow", "elementwise_min", - "elementwise_max" + "elementwise_add", + "elementwise_mul", + "elementwise_sub", + "elementwise_div", + "elementwise_pow", + "elementwise_min", + "elementwise_max", ]: for axis in [-1]: self.dims = len(shape) dics = [{"axis": axis}] - ops_config = [{ - "op_type": op_type, - "op_inputs": { - "X": ["input_data"], - "Y": ["weight"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": op_type, + "op_inputs": { + "X": ["input_data"], + "Y": ["weight"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "weight": - TensorConfig(data_gen=partial(generate_weight)) + "weight": TensorConfig( + data_gen=partial(generate_weight) + ) }, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, shape)), + "input_data": TensorConfig( + data_gen=partial(generate_input, shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The input.dims[1] must be equal to the weight's length. if self.dims == 4: @@ -106,19 +110,23 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-5) + attrs, True + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-3, 1e-3) + attrs, True + ), (1e-3, 1e-3) def add_skip_trt_case(self): pass @@ -130,12 +138,10 @@ class TrtConvertElementwiseTest_one_input_special_case0(TrtLayerAutoScanTest): # This is the special test case class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -144,44 +150,47 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest): for shape in [[32]]: for op_type in [ - "elementwise_add", "elementwise_mul", "elementwise_sub", - "elementwise_div", "elementwise_pow", "elementwise_min", - "elementwise_max" + "elementwise_add", + "elementwise_mul", + "elementwise_sub", + "elementwise_div", + "elementwise_pow", + "elementwise_min", + "elementwise_max", ]: for axis in [-1]: self.dims = len(shape) dics = [{"axis": axis}] - ops_config = [{ - "op_type": op_type, - "op_inputs": { - "X": ["input_data"], - "Y": ["weight"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": op_type, + "op_inputs": {"X": ["input_data"], "Y": ["weight"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "weight": - TensorConfig(data_gen=partial(generate_weight)) + "weight": TensorConfig( + data_gen=partial(generate_weight) + ) }, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, shape)), + "input_data": TensorConfig( + data_gen=partial(generate_input, shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [32]} self.dynamic_shape.max_input_shape = {"input_data": [64]} @@ -205,19 +214,23 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-5) + attrs, True + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-3, 1e-3) + attrs, True + ), (1e-3, 1e-3) def add_skip_trt_case(self): pass @@ -228,12 +241,10 @@ class TrtConvertElementwiseTest_one_input_special_case1(TrtLayerAutoScanTest): class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -241,47 +252,57 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest): return np.random.randn(32).astype(np.float32) for batch in [1, 4]: - for shape in [[32], [batch, 32], [batch, 32, 32], - [batch, 32, 16, 32]]: + for shape in [ + [32], + [batch, 32], + [batch, 32, 32], + [batch, 32, 16, 32], + ]: for op_type in [ - "elementwise_add", "elementwise_mul", "elementwise_sub", - "elementwise_div", "elementwise_pow", "elementwise_min", - "elementwise_max" + "elementwise_add", + "elementwise_mul", + "elementwise_sub", + "elementwise_div", + "elementwise_pow", + "elementwise_min", + "elementwise_max", ]: for axis in [-1 if len(shape) == 1 else 1]: self.dims = len(shape) dics = [{"axis": axis}] - ops_config = [{ - "op_type": op_type, - "op_inputs": { - "X": ["input_data"], - "Y": ["weight"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": op_type, + "op_inputs": { + "X": ["input_data"], + "Y": ["weight"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "weight": - TensorConfig(data_gen=partial(generate_weight)) + "weight": TensorConfig( + data_gen=partial(generate_weight) + ) }, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, shape)), + "input_data": TensorConfig( + data_gen=partial(generate_input, shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The input.dims[1] must be equal to the weight's length. if self.dims == 1: @@ -325,19 +346,23 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-5) + attrs, True + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-3, 1e-3) + attrs, True + ), (1e-3, 1e-3) def add_skip_trt_case(self): pass @@ -348,108 +373,112 @@ class TrtConvertElementwiseTest_one_input(TrtLayerAutoScanTest): class TrtConvertElementwiseTest_two_input_without_broadcast( - TrtLayerAutoScanTest): - + TrtLayerAutoScanTest +): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) - for shape in [[4], [4, 32], [2, 64, 32], [1, 8, 16, 32]]: + for shape in [[4], [4, 32], [2, 32, 16], [1, 8, 16, 32]]: for op_type in [ - "elementwise_add", "elementwise_mul", "elementwise_sub", - "elementwise_div", "elementwise_pow", "elementwise_min", - "elementwise_max" + "elementwise_add", + "elementwise_mul", + "elementwise_sub", + "elementwise_div", + "elementwise_pow", + "elementwise_min", + "elementwise_max", ]: for axis in [0, -1]: self.dims = len(shape) dics = [{"axis": axis}] - ops_config = [{ - "op_type": op_type, - "op_inputs": { - "X": ["input_data1"], - "Y": ["input_data2"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": op_type, + "op_inputs": { + "X": ["input_data1"], + "Y": ["input_data2"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data1": - TensorConfig( - data_gen=partial(generate_input, shape)), - "input_data2": - TensorConfig( - data_gen=partial(generate_input, shape)) + "input_data1": TensorConfig( + data_gen=partial(generate_input, shape) + ), + "input_data2": TensorConfig( + data_gen=partial(generate_input, shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = { "input_data1": [1], - "input_data2": [1] + "input_data2": [1], } self.dynamic_shape.max_input_shape = { "input_data1": [128], - "input_data2": [128] + "input_data2": [128], } self.dynamic_shape.opt_input_shape = { "input_data1": [32], - "input_data2": [32] + "input_data2": [32], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4], - "input_data2": [1, 4] + "input_data2": [1, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [128, 256], - "input_data2": [128, 256] + "input_data2": [128, 256], } self.dynamic_shape.opt_input_shape = { "input_data1": [32, 64], - "input_data2": [32, 64] + "input_data2": [32, 64], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4, 4], - "input_data2": [1, 4, 4] + "input_data2": [1, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [128, 128, 256], - "input_data2": [128, 128, 256] + "input_data2": [128, 128, 256], } self.dynamic_shape.opt_input_shape = { - "input_data1": [2, 64, 64], - "input_data2": [2, 64, 64] + "input_data1": [2, 32, 16], + "input_data2": [2, 32, 16], } elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 4, 4, 4], - "input_data2": [1, 4, 4, 4] + "input_data2": [1, 4, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [8, 128, 64, 128], - "input_data2": [8, 128, 64, 128] + "input_data2": [8, 128, 64, 128], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 64, 32, 32], - "input_data2": [2, 64, 32, 32] + "input_data2": [2, 64, 32, 32], } def clear_dynamic_shape(): @@ -470,10 +499,12 @@ class TrtConvertElementwiseTest_two_input_without_broadcast( clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) @@ -491,7 +522,6 @@ class TrtConvertElementwiseTest_two_input_without_broadcast( class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs if len(inputs['input_data1'].shape) != len(inputs['input_data2'].shape): @@ -500,7 +530,6 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -512,8 +541,12 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest): input2_shape5_list = [[32], [2, 1, 32], [4, 1, 1, 32]] input2_shape6_list = [[1, 32], [1, 32], [1, 1, 1, 32]] input2_shape_list = [ - input2_shape1_list, input2_shape2_list, input2_shape3_list, - input2_shape4_list, input2_shape5_list, input2_shape6_list + input2_shape1_list, + input2_shape2_list, + input2_shape3_list, + input2_shape4_list, + input2_shape5_list, + input2_shape6_list, ] axis1_list = [[-1], [1, -1], [1, -1]] axis2_list = [[-1], [0], [0]] @@ -522,8 +555,12 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest): axis5_list = [[-1, 1], [-1, 0], [-1, 0]] axis6_list = [[-1, 0], [-1, 1], [-1, 0]] axis_list = [ - axis1_list, axis2_list, axis3_list, axis4_list, axis5_list, - axis6_list + axis1_list, + axis2_list, + axis3_list, + axis4_list, + axis5_list, + axis6_list, ] for i in range(3): @@ -531,66 +568,75 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest): for j in range(6): input2_shape = input2_shape_list[j][i] for op_type in [ - "elementwise_add", - "elementwise_mul", - "elementwise_sub", - "elementwise_div", - "elementwise_pow", - "elementwise_min", - "elementwise_max", + "elementwise_add", + "elementwise_mul", + "elementwise_sub", + "elementwise_div", + "elementwise_pow", + "elementwise_min", + "elementwise_max", ]: for axis in axis_list[j][i]: self.shape1 = input1_shape self.shape2 = input2_shape dics = [{"axis": axis}] - ops_config = [{ - "op_type": op_type, - "op_inputs": { - "X": ["input_data1"], - "Y": ["input_data2"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": op_type, + "op_inputs": { + "X": ["input_data1"], + "Y": ["input_data2"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data1": - TensorConfig(data_gen=partial( - generate_input, input1_shape)), - "input_data2": - TensorConfig(data_gen=partial( - generate_input, input2_shape)) + "input_data1": TensorConfig( + data_gen=partial( + generate_input, input1_shape + ) + ), + "input_data2": TensorConfig( + data_gen=partial( + generate_input, input2_shape + ) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): - max_shape = [[128], [128, 128], [128, 128, 128], - [128, 128, 128, 128]] + max_shape = [ + [128], + [128, 128], + [128, 128, 128], + [128, 128, 128, 128], + ] min_shape = [[1], [1, 1], [1, 1, 1], [1, 1, 1, 1]] opt_shape = [[32], [32, 32], [32, 32, 32], [32, 32, 32, 32]] self.dynamic_shape.min_input_shape = { "input_data1": min_shape[len(self.shape1) - 1], - "input_data2": min_shape[len(self.shape2) - 1] + "input_data2": min_shape[len(self.shape2) - 1], } self.dynamic_shape.max_input_shape = { "input_data1": max_shape[len(self.shape1) - 1], - "input_data2": max_shape[len(self.shape2) - 1] + "input_data2": max_shape[len(self.shape2) - 1], } self.dynamic_shape.opt_input_shape = { "input_data1": opt_shape[len(self.shape1) - 1], - "input_data2": opt_shape[len(self.shape2) - 1] + "input_data2": opt_shape[len(self.shape2) - 1], } def clear_dynamic_shape(): @@ -626,12 +672,10 @@ class TrtConvertElementwiseTest_two_input_with_broadcast(TrtLayerAutoScanTest): class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -640,52 +684,58 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest): return np.random.rand(32).astype(np.float32) for batch in [1, 2, 4]: - for shape in [[32], [batch, 32], [batch, 32, 32], - [batch, 32, 16, 32]]: + for shape in [ + [32], + [batch, 32], + [batch, 32, 32], + [batch, 32, 16, 32], + ]: for op_type in [ - "elementwise_add", - "elementwise_mul", - "elementwise_sub", - "elementwise_div", - "elementwise_pow", - "elementwise_min", - "elementwise_max", + "elementwise_add", + "elementwise_mul", + "elementwise_sub", + "elementwise_div", + "elementwise_pow", + "elementwise_min", + "elementwise_max", ]: self.op_type = op_type for axis in [-1 if len(shape) == 1 else 1]: self.dims = len(shape) dics = [{"axis": axis}] - ops_config = [{ - "op_type": op_type, - "op_inputs": { - "X": ["weight"], - "Y": ["input_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": op_type, + "op_inputs": { + "X": ["weight"], + "Y": ["input_data"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "weight": - TensorConfig(data_gen=partial(generate_weight)) + "weight": TensorConfig( + data_gen=partial(generate_weight) + ) }, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, shape)), + "input_data": TensorConfig( + data_gen=partial(generate_input, shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The input.dims[1] must be equal to the weight's length. if self.dims == 1: diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_equal.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_equal.py index 285ac3f2202d7370ed51f4e46a09d913179d2a9e..8612acc51acbda9ae66c2f1605f1b6f32c6d557c 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_equal.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_equal.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) @@ -35,7 +34,6 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -44,86 +42,84 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest): for axis in [-1 if len(shape) == 1 else 1]: self.dims = len(shape) dics = [{"axis": axis}, {"in_dtype": 0, "out_dtype": 5}] - ops_config = [{ - "op_type": "equal", - "op_inputs": { - "X": ["input_data1"], - "Y": ["input_data2"] - }, - "op_outputs": { - "Out": ["compare_output_data"] + ops_config = [ + { + "op_type": "equal", + "op_inputs": { + "X": ["input_data1"], + "Y": ["input_data2"], + }, + "op_outputs": {"Out": ["compare_output_data"]}, + "op_attrs": dics[0], }, - "op_attrs": dics[0] - }, { - "op_type": "cast", - "op_inputs": { - "X": ["compare_output_data"] + { + "op_type": "cast", + "op_inputs": {"X": ["compare_output_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[1], }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[1] - }] + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data1": - TensorConfig( - data_gen=partial(generate_input, shape)), - "input_data2": - TensorConfig( - data_gen=partial(generate_input, shape)) + "input_data1": TensorConfig( + data_gen=partial(generate_input, shape) + ), + "input_data2": TensorConfig( + data_gen=partial(generate_input, shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The input.dims[1] must be equal to the weight's length. if self.dims == 2: self.dynamic_shape.min_input_shape = { "input_data1": [1, 1], - "input_data2": [1, 1] + "input_data2": [1, 1], } self.dynamic_shape.max_input_shape = { "input_data1": [4, 1], - "input_data2": [4, 1] + "input_data2": [4, 1], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 1], - "input_data2": [2, 1] + "input_data2": [2, 1], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "input_data1": [1, 1, 4], - "input_data2": [1, 1, 4] + "input_data2": [1, 1, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [4, 1, 256], - "input_data2": [1, 1, 256] + "input_data2": [1, 1, 256], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 1, 16], - "input_data2": [2, 1, 16] + "input_data2": [2, 1, 16], } elif self.dims == 4: self.dynamic_shape.min_input_shape = { "input_data1": [1, 1, 4, 4], - "input_data2": [1, 1, 4, 4] + "input_data2": [1, 1, 4, 4], } self.dynamic_shape.max_input_shape = { "input_data1": [4, 1, 128, 256], - "input_data2": [4, 1, 128, 256] + "input_data2": [4, 1, 128, 256], } self.dynamic_shape.opt_input_shape = { "input_data1": [2, 1, 32, 16], - "input_data2": [2, 1, 32, 16] + "input_data2": [2, 1, 32, 16], } def clear_dynamic_shape(): @@ -144,19 +140,23 @@ class TrtConvertElementwiseTest_one_input_corner_case(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fc.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fc.py index 0eb88b5c019f141a69d4fca98305174a21e46caf..c8c21c4174cdd9bcd38a5b169371dbf7943dac47 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fc.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fc.py @@ -23,10 +23,9 @@ import os class TrtConvertFcTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: # The output has diff between gpu and trt in CI windows - if (os.name == 'nt'): + if os.name == 'nt': return False return True @@ -34,12 +33,14 @@ class TrtConvertFcTest(TrtLayerAutoScanTest): self.trt_param.workspace_size = 1073741824 def generate_input1(batch, attrs: List[Dict[str, Any]]): - return np.random.random([batch, 3, 64, (int)(attrs[0]["m"] / 2), - 2]).astype(np.float32) + return np.random.random( + [batch, 3, 64, (int)(attrs[0]["m"] / 2), 2] + ).astype(np.float32) def generate_w(batch, attrs: List[Dict[str, Any]]): - return np.random.random([attrs[0]["m"], - attrs[0]["n"]]).astype(np.float32) + return np.random.random([attrs[0]["m"], attrs[0]["n"]]).astype( + np.float32 + ) def generate_bias(batch, attrs: List[Dict[str, Any]]): return np.random.random([attrs[0]["n"]]).astype(np.float32) @@ -53,7 +54,7 @@ class TrtConvertFcTest(TrtLayerAutoScanTest): "m": m, "n": n, }, - {} + {}, ] ops_config = [ @@ -62,12 +63,10 @@ class TrtConvertFcTest(TrtLayerAutoScanTest): "op_inputs": { "Input": ["input_data"], "W": ["w_data"], - "Bias": ["bias_data"] - }, - "op_outputs": { - "Out": ["output_data"] + "Bias": ["bias_data"], }, - "op_attrs": dics[0] + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], }, ] @@ -76,24 +75,26 @@ class TrtConvertFcTest(TrtLayerAutoScanTest): program_config = ProgramConfig( ops=ops, weights={ - "w_data": - TensorConfig(data_gen=partial(generate_w, batch, dics)), - "bias_data": - TensorConfig( - data_gen=partial(generate_bias, batch, dics)) + "w_data": TensorConfig( + data_gen=partial(generate_w, batch, dics) + ), + "bias_data": TensorConfig( + data_gen=partial(generate_bias, batch, dics) + ), }, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input1, batch, dics)), + "input_data": TensorConfig( + data_gen=partial(generate_input1, batch, dics) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 3, 32, 16, 2], @@ -121,19 +122,23 @@ class TrtConvertFcTest(TrtLayerAutoScanTest): # clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-5) + attrs, True + ), (1e-3, 1e-3) def test(self): self.run_test() @@ -143,10 +148,9 @@ class TrtConvertFcTest(TrtLayerAutoScanTest): class TrtConvertFcTest2(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: # The output has diff between gpu and trt in CI windows - if (os.name == 'nt'): + if os.name == 'nt': return False return True @@ -157,8 +161,9 @@ class TrtConvertFcTest2(TrtLayerAutoScanTest): return np.random.random([batch, 3, 64, 14]).astype(np.float32) def generate_w(batch, attrs: List[Dict[str, Any]]): - return np.random.random([attrs[0]["m"], - attrs[0]["n"]]).astype(np.float32) + return np.random.random([attrs[0]["m"], attrs[0]["n"]]).astype( + np.float32 + ) def generate_bias(batch, attrs: List[Dict[str, Any]]): return np.random.random([attrs[0]["n"]]).astype(np.float32) @@ -172,7 +177,7 @@ class TrtConvertFcTest2(TrtLayerAutoScanTest): "m": m, "n": n, }, - {} + {}, ] ops_config = [ @@ -181,12 +186,10 @@ class TrtConvertFcTest2(TrtLayerAutoScanTest): "op_inputs": { "Input": ["input_data"], "W": ["w_data"], - "Bias": ["bias_data"] + "Bias": ["bias_data"], }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], }, ] @@ -195,24 +198,26 @@ class TrtConvertFcTest2(TrtLayerAutoScanTest): program_config = ProgramConfig( ops=ops, weights={ - "w_data": - TensorConfig(data_gen=partial(generate_w, batch, dics)), - "bias_data": - TensorConfig( - data_gen=partial(generate_bias, batch, dics)) + "w_data": TensorConfig( + data_gen=partial(generate_w, batch, dics) + ), + "bias_data": TensorConfig( + data_gen=partial(generate_bias, batch, dics) + ), }, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input1, batch, dics)), + "input_data": TensorConfig( + data_gen=partial(generate_input1, batch, dics) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): self.dynamic_shape.min_input_shape = { "input_data": [1, 3, 32, 14], @@ -234,14 +239,14 @@ class TrtConvertFcTest2(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), (1e-5, 1e-5) + yield self.create_inference_config(), (1, 2), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), (1e-5, 1e-5) + yield self.create_inference_config(), (1, 2), (1e-3, 1e-3) def test(self): self.run_test() @@ -277,7 +282,7 @@ class TrtConvertFcTest3(TrtLayerAutoScanTest): "m": m, "n": n, }, - {} + {}, ] ops_config = [ @@ -286,12 +291,10 @@ class TrtConvertFcTest3(TrtLayerAutoScanTest): "op_inputs": { "Input": ["input_data"], "W": ["w_data"], - "Bias": ["bias_data"] + "Bias": ["bias_data"], }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], }, ] @@ -300,24 +303,26 @@ class TrtConvertFcTest3(TrtLayerAutoScanTest): program_config = ProgramConfig( ops=ops, weights={ - "w_data": - TensorConfig(data_gen=partial(generate_w, batch, dics)), - "bias_data": - TensorConfig( - data_gen=partial(generate_bias, batch, dics)) + "w_data": TensorConfig( + data_gen=partial(generate_w, batch, dics) + ), + "bias_data": TensorConfig( + data_gen=partial(generate_bias, batch, dics) + ), }, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input1, batch, dics)), + "input_data": TensorConfig( + data_gen=partial(generate_input1, batch, dics) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): self.dynamic_shape.min_input_shape = { "input_data": [1, 14, 1, 2], @@ -339,16 +344,16 @@ class TrtConvertFcTest3(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), (1e-5, 1e-5) + yield self.create_inference_config(), (1, 2), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), (1e-5, 1e-5) + yield self.create_inference_config(), (1, 2), (1e-3, 1e-3) self.trt_param.precision = paddle_infer.PrecisionType.Int8 - yield self.create_inference_config(), (1, 2), (1e-5, 1e-5) + yield self.create_inference_config(), (1, 2), (1e-3, 1e-3) def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.py index cc686be6d8a83ef511f83ba85a13df5064e8bd32..b70e91a58508c49d631f379b832c742239dec95b 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_fill_constant.py @@ -22,12 +22,10 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertSplitTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_value_data(attrs: List[Dict[str, Any]]): return np.array([1]).astype(np.int32) @@ -47,21 +45,28 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): str_value = str_value else: str_value = "" - dics = [{ - "str_value": str_value, - "value": value, - "shape": shape, - "dtype": dtype - }, { - "axis": -1 - }] - dics_intput = [{ - "ValueTensor": ["value_data"] - }, { - "ShapeTensor": ["shape_data"], - }, { - "ShapeTensorList": ["shapeT1_data", "shapeT2_data"], - }, {}] + dics = [ + { + "str_value": str_value, + "value": value, + "shape": shape, + "dtype": dtype, + }, + {"axis": -1}, + ] + dics_intput = [ + {"ValueTensor": ["value_data"]}, + { + "ShapeTensor": ["shape_data"], + }, + { + "ShapeTensorList": [ + "shapeT1_data", + "shapeT2_data", + ], + }, + {}, + ] ops_config = [ { "op_type": "fill_constant", @@ -69,7 +74,7 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): "op_outputs": { "Out": ["out_data"], }, - "op_attrs": dics[0] + "op_attrs": dics[0], }, ] @@ -81,26 +86,31 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): ops=ops, weights={}, inputs={ - "value_data": - TensorConfig(data_gen=partial( - generate_value_data, dics)), - "shape_data": - TensorConfig(data_gen=partial( - generate_shape_data, dics)), - "shapeT1_data": - TensorConfig(data_gen=partial( - generate_shapelist_data, dics)), - "shapeT2_data": - TensorConfig(data_gen=partial( - generate_shapelist_data, dics)), + "value_data": TensorConfig( + data_gen=partial(generate_value_data, dics) + ), + "shape_data": TensorConfig( + data_gen=partial(generate_shape_data, dics) + ), + "shapeT1_data": TensorConfig( + data_gen=partial( + generate_shapelist_data, dics + ) + ), + "shapeT2_data": TensorConfig( + data_gen=partial( + generate_shapelist_data, dics + ) + ), }, - outputs=["out_data"]) + outputs=["out_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.input_shape = [1, 1] max_shape = list(self.input_shape) @@ -118,7 +128,7 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(attrs, dynamic_shape): - if (self.num_input < 3): + if self.num_input < 3: return 0, 6 return 1, 5 @@ -131,10 +141,12 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_flatten.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_flatten.py index e9f9b70b91671533da033ed31f614555f99c2804..b9f8c4fffc34a2b059d7c04a4a9206e5542c42aa 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_flatten.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_flatten.py @@ -22,16 +22,14 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertFlattenTest_dim_2(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(batch): return np.random.random([batch, 32]).astype(np.float32) - for batch in [1, 2, 4]: + for batch in [1, 4]: for axis in [0, 1]: for type in ["flatten", "flatten2"]: if type == "flatten": @@ -39,34 +37,35 @@ class TrtConvertFlattenTest_dim_2(TrtLayerAutoScanTest): else: op_outputs = { "Out": ["output_data"], - "XShape": ["xshape_data"] + "XShape": ["xshape_data"], } dics = [{"axis": axis}] - ops_config = [{ - "op_type": "flatten", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": op_outputs, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "flatten", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": op_outputs, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, batch)) + "input_data": TensorConfig( + data_gen=partial(generate_input, batch) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 8]} self.dynamic_shape.max_input_shape = {"input_data": [4, 64]} @@ -100,35 +99,37 @@ class TrtConvertFlattenTest_dim_2(TrtLayerAutoScanTest): # for static_shape clear_dynamic_shape() yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-3, 1e-3) + attrs, True + ), (1e-3, 1e-3) def test(self): self.run_test() class TrtConvertFlattenTest_dim_3(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(batch): return np.random.random([batch, 32, 64]).astype(np.float32) - for batch in [1, 2, 4]: + for batch in [1, 4]: for axis in [0, 1, 2]: for type in ["flatten", "flatten2"]: if type == "flatten": @@ -136,38 +137,39 @@ class TrtConvertFlattenTest_dim_3(TrtLayerAutoScanTest): else: op_outputs = { "Out": ["output_data"], - "XShape": ["xshape_data"] + "XShape": ["xshape_data"], } dics = [{"axis": axis}] - ops_config = [{ - "op_type": "flatten", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": op_outputs, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "flatten", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": op_outputs, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, batch)) + "input_data": TensorConfig( + data_gen=partial(generate_input, batch) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 8, 8]} - self.dynamic_shape.max_input_shape = {"input_data": [4, 64, 768]} - self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 256]} + self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64]} + self.dynamic_shape.opt_input_shape = {"input_data": [2, 32, 64]} def clear_dynamic_shape(): self.dynamic_shape.max_input_shape = {} @@ -198,35 +200,37 @@ class TrtConvertFlattenTest_dim_3(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-3, 1e-3) + attrs, True + ), (1e-3, 1e-3) def test(self): self.run_test() class TrtConvertFlattenTest_dim_4(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(batch): return np.random.random([batch, 8, 8, 8]).astype(np.float32) - for batch in [1, 2, 4]: + for batch in [1, 4]: for axis in [0, 1, 2, 3]: for type in ["flatten", "flatten2"]: if type == "flatten": @@ -234,37 +238,38 @@ class TrtConvertFlattenTest_dim_4(TrtLayerAutoScanTest): else: op_outputs = { "Out": ["output_data"], - "XShape": ["xshape_data"] + "XShape": ["xshape_data"], } dics = [{"axis": axis}] - ops_config = [{ - "op_type": "flatten", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": op_outputs, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "flatten", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": op_outputs, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, batch)) + "input_data": TensorConfig( + data_gen=partial(generate_input, batch) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 4, 4, 4]} - self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64, 64]} + self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 32, 32]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 16, 8]} def clear_dynamic_shape(): @@ -294,36 +299,39 @@ class TrtConvertFlattenTest_dim_4(TrtLayerAutoScanTest): # for static_shape clear_dynamic_shape() + self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-3, 1e-3) + attrs, True + ), (1e-3, 1e-3) def test(self): self.run_test() class TrtConvertFlattenTest_dim_5(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(batch): return np.random.random([batch, 8, 8, 8]).astype(np.float32) - for batch in [1, 2, 4]: + for batch in [1, 4]: for axis in [0, 1, 2, 3, 4]: for type in ["flatten", "flatten2"]: if type == "flatten": @@ -331,37 +339,38 @@ class TrtConvertFlattenTest_dim_5(TrtLayerAutoScanTest): else: op_outputs = { "Out": ["output_data"], - "XShape": ["xshape_data"] + "XShape": ["xshape_data"], } dics = [{"axis": axis}] - ops_config = [{ - "op_type": "flatten", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": op_outputs, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "flatten", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": op_outputs, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, batch)) + "input_data": TensorConfig( + data_gen=partial(generate_input, batch) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 4, 4, 4]} - self.dynamic_shape.max_input_shape = {"input_data": [4, 32, 64, 64]} + self.dynamic_shape.max_input_shape = {"input_data": [4, 16, 16, 8]} self.dynamic_shape.opt_input_shape = {"input_data": [2, 16, 16, 8]} def clear_dynamic_shape(): @@ -391,20 +400,25 @@ class TrtConvertFlattenTest_dim_5(TrtLayerAutoScanTest): # for static_shape clear_dynamic_shape() + self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-3, 1e-3) + attrs, True + ), (1e-3, 1e-3) def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather.py index 25d0d48c8c3bf3dcc94efda97b297112475d2585..784c12fc8eeadf1458ad64c51c700a50a72bf420 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather.py @@ -23,7 +23,6 @@ import unittest class TrtConvertGatherTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs attrs = [ @@ -35,7 +34,6 @@ class TrtConvertGatherTest(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input1(shape): return np.random.random(shape).astype(np.float32) @@ -52,112 +50,126 @@ class TrtConvertGatherTest(TrtLayerAutoScanTest): for index in [[1, 4], [4, 8]]: for axis in [0, 1, 2, 3]: for overwrite in [True, False]: - for input in [{ - "X": ["input_data"], - "Index": ["index_data"] - }, { + for input in [ + {"X": ["input_data"], "Index": ["index_data"]}, + { "X": ["input_data"], "Index": ["index_data"], - "Axis": ["axis_data"] - }]: + "Axis": ["axis_data"], + }, + ]: for index_type_int32 in [True, False]: self.shape = shape self.axis = axis self.input_num = len(input) self.index_type_int32 = index_type_int32 dics = [{"overwrite": overwrite, "axis": axis}] - ops_config = [{ - "op_type": "gather", - "op_inputs": input, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "gather", + "op_inputs": input, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, shape)), - "index_data": - TensorConfig(data_gen=partial( - generate_input2 - if index_type_int32 == - True else generate_input4, index)), - } if len(input) == 2 else { - "input_data": - TensorConfig(data_gen=partial( - generate_input1, shape)), - "index_data": - TensorConfig(data_gen=partial( - generate_input2, index)), - "axis_data": - TensorConfig(data_gen=partial( - generate_input3, axis)), + "input_data": TensorConfig( + data_gen=partial( + generate_input1, shape + ) + ), + "index_data": TensorConfig( + data_gen=partial( + generate_input2 + if index_type_int32 == True + else generate_input4, + index, + ) + ), + } + if len(input) == 2 + else { + "input_data": TensorConfig( + data_gen=partial( + generate_input1, shape + ) + ), + "index_data": TensorConfig( + data_gen=partial( + generate_input2, index + ) + ), + "axis_data": TensorConfig( + data_gen=partial( + generate_input3, axis + ) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if len(self.shape) == 1: self.dynamic_shape.min_input_shape = { "input_data": [4], - "index_data": [1] + "index_data": [1], } self.dynamic_shape.max_input_shape = { "input_data": [128], - "index_data": [4] + "index_data": [4], } self.dynamic_shape.opt_input_shape = { "input_data": [16], - "index_data": [2] + "index_data": [2], } elif len(self.shape) == 2: self.dynamic_shape.min_input_shape = { "input_data": [2, 4], - "index_data": [1] + "index_data": [1], } self.dynamic_shape.max_input_shape = { "input_data": [256, 256], - "index_data": [4] + "index_data": [4], } self.dynamic_shape.opt_input_shape = { "input_data": [64, 32], - "index_data": [2] + "index_data": [2], } elif len(self.shape) == 3: self.dynamic_shape.min_input_shape = { "input_data": [2, 4, 4], - "index_data": [1] + "index_data": [1], } self.dynamic_shape.max_input_shape = { "input_data": [128, 256, 256], - "index_data": [4] + "index_data": [4], } self.dynamic_shape.opt_input_shape = { "input_data": [16, 64, 32], - "index_data": [2] + "index_data": [2], } elif len(self.shape) == 4: self.dynamic_shape.min_input_shape = { "input_data": [2, 4, 4, 2], - "index_data": [1] + "index_data": [1], } self.dynamic_shape.max_input_shape = { "input_data": [128, 256, 64, 128], - "index_data": [4] + "index_data": [4], } self.dynamic_shape.opt_input_shape = { "input_data": [16, 64, 16, 32], - "index_data": [2] + "index_data": [2], } def clear_dynamic_shape(): @@ -182,10 +194,12 @@ class TrtConvertGatherTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - False), 1e-5 + False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - False), 1e-5 + False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) @@ -201,14 +215,17 @@ class TrtConvertGatherTest(TrtLayerAutoScanTest): def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0: inputs = program_config.inputs - if len(inputs['input_data'].shape) == 1 or len( - inputs['index_data'].shape) == 1: + if ( + len(inputs['input_data'].shape) == 1 + or len(inputs['index_data'].shape) == 1 + ): return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_SUPPORT, - "Need to repair the case: trt reshape out failed for dynamic shape mode when inputs' dims==1. under trt7.0 " + teller1, + SkipReasons.TRT_NOT_SUPPORT, + "Need to repair the case: trt reshape out failed for dynamic shape mode when inputs' dims==1. under trt7.0 ", ) def test(self): diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py index 75b5cba9e81b398d7259c7fb69b20a825ba7429e..7f2372a8846cdcd816dfa91c1497e02c43ec63d1 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py @@ -23,7 +23,6 @@ import os class TrtConvertGatherNdTest_dim_4_1(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: # The output has diff between gpu and trt in CI windows # if ( and self.trt_param.precision == paddle_infer.PrecisionType.Half): @@ -31,54 +30,53 @@ class TrtConvertGatherNdTest_dim_4_1(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input1(): return np.random.random([2, 32, 64, 64]).astype(np.float32) def generate_input2(): return np.ones([1]).astype(np.int32) - ops_config = [{ - "op_type": "gather_nd", - "op_inputs": { - "X": ["input_data"], - "Index": ["index_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "gather_nd", + "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) for i in range(10): program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1)), - "index_data": - TensorConfig(data_gen=partial(generate_input2)), + "input_data": TensorConfig( + data_gen=partial(generate_input1) + ), + "index_data": TensorConfig( + data_gen=partial(generate_input2) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 8, 8, 8], - "index_data": [1] + "index_data": [1], } self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 64, 64], - "index_data": [1] + "index_data": [1], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 64, 64], - "index_data": [1] + "index_data": [1], } def clear_dynamic_shape(): @@ -95,25 +93,26 @@ class TrtConvertGatherNdTest_dim_4_1(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 4), 1e-5 + yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_SUPPORT, - "Under Windows Ci, this case will sporadically fail.") + teller1, + SkipReasons.TRT_NOT_SUPPORT, + "Under Windows Ci, this case will sporadically fail.", + ) def test(self): self.add_skip_trt_case() @@ -121,29 +120,24 @@ class TrtConvertGatherNdTest_dim_4_1(TrtLayerAutoScanTest): class TrtConvertGatherNdTest_dim_4_1_2(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([2, 32, 64, 64]).astype(np.float32) def generate_input2(): return np.array([1, 2]).astype(np.int32) - ops_config = [{ - "op_type": "gather_nd", - "op_inputs": { - "X": ["input_data"], - "Index": ["index_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "gather_nd", + "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( @@ -153,25 +147,26 @@ class TrtConvertGatherNdTest_dim_4_1_2(TrtLayerAutoScanTest): "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 8, 8, 8], - "index_data": [2] + "index_data": [2], } self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 64, 64], - "index_data": [2] + "index_data": [2], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 64, 64], - "index_data": [2] + "index_data": [2], } def clear_dynamic_shape(): @@ -188,25 +183,26 @@ class TrtConvertGatherNdTest_dim_4_1_2(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 4), 1e-5 + yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_SUPPORT, - "Under Windows Ci, this case will sporadically fail.") + teller1, + SkipReasons.TRT_NOT_SUPPORT, + "Under Windows Ci, this case will sporadically fail.", + ) def test(self): self.add_skip_trt_case() @@ -214,29 +210,24 @@ class TrtConvertGatherNdTest_dim_4_1_2(TrtLayerAutoScanTest): class TrtConvertGatherNdTest_dim_4_2(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([2, 32, 64, 64]).astype(np.float32) def generate_input2(): return np.ones([2, 2]).astype(np.int32) - ops_config = [{ - "op_type": "gather_nd", - "op_inputs": { - "X": ["input_data"], - "Index": ["index_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "gather_nd", + "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( @@ -246,25 +237,26 @@ class TrtConvertGatherNdTest_dim_4_2(TrtLayerAutoScanTest): "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 8, 8, 8], - "index_data": [2, 2] + "index_data": [2, 2], } self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 64, 64], - "index_data": [2, 2] + "index_data": [2, 2], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 64, 64], - "index_data": [2, 2] + "index_data": [2, 2], } def clear_dynamic_shape(): @@ -281,25 +273,26 @@ class TrtConvertGatherNdTest_dim_4_2(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 4), 1e-5 + yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_SUPPORT, - "Under Windows Ci, this case will sporadically fail.") + teller1, + SkipReasons.TRT_NOT_SUPPORT, + "Under Windows Ci, this case will sporadically fail.", + ) def test(self): self.add_skip_trt_case() @@ -307,29 +300,24 @@ class TrtConvertGatherNdTest_dim_4_2(TrtLayerAutoScanTest): class TrtConvertGatherNdTest_dim_4_3(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([2, 32, 64, 64]).astype(np.float32) def generate_input2(): return np.ones([2, 2, 4]).astype(np.int32) - ops_config = [{ - "op_type": "gather_nd", - "op_inputs": { - "X": ["input_data"], - "Index": ["index_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "gather_nd", + "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( @@ -339,25 +327,26 @@ class TrtConvertGatherNdTest_dim_4_3(TrtLayerAutoScanTest): "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 8, 8, 8], - "index_data": [2, 2, 4] + "index_data": [2, 2, 4], } self.dynamic_shape.max_input_shape = { "input_data": [4, 32, 64, 64], - "index_data": [2, 2, 4] + "index_data": [2, 2, 4], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 32, 64, 64], - "index_data": [2, 2, 4] + "index_data": [2, 2, 4], } def clear_dynamic_shape(): @@ -374,25 +363,26 @@ class TrtConvertGatherNdTest_dim_4_3(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 4), 1e-5 + yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_SUPPORT, - "Under Windows Ci, this case will sporadically fail.") + teller1, + SkipReasons.TRT_NOT_SUPPORT, + "Under Windows Ci, this case will sporadically fail.", + ) def test(self): self.add_skip_trt_case() @@ -400,29 +390,24 @@ class TrtConvertGatherNdTest_dim_4_3(TrtLayerAutoScanTest): class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([2, 32]).astype(np.float32) def generate_input2(): return np.array([[0, 3], [1, 9]]).astype(np.int32) - ops_config = [{ - "op_type": "gather_nd", - "op_inputs": { - "X": ["input_data"], - "Index": ["index_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "gather_nd", + "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( @@ -432,25 +417,26 @@ class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest): "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 4], - "index_data": [2, 2] + "index_data": [2, 2], } self.dynamic_shape.max_input_shape = { "input_data": [4, 64], - "index_data": [2, 2] + "index_data": [2, 2], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 8], - "index_data": [2, 2] + "index_data": [2, 2], } def clear_dynamic_shape(): @@ -467,25 +453,26 @@ class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 4), 1e-5 + yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if len(self.dynamic_shape.min_input_shape) != 0 and os.name == 'nt': return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_SUPPORT, - "Under Windows Ci, this case will sporadically fail.") + teller1, + SkipReasons.TRT_NOT_SUPPORT, + "Under Windows Ci, this case will sporadically fail.", + ) def test(self): self.add_skip_trt_case() @@ -493,30 +480,26 @@ class TrtConvertGatherNdTest_dim_2_2(TrtLayerAutoScanTest): class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([16, 32, 256]).astype(np.float32) def generate_input2(): - return np.array([[[2, 5], [3, 8]], [[0, 2], [0, - 3]]]).astype(np.int32) - - ops_config = [{ - "op_type": "gather_nd", - "op_inputs": { - "X": ["input_data"], - "Index": ["index_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": {} - }] + return np.array([[[2, 5], [3, 8]], [[0, 2], [0, 3]]]).astype( + np.int32 + ) + + ops_config = [ + { + "op_type": "gather_nd", + "op_inputs": {"X": ["input_data"], "Index": ["index_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( @@ -526,25 +509,26 @@ class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest): "input_data": TensorConfig(data_gen=partial(generate_input1)), "index_data": TensorConfig(data_gen=partial(generate_input2)), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 4, 4], - "index_data": [1, 1, 1] + "index_data": [1, 1, 1], } self.dynamic_shape.max_input_shape = { "input_data": [16, 64, 512], - "index_data": [4, 2, 4] + "index_data": [4, 2, 4], } self.dynamic_shape.opt_input_shape = { "input_data": [2, 8, 64], - "index_data": [2, 2, 2] + "index_data": [2, 2, 2], } def clear_dynamic_shape(): @@ -561,14 +545,14 @@ class TrtConvertGatherNdTest_dim_3_3(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 4), 1e-5 + yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gelu.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gelu.py index 29f656130f793d3cdd28b8b1f065aa103c781679..818f2a4c1b842e59ea526deb3325f2f04638da34 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gelu.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gelu.py @@ -22,12 +22,10 @@ import unittest class TrtConvertGeluTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(dims, attrs: List[Dict[str, Any]]): if dims == 1: return np.ones([32]).astype(np.float32) @@ -43,33 +41,32 @@ class TrtConvertGeluTest(TrtLayerAutoScanTest): self.dims = dims dics = [{"approximate": approximate}] - ops_config = [{ - "op_type": "gelu", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "gelu", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input1, dims, dics)) + "input_data": TensorConfig( + data_gen=partial(generate_input1, dims, dics) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [1]} @@ -123,19 +120,23 @@ class TrtConvertGeluTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_grid_sampler.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_grid_sampler.py index 17518622bf2d16bca0802d78532de76c078705e7..36b0c1638cb77e111b0d660c1d10b446aa6af843 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_grid_sampler.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_grid_sampler.py @@ -22,29 +22,27 @@ import unittest class TrtConvertGridSampler(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([1, 3, 32, 32]).astype(np.float32) def generate_input2(): return np.random.random([1, 3, 3, 2]).astype(np.float32) - ops_config = [{ - "op_type": "grid_sampler", - "op_inputs": { - "X": ["input_data"], - "Grid": ["grid_data"], - }, - "op_outputs": { - "Output": ["output_data"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "grid_sampler", + "op_inputs": { + "X": ["input_data"], + "Grid": ["grid_data"], + }, + "op_outputs": {"Output": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) for i in range(10): @@ -52,30 +50,33 @@ class TrtConvertGridSampler(TrtLayerAutoScanTest): ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1)), - "grid_data": - TensorConfig(data_gen=partial(generate_input2)), + "input_data": TensorConfig( + data_gen=partial(generate_input1) + ), + "grid_data": TensorConfig( + data_gen=partial(generate_input2) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 3, 32, 32], - "grid_data": [1, 3, 3, 2] + "grid_data": [1, 3, 3, 2], } self.dynamic_shape.max_input_shape = { "input_data": [1, 3, 64, 64], - "grid_data": [1, 3, 4, 4] + "grid_data": [1, 3, 4, 4], } self.dynamic_shape.opt_input_shape = { "input_data": [1, 3, 32, 32], - "grid_data": [1, 3, 3, 2] + "grid_data": [1, 3, 3, 2], } def clear_dynamic_shape(): @@ -92,14 +93,14 @@ class TrtConvertGridSampler(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 4), 1e-5 + yield self.create_inference_config(), (0, 4), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_group_norm.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_group_norm.py index 6115ae60eff328ed2a7021677e20072f02c9473a..3b3ac529389560082019ed99204a13b3dbe038d6 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_group_norm.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_group_norm.py @@ -22,7 +22,6 @@ import unittest class TrtConvertGroupNormTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -36,7 +35,6 @@ class TrtConvertGroupNormTest(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input(attrs: List[Dict[str, Any]], batch): if attrs[0]['data_layout'] == 'NCHW': return np.random.random([batch, 32, 64, 64]).astype(np.float32) @@ -53,47 +51,56 @@ class TrtConvertGroupNormTest(TrtLayerAutoScanTest): for group in [1, 4, 32, -1]: for epsilon in [0.0001, 0.0007, -1, 1]: for data_layout in ['NCHW']: - dics = [{ - "epsilon": epsilon, - "groups": group, - "data_layout": data_layout - }] - ops_config = [{ - "op_type": "group_norm", - "op_inputs": { - "X": ["input_data"], - "Scale": ["scale_weight"], - "Bias": ["bias_weight"] - }, - "op_outputs": { - "Y": ["y_output"], - "Mean": ["mean_output"], - "Variance": ["variance_output"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "epsilon": epsilon, + "groups": group, + "data_layout": data_layout, + } + ] + ops_config = [ + { + "op_type": "group_norm", + "op_inputs": { + "X": ["input_data"], + "Scale": ["scale_weight"], + "Bias": ["bias_weight"], + }, + "op_outputs": { + "Y": ["y_output"], + "Mean": ["mean_output"], + "Variance": ["variance_output"], + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "scale_weight": - TensorConfig(data_gen=partial(generate_scale)), - "bias_weight": - TensorConfig(data_gen=partial(generate_bias)) + "scale_weight": TensorConfig( + data_gen=partial(generate_scale) + ), + "bias_weight": TensorConfig( + data_gen=partial(generate_bias) + ), }, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input, dics, batch)) + "input_data": TensorConfig( + data_gen=partial( + generate_input, dics, batch + ) + ) }, - outputs=["y_output"]) + outputs=["y_output"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 16, 16, 16]} self.dynamic_shape.max_input_shape = { @@ -117,19 +124,23 @@ class TrtConvertGroupNormTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), (1e-3, 1e-3) def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_sigmoid.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_sigmoid.py index 0980acccb88b5ac65e7662eff7f2e59671fbb708..8ed6407476acbce2ce8435d420bc297c559fe4ce 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_sigmoid.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_sigmoid.py @@ -22,12 +22,10 @@ import unittest class TrtConvertHardSigmoidTest_dim_2(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -37,33 +35,34 @@ class TrtConvertHardSigmoidTest_dim_2(TrtLayerAutoScanTest): for slope in [0.1, 0.5]: for offset in [0.2, 0.7]: dics = [{"slope": slope, "offset": offset}] - ops_config = [{ - "op_type": "hard_sigmoid", - "op_inputs": { - "X": ["input_data"], - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "hard_sigmoid", + "op_inputs": { + "X": ["input_data"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input, shape)) + "input_data": TensorConfig( + data_gen=partial(generate_input, shape) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.input_dim == 2: self.dynamic_shape.min_input_shape = {"input_data": [1, 8]} @@ -98,14 +97,14 @@ class TrtConvertHardSigmoidTest_dim_2(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), 1e-5 + yield self.create_inference_config(), (1, 2), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), 1e-5 + yield self.create_inference_config(), (1, 2), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py index 220611517e0634d6074b08cf8540e5abdc2f7830..106431e740fc6a79df85d69599bc4f57c2377cf1 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_hard_swish.py @@ -22,7 +22,6 @@ import unittest class TrtConvertHardSwishTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -36,46 +35,46 @@ class TrtConvertHardSwishTest(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]]): return np.ones([1, 3, 32, 32]).astype(np.float32) for threshold in [6.0, 7.0, 100.0, 0.0, -1.0]: for scale in [5.0, 7.0, -1.0, 0.0, 100.0]: for offset in [3.0, 5.0, -1.0, 0.0, 100.0]: - dics = [{ - "threshold": threshold, - "scale": scale, - "offset": offset - }] - - ops_config = [{ - "op_type": "hard_swish", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["hard_swish_output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "threshold": threshold, + "scale": scale, + "offset": offset, + } + ] + + ops_config = [ + { + "op_type": "hard_swish", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["hard_swish_output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input1, dics)) + "input_data": TensorConfig( + data_gen=partial(generate_input1, dics) + ) }, - outputs=["hard_swish_output_data"]) + outputs=["hard_swish_output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 16, 16]} self.dynamic_shape.max_input_shape = {"input_data": [2, 3, 32, 32]} @@ -97,19 +96,23 @@ class TrtConvertHardSwishTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-5) + attrs, True + ), (1e-3, 1e-3) def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_inverse.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_inverse.py index 258aca7eb9bd4031919f549ca3810893142a6c11..6ccb00d1a0f51d665576a3ab0a23974380ebc3ac 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_inverse.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_inverse.py @@ -22,41 +22,41 @@ import unittest class TrtConvertInverse(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([32, 32]).astype(np.float32) - ops_config = [{ - "op_type": "inverse", - "op_inputs": { - "Input": ["input_data"], - }, - "op_outputs": { - "Output": ["output_data"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "inverse", + "op_inputs": { + "Input": ["input_data"], + }, + "op_outputs": {"Output": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) for i in range(10): program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1)), + "input_data": TensorConfig( + data_gen=partial(generate_input1) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 1], @@ -82,14 +82,14 @@ class TrtConvertInverse(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 3), 1e-5 + yield self.create_inference_config(), (0, 3), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), 1e-5 + yield self.create_inference_config(), (1, 2), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_leaky_relu.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_leaky_relu.py index 7f33cfc64a86630a8b66099088ff5167a38947af..0ac59aa8c547dbd726fca88cf5bac498a8cbcbca 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_leaky_relu.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_leaky_relu.py @@ -23,12 +23,10 @@ import unittest class TrtConvertLeakyReluTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(shape): return np.random.random(shape).astype(np.float32) @@ -37,32 +35,35 @@ class TrtConvertLeakyReluTest(TrtLayerAutoScanTest): self.input_dim = len(shape) for alpha in [0.02, 1.0, 100.0, -1.0, 0.0]: dics = [{"alpha": alpha}] - ops_config = [{ - "op_type": "leaky_relu", - "op_inputs": { - "X": ["input_data"], - }, - "op_outputs": { - "Out": ["y_data"], - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "leaky_relu", + "op_inputs": { + "X": ["input_data"], + }, + "op_outputs": { + "Out": ["y_data"], + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input1, shape)) + "input_data": TensorConfig( + data_gen=partial(generate_input1, shape) + ) }, - outputs=["y_data"]) + outputs=["y_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.input_dim == 2: self.dynamic_shape.min_input_shape = {"input_data": [1, 8]} @@ -101,25 +102,31 @@ class TrtConvertLeakyReluTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-3, 1e-3) self.trt_param.precision = paddle_infer.PrecisionType.Int8 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-5) + attrs, True + ), (1e-3, 1e-3) self.trt_param.precision = paddle_infer.PrecisionType.Int8 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-5) + attrs, True + ), (1e-3, 1e-3) def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul.py index 76fcffad4592c328fc498179d500bad5f9c79951..3ba8aad3cae021144a130d8639266001035e01c3 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul.py @@ -22,12 +22,10 @@ import unittest class TrtConvertMatmulTest_static(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -47,48 +45,55 @@ class TrtConvertMatmulTest_static(TrtLayerAutoScanTest): input1_shape = [batch, 32, 6] input2_shape = [batch, 6, 11] for alpha in [0.3, 1.0]: - dics = [{ - "transpose_X": trans_x, - "transpose_Y": trans_y, - "alpha": alpha, - "fused_reshape_X": [], - "fused_reshape_Y": [], - "fused_transpose_X": [], - "fused_transpose_Y": [], - "fused_reshape_Out": [], - "fused_transpose_Out": [] - }] - ops_config = [{ - "op_type": "matmul", - "op_inputs": { - "X": ["input1_data"], - "Y": ["input2_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "transpose_X": trans_x, + "transpose_Y": trans_y, + "alpha": alpha, + "fused_reshape_X": [], + "fused_reshape_Y": [], + "fused_transpose_X": [], + "fused_transpose_Y": [], + "fused_reshape_Out": [], + "fused_transpose_Out": [], + } + ] + ops_config = [ + { + "op_type": "matmul", + "op_inputs": { + "X": ["input1_data"], + "Y": ["input2_data"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1_data": - TensorConfig(data_gen=partial( - generate_input, input1_shape)), - "input2_data": - TensorConfig(data_gen=partial( - generate_input, input2_shape)) + "input1_data": TensorConfig( + data_gen=partial( + generate_input, input1_shape + ) + ), + "input2_data": TensorConfig( + data_gen=partial( + generate_input, input2_shape + ) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): pass @@ -102,19 +107,17 @@ class TrtConvertMatmulTest_static(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def test(self): self.run_test() class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -133,60 +136,63 @@ class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest): # input1_shape = [batch, 32, 6] # input2_shape = [batch, 6, 11] for alpha in [0.3, 1.0]: - dics = [{ - "transpose_X": trans_x, - "transpose_Y": trans_y, - "alpha": alpha, - "fused_reshape_X": [], - "fused_reshape_Y": [], - "fused_transpose_X": [], - "fused_transpose_Y": [], - "fused_reshape_Out": [], - "fused_transpose_Out": [] - }] - ops_config = [{ - "op_type": "matmul", - "op_inputs": { - "X": ["input1_data"], - "Y": ["input2_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "transpose_X": trans_x, + "transpose_Y": trans_y, + "alpha": alpha, + "fused_reshape_X": [], + "fused_reshape_Y": [], + "fused_transpose_X": [], + "fused_transpose_Y": [], + "fused_reshape_Out": [], + "fused_transpose_Out": [], + } + ] + ops_config = [ + { + "op_type": "matmul", + "op_inputs": { + "X": ["input1_data"], + "Y": ["input2_data"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1_data": - TensorConfig( - data_gen=partial(generate_input, input1_shape)), - "input2_data": - TensorConfig( - data_gen=partial(generate_input, input2_shape)) + "input1_data": TensorConfig( + data_gen=partial(generate_input, input1_shape) + ), + "input2_data": TensorConfig( + data_gen=partial(generate_input, input2_shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input1_data": [1, 4, 4], - "input2_data": [1, 4, 4] + "input2_data": [1, 4, 4], } self.dynamic_shape.max_input_shape = { "input1_data": [16, 4, 4], - "input2_data": [16, 4, 4] + "input2_data": [16, 4, 4], } self.dynamic_shape.opt_input_shape = { "input1_data": [8, 4, 4], - "input2_data": [8, 4, 4] + "input2_data": [8, 4, 4], } attrs = [ @@ -198,7 +204,7 @@ class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul_v2.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul_v2.py index d895872db4bd2ab3cc5fb2cdc102b6b33d7c6dd7..ec6c9e633071c6c03b7364e42b955e91c8eb2e48 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul_v2.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_matmul_v2.py @@ -23,9 +23,7 @@ import os class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest): - def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -34,53 +32,56 @@ class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest): for trans_y in [False]: input1_shape = [batch, 64, 350, 75] input2_shape = [75, 25] - dics = [{ - "trans_x": trans_x, - "trans_y": trans_y, - }] - ops_config = [{ - "op_type": "matmul_v2", - "op_inputs": { - "X": ["input1_data"], - "Y": ["input2_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "trans_x": trans_x, + "trans_y": trans_y, + } + ] + ops_config = [ + { + "op_type": "matmul_v2", + "op_inputs": { + "X": ["input1_data"], + "Y": ["input2_data"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1_data": - TensorConfig( - data_gen=partial(generate_input, input1_shape)), - "input2_data": - TensorConfig( - data_gen=partial(generate_input, input2_shape)) + "input1_data": TensorConfig( + data_gen=partial(generate_input, input1_shape) + ), + "input2_data": TensorConfig( + data_gen=partial(generate_input, input2_shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input1_data": [10, 64, 350, 75], - "input2_data": [75, 25] + "input2_data": [75, 25], } self.dynamic_shape.max_input_shape = { "input1_data": [100, 64, 350, 75], - "input2_data": [75, 25] + "input2_data": [75, 25], } self.dynamic_shape.opt_input_shape = { "input1_data": [15, 64, 350, 75], - "input2_data": [75, 25] + "input2_data": [75, 25], } attrs = [ @@ -90,7 +91,7 @@ class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest): # The output has little diff between gpu and trt in CI-Windows-Inference tol_fp32 = 1e-5 tol_half = 1e-5 - if (os.name == 'nt'): + if os.name == 'nt': tol_fp32 = 1e-3 tol_half = 1e-3 # for dynamic_shape @@ -109,9 +110,7 @@ class TrtConvertMatmulTest_dynamic(TrtLayerAutoScanTest): class TrtConvertMatmulTest_dynamic2(TrtLayerAutoScanTest): - def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -120,53 +119,56 @@ class TrtConvertMatmulTest_dynamic2(TrtLayerAutoScanTest): for trans_y in [False]: input1_shape = [60, 40] input2_shape = [batch, 40, 90] - dics = [{ - "trans_x": trans_x, - "trans_y": trans_y, - }] - ops_config = [{ - "op_type": "matmul_v2", - "op_inputs": { - "X": ["input1_data"], - "Y": ["input2_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "trans_x": trans_x, + "trans_y": trans_y, + } + ] + ops_config = [ + { + "op_type": "matmul_v2", + "op_inputs": { + "X": ["input1_data"], + "Y": ["input2_data"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1_data": - TensorConfig( - data_gen=partial(generate_input, input1_shape)), - "input2_data": - TensorConfig( - data_gen=partial(generate_input, input2_shape)) + "input1_data": TensorConfig( + data_gen=partial(generate_input, input1_shape) + ), + "input2_data": TensorConfig( + data_gen=partial(generate_input, input2_shape) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input1_data": [60, 40], - "input2_data": [10, 40, 90] + "input2_data": [10, 40, 90], } self.dynamic_shape.max_input_shape = { "input1_data": [60, 40], - "input2_data": [20, 40, 90] + "input2_data": [20, 40, 90], } self.dynamic_shape.opt_input_shape = { "input1_data": [60, 40], - "input2_data": [15, 40, 90] + "input2_data": [15, 40, 90], } attrs = [ @@ -175,7 +177,7 @@ class TrtConvertMatmulTest_dynamic2(TrtLayerAutoScanTest): # The output has little diff between gpu and trt in CI-Windows-Inference tol_fp32 = 1e-5 tol_half = 1e-5 - if (os.name == 'nt'): + if os.name == 'nt': tol_fp32 = 1e-3 tol_half = 1e-3 # for dynamic_shape @@ -194,9 +196,7 @@ class TrtConvertMatmulTest_dynamic2(TrtLayerAutoScanTest): class TrtConvertMatmulTest_dynamic3(TrtLayerAutoScanTest): - def sample_program_configs(self): - def generate_input(shape): return np.random.random(shape).astype(np.float32) @@ -219,93 +219,102 @@ class TrtConvertMatmulTest_dynamic3(TrtLayerAutoScanTest): elif case == 2: input1_shape = [50] input2_shape = [50] - if (case == 0 or case == 1): - dics = [{ - "trans_x": False, - "trans_y": False, - }] - elif (case == 2): - dics = [{ - "trans_x": trans_x, - "trans_y": trans_y, - }] - ops_config = [{ - "op_type": "matmul_v2", - "op_inputs": { - "X": ["input1_data"], - "Y": ["input2_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + if case == 0 or case == 1: + dics = [ + { + "trans_x": False, + "trans_y": False, + } + ] + elif case == 2: + dics = [ + { + "trans_x": trans_x, + "trans_y": trans_y, + } + ] + ops_config = [ + { + "op_type": "matmul_v2", + "op_inputs": { + "X": ["input1_data"], + "Y": ["input2_data"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1_data": - TensorConfig(data_gen=partial( - generate_input, input1_shape)), - "input2_data": - TensorConfig(data_gen=partial( - generate_input, input2_shape)) + "input1_data": TensorConfig( + data_gen=partial( + generate_input, input1_shape + ) + ), + "input2_data": TensorConfig( + data_gen=partial( + generate_input, input2_shape + ) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): - if (self.case == 0): + if self.case == 0: self.dynamic_shape.min_input_shape = { "input1_data": [20, 50], - "input2_data": [50] + "input2_data": [50], } self.dynamic_shape.max_input_shape = { "input1_data": [30, 50], - "input2_data": [50] + "input2_data": [50], } self.dynamic_shape.opt_input_shape = { "input1_data": [25, 50], - "input2_data": [50] + "input2_data": [50], } - elif (self.case == 1): + elif self.case == 1: self.dynamic_shape.min_input_shape = { "input2_data": [50, 20], - "input1_data": [50] + "input1_data": [50], } self.dynamic_shape.max_input_shape = { "input2_data": [50, 30], - "input1_data": [50] + "input1_data": [50], } self.dynamic_shape.opt_input_shape = { "input2_data": [50, 25], - "input1_data": [50] + "input1_data": [50], } - elif (self.case == 2): + elif self.case == 2: self.dynamic_shape.min_input_shape = { "input2_data": [30], - "input1_data": [50] + "input1_data": [50], } self.dynamic_shape.max_input_shape = { "input2_data": [50], - "input1_data": [50] + "input1_data": [50], } self.dynamic_shape.opt_input_shape = { "input2_data": [50], - "input1_data": [50] + "input1_data": [50], } generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), 1e-5 + yield self.create_inference_config(), (1, 3), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_multihead_matmul.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_multihead_matmul.py index d552692ae4ff93ba6b03c4e316da86a3de4349b6..5bc980a227feac7432eab37620015e569c86b11f 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_multihead_matmul.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_multihead_matmul.py @@ -22,12 +22,10 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(batch, dim1): return np.random.random((batch, dim1, 768)).astype(np.float32) @@ -44,103 +42,86 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): self.batch = batch for reshape_shape in [[0, 0, 12, 64]]: for dim1 in [128]: - input2_shapes = [[batch, reshape_shape[2], dim1, dim1], - [batch, 1, 1, dim1]] + input2_shapes = [ + [batch, reshape_shape[2], dim1, dim1], + [batch, 1, 1, dim1], + ] for input2_shape in input2_shapes: for axis in [0]: - dics = [{ - "x_num_col_dims": 2, - "y_num_col_dims": 1 - }, { - "axis": 2 - }, { - "shape": reshape_shape - }, { - "axis": [0, 2, 1, 3] - }, { - "x_num_col_dims": 2, - "y_num_col_dims": 1 - }, { - "axis": 2 - }, { - "shape": reshape_shape - }, { - "axis": [0, 2, 1, 3] - }, { - "x_num_col_dims": 2, - "y_num_col_dims": 1 - }, { - "axis": 2 - }, { - "shape": reshape_shape - }, { - "axis": [0, 2, 1, 3] - }, { - "scale": 0.125, - "bias": 0.0, - "bias_after_scale": True - }, { - "alpha": 1.0, - "transpose_X": False, - "transpose_Y": True, - "fused_reshape_X": [], - "fused_reshape_Y": [], - "fused_transpose_X": [], - "fused_transpose_Y": [], - "fused_reshape_Out": [], - "fused_transpose_Out": [] - }, { - "axis": axis - }, { - "axis": -1, - "is_test": True - }, { - "seed": 0, - "dropout_prob": 0.10000000149011612, - "dropout_implementation": "upscale_in_train", - "fix_seed": False, - "is_test": True - }, { - "alpha": 1.0, - "transpose_X": False, - "transpose_Y": False, - "fused_reshape_X": [], - "fused_reshape_Y": [], - "fused_transpose_X": [], - "fused_transpose_Y": [], - "fused_reshape_Out": [], - "fused_transpose_Out": [] - }, { - "axis": [0, 2, 1, 3] - }, { - "shape": [0, 0, 768] - }, { - "x_num_col_dims": 2, - "y_num_col_dims": 1 - }] + dics = [ + {"x_num_col_dims": 2, "y_num_col_dims": 1}, + {"axis": 2}, + {"shape": reshape_shape}, + {"axis": [0, 2, 1, 3]}, + {"x_num_col_dims": 2, "y_num_col_dims": 1}, + {"axis": 2}, + {"shape": reshape_shape}, + {"axis": [0, 2, 1, 3]}, + {"x_num_col_dims": 2, "y_num_col_dims": 1}, + {"axis": 2}, + {"shape": reshape_shape}, + {"axis": [0, 2, 1, 3]}, + { + "scale": 0.125, + "bias": 0.0, + "bias_after_scale": True, + }, + { + "alpha": 1.0, + "transpose_X": False, + "transpose_Y": True, + "fused_reshape_X": [], + "fused_reshape_Y": [], + "fused_transpose_X": [], + "fused_transpose_Y": [], + "fused_reshape_Out": [], + "fused_transpose_Out": [], + }, + {"axis": axis}, + {"axis": -1, "is_test": True}, + { + "seed": 0, + "dropout_prob": 0.10000000149011612, + "dropout_implementation": "upscale_in_train", + "fix_seed": False, + "is_test": True, + }, + { + "alpha": 1.0, + "transpose_X": False, + "transpose_Y": False, + "fused_reshape_X": [], + "fused_reshape_Y": [], + "fused_transpose_X": [], + "fused_transpose_Y": [], + "fused_reshape_Out": [], + "fused_transpose_Out": [], + }, + {"axis": [0, 2, 1, 3]}, + {"shape": [0, 0, 768]}, + {"x_num_col_dims": 2, "y_num_col_dims": 1}, + ] ops_config = [ { "op_type": "mul", "op_inputs": { "X": ["input_data1"], - "Y": ["mul1_weight"] + "Y": ["mul1_weight"], }, - "op_outputs": { - "Out": ["mul1_output"] - }, - "op_attrs": dics[0] + "op_outputs": {"Out": ["mul1_output"]}, + "op_attrs": dics[0], }, { "op_type": "elementwise_add", "op_inputs": { "X": ["mul1_output"], - "Y": ["elementwise_add1_weight"] + "Y": ["elementwise_add1_weight"], }, "op_outputs": { "Out": ["elementwise_add1_output"] }, - "op_attrs": dics[1] + "op_attrs": dics[1], }, { "op_type": "reshape2", @@ -149,42 +130,38 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): }, "op_outputs": { "Out": ["reshape21_output"], - "XShape": ["reshape21_output_xshape"] + "XShape": ["reshape21_output_xshape"], }, - "op_attrs": dics[2] + "op_attrs": dics[2], }, { "op_type": "transpose2", - "op_inputs": { - "X": ["reshape21_output"] - }, + "op_inputs": {"X": ["reshape21_output"]}, "op_outputs": { "Out": ["transpose21_output"], - "XShape": ["transpose21_output_xshape"] + "XShape": ["transpose21_output_xshape"], }, - "op_attrs": dics[3] + "op_attrs": dics[3], }, { "op_type": "mul", "op_inputs": { "X": ["input_data1"], - "Y": ["mul2_weight"] + "Y": ["mul2_weight"], }, - "op_outputs": { - "Out": ["mul2_output"] - }, - "op_attrs": dics[4] + "op_outputs": {"Out": ["mul2_output"]}, + "op_attrs": dics[4], }, { "op_type": "elementwise_add", "op_inputs": { "X": ["mul2_output"], - "Y": ["elementwise_add2_weight"] + "Y": ["elementwise_add2_weight"], }, "op_outputs": { "Out": ["elementwise_add2_output"] }, - "op_attrs": dics[5] + "op_attrs": dics[5], }, { "op_type": "reshape2", @@ -193,42 +170,38 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): }, "op_outputs": { "Out": ["reshape22_output"], - "XShape": ["reshape22_output_xshape"] + "XShape": ["reshape22_output_xshape"], }, - "op_attrs": dics[6] + "op_attrs": dics[6], }, { "op_type": "transpose2", - "op_inputs": { - "X": ["reshape22_output"] - }, + "op_inputs": {"X": ["reshape22_output"]}, "op_outputs": { "Out": ["transpose22_output"], - "XShape": ["transpose22_output_xshape"] + "XShape": ["transpose22_output_xshape"], }, - "op_attrs": dics[7] + "op_attrs": dics[7], }, { "op_type": "mul", "op_inputs": { "X": ["input_data1"], - "Y": ["mul3_weight"] + "Y": ["mul3_weight"], }, - "op_outputs": { - "Out": ["mul3_output"] - }, - "op_attrs": dics[8] + "op_outputs": {"Out": ["mul3_output"]}, + "op_attrs": dics[8], }, { "op_type": "elementwise_add", "op_inputs": { "X": ["mul3_output"], - "Y": ["elementwise_add3_weight"] + "Y": ["elementwise_add3_weight"], }, "op_outputs": { "Out": ["elementwise_add3_output"] }, - "op_attrs": dics[9] + "op_attrs": dics[9], }, { "op_type": "reshape2", @@ -237,30 +210,26 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): }, "op_outputs": { "Out": ["reshape23_output"], - "XShape": ["reshape23_output_xshape"] + "XShape": ["reshape23_output_xshape"], }, - "op_attrs": dics[10] + "op_attrs": dics[10], }, { "op_type": "transpose2", - "op_inputs": { - "X": ["reshape23_output"] - }, + "op_inputs": {"X": ["reshape23_output"]}, "op_outputs": { "Out": ["transpose23_output"], - "XShape": ["transpose23_output_xshape"] + "XShape": ["transpose23_output_xshape"], }, - "op_attrs": dics[11] + "op_attrs": dics[11], }, { "op_type": "scale", "op_inputs": { "X": ["transpose23_output"], }, - "op_outputs": { - "Out": ["scale_output"] - }, - "op_attrs": dics[12] + "op_outputs": {"Out": ["scale_output"]}, + "op_attrs": dics[12], }, { "op_type": "matmul", @@ -268,41 +237,35 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): "X": ["scale_output"], "Y": ["transpose22_output"], }, - "op_outputs": { - "Out": ["matmul1_output"] - }, - "op_attrs": dics[13] + "op_outputs": {"Out": ["matmul1_output"]}, + "op_attrs": dics[13], }, { "op_type": "elementwise_add", "op_inputs": { "X": ["matmul1_output"], - "Y": ["input_data2"] + "Y": ["input_data2"], }, "op_outputs": { "Out": ["elementwise_add4_output"] }, - "op_attrs": dics[14] + "op_attrs": dics[14], }, { "op_type": "softmax", "op_inputs": { "X": ["elementwise_add4_output"] }, - "op_outputs": { - "Out": ["softmax_output"] - }, - "op_attrs": dics[15] + "op_outputs": {"Out": ["softmax_output"]}, + "op_attrs": dics[15], }, { "op_type": "dropout", "op_inputs": { "X": ["softmax_output"], }, - "op_outputs": { - "Out": ["dropout3_output"] - }, - "op_attrs": dics[16] + "op_outputs": {"Out": ["dropout3_output"]}, + "op_attrs": dics[16], }, { "op_type": "matmul", @@ -310,32 +273,26 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): "X": ["dropout3_output"], "Y": ["transpose21_output"], }, - "op_outputs": { - "Out": ["matmul2_output"] - }, - "op_attrs": dics[17] + "op_outputs": {"Out": ["matmul2_output"]}, + "op_attrs": dics[17], }, { "op_type": "transpose2", - "op_inputs": { - "X": ["matmul2_output"] - }, + "op_inputs": {"X": ["matmul2_output"]}, "op_outputs": { "Out": ["transpose24_output"], - "XShape": ["transpose24_output_xshape"] + "XShape": ["transpose24_output_xshape"], }, - "op_attrs": dics[18] + "op_attrs": dics[18], }, { "op_type": "reshape2", - "op_inputs": { - "X": ["transpose24_output"] - }, + "op_inputs": {"X": ["transpose24_output"]}, "op_outputs": { "Out": ["reshape24_output"], - "XShape": ["reshape24_output_xshape"] + "XShape": ["reshape24_output_xshape"], }, - "op_attrs": dics[19] + "op_attrs": dics[19], }, # In order to fuse ops with # multihead_matmul_fuse_pass_v2, the last op @@ -344,72 +301,75 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): "op_type": "mul", "op_inputs": { "X": ["reshape24_output"], - "Y": ["mul4_weight"] + "Y": ["mul4_weight"], }, - "op_outputs": { - "Out": ["mul4_output"] - }, - "op_attrs": dics[20] - } + "op_outputs": {"Out": ["mul4_output"]}, + "op_attrs": dics[20], + }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "mul1_weight": - TensorConfig( - data_gen=partial(generate_weight1)), - "mul2_weight": - TensorConfig( - data_gen=partial(generate_weight1)), - "mul3_weight": - TensorConfig( - data_gen=partial(generate_weight1)), - "mul4_weight": - TensorConfig( - data_gen=partial(generate_weight1)), - "elementwise_add1_weight": - TensorConfig( - data_gen=partial(generate_weight2)), - "elementwise_add2_weight": - TensorConfig( - data_gen=partial(generate_weight2)), - "elementwise_add3_weight": - TensorConfig( - data_gen=partial(generate_weight2)), + "mul1_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "mul2_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "mul3_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "mul4_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "elementwise_add1_weight": TensorConfig( + data_gen=partial(generate_weight2) + ), + "elementwise_add2_weight": TensorConfig( + data_gen=partial(generate_weight2) + ), + "elementwise_add3_weight": TensorConfig( + data_gen=partial(generate_weight2) + ), }, inputs={ - "input_data1": - TensorConfig(data_gen=partial( - generate_input1, batch, dim1)), - "input_data2": - TensorConfig(data_gen=partial( - generate_input2, input2_shape)), + "input_data1": TensorConfig( + data_gen=partial( + generate_input1, batch, dim1 + ) + ), + "input_data2": TensorConfig( + data_gen=partial( + generate_input2, input2_shape + ) + ), }, - outputs=["mul4_output"]) + outputs=["mul4_output"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The last dim of input1 and input2 should be static. self.dynamic_shape.min_input_shape = { "input_data1": [1, 8, 768], "input_data2": [1, 1, 1, 128], - "reshape24_output": [1, 128, 768] + "reshape24_output": [1, 128, 768], } self.dynamic_shape.max_input_shape = { "input_data1": [16, 512, 768], "input_data2": [16, 256, 512, 128], - "reshape24_output": [1, 128, 768] + "reshape24_output": [1, 128, 768], } self.dynamic_shape.opt_input_shape = { "input_data1": [8, 128, 768], "input_data2": [8, 32, 64, 128], - "reshape24_output": [1, 128, 768] + "reshape24_output": [1, 128, 768], } def clear_dynamic_shape(): @@ -427,7 +387,7 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): self.trt_param.workspace_size = 2013265920 yield self.create_inference_config(), (1, 4), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 4), (1e-5, 1e-5) + yield self.create_inference_config(), (1, 4), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) @@ -435,28 +395,33 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): self.trt_param.workspace_size = 2013265920 yield self.create_inference_config(), (1, 3), (1e-5, 1e-4) self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 3), (1e-5, 1e-5) + yield self.create_inference_config(), (1, 3), (1e-3, 1e-3) def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if self.trt_param.precision == paddle_infer.PrecisionType.Half: return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_IMPLEMENTED, - "The output has diff between gpu and trt in fp16 mode.") + teller1, + SkipReasons.TRT_NOT_IMPLEMENTED, + "The output has diff between gpu and trt in fp16 mode.", + ) def teller2(program_config, predictor_config): - if self.trt_param.precision == paddle_infer.PrecisionType.Float32 and len( - self.dynamic_shape.min_input_shape) != 0 and self.batch > 2: + if ( + self.trt_param.precision == paddle_infer.PrecisionType.Float32 + and len(self.dynamic_shape.min_input_shape) != 0 + and self.batch > 2 + ): return True return False self.add_skip_case( - teller2, SkipReasons.TRT_NOT_IMPLEMENTED, - "The output has diff between gpu and trt when dynamic fp32 mode and batch size > 2." + teller2, + SkipReasons.TRT_NOT_IMPLEMENTED, + "The output has diff between gpu and trt when dynamic fp32 mode and batch size > 2.", ) def teller3(program_config, predictor_config): @@ -465,8 +430,10 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): return False self.add_skip_case( - teller3, SkipReasons.TRT_NOT_IMPLEMENTED, - "The output has diff between gpu and trt in int8 mode.") + teller3, + SkipReasons.TRT_NOT_IMPLEMENTED, + "The output has diff between gpu and trt in int8 mode.", + ) def test(self): self.add_skip_trt_case() @@ -474,9 +441,7 @@ class TrtConvertMultiHeadMatmulTest(TrtLayerAutoScanTest): class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest): - def sample_program_configs(self): - def generate_input1(batch, dim1): return np.random.random((batch, dim1, 768)).astype(np.float32) @@ -493,112 +458,110 @@ class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest): self.batch = batch for reshape_shape in [[0, 0, 12, 64]]: for dim1 in [128]: - input2_shapes = [[batch, reshape_shape[2], dim1, dim1], - [batch, 1, 1, dim1]] + input2_shapes = [ + [batch, reshape_shape[2], dim1, dim1], + [batch, 1, 1, dim1], + ] for input2_shape in input2_shapes: for axis in [0]: - dics = [{ - "x_num_col_dims": 2, - "y_num_col_dims": 1, - "enable_int8": True, - "Input_scale": 1.0, - }, { - "axis": 2, - "out_threshold": 1.0, - }, { - "shape": reshape_shape - }, { - "axis": [0, 2, 1, 3] - }, { - "x_num_col_dims": 2, - "y_num_col_dims": 1, - "enable_int8": True, - "Input_scale": 1.0, - }, { - "axis": 2, - "out_threshold": 1.0, - }, { - "shape": reshape_shape - }, { - "axis": [0, 2, 1, 3] - }, { - "x_num_col_dims": 2, - "y_num_col_dims": 1, - "enable_int8": True, - "Input_scale": 1.0, - }, { - "axis": 2, - "out_threshold": 1.0, - }, { - "shape": reshape_shape - }, { - "axis": [0, 2, 1, 3] - }, { - "scale": 0.125, - "bias": 0.0, - "bias_after_scale": True - }, { - "alpha": 1.0, - "transpose_X": False, - "transpose_Y": True, - "fused_reshape_X": [], - "fused_reshape_Y": [], - "fused_transpose_X": [], - "fused_transpose_Y": [], - "fused_reshape_Out": [], - "fused_transpose_Out": [] - }, { - "axis": axis - }, { - "axis": -1, - "is_test": True - }, { - "seed": 0, - "dropout_prob": 0.10000000149011612, - "dropout_implementation": "upscale_in_train", - "fix_seed": False, - "is_test": True - }, { - "alpha": 1.0, - "transpose_X": False, - "transpose_Y": False, - "fused_reshape_X": [], - "fused_reshape_Y": [], - "fused_transpose_X": [], - "fused_transpose_Y": [], - "fused_reshape_Out": [], - "fused_transpose_Out": [] - }, { - "axis": [0, 2, 1, 3] - }, { - "shape": [0, 0, 768] - }, { - "x_num_col_dims": 2, - "y_num_col_dims": 1 - }] + dics = [ + { + "x_num_col_dims": 2, + "y_num_col_dims": 1, + "enable_int8": True, + "Input_scale": 1.0, + }, + { + "axis": 2, + "out_threshold": 1.0, + }, + {"shape": reshape_shape}, + {"axis": [0, 2, 1, 3]}, + { + "x_num_col_dims": 2, + "y_num_col_dims": 1, + "enable_int8": True, + "Input_scale": 1.0, + }, + { + "axis": 2, + "out_threshold": 1.0, + }, + {"shape": reshape_shape}, + {"axis": [0, 2, 1, 3]}, + { + "x_num_col_dims": 2, + "y_num_col_dims": 1, + "enable_int8": True, + "Input_scale": 1.0, + }, + { + "axis": 2, + "out_threshold": 1.0, + }, + {"shape": reshape_shape}, + {"axis": [0, 2, 1, 3]}, + { + "scale": 0.125, + "bias": 0.0, + "bias_after_scale": True, + }, + { + "alpha": 1.0, + "transpose_X": False, + "transpose_Y": True, + "fused_reshape_X": [], + "fused_reshape_Y": [], + "fused_transpose_X": [], + "fused_transpose_Y": [], + "fused_reshape_Out": [], + "fused_transpose_Out": [], + }, + {"axis": axis}, + {"axis": -1, "is_test": True}, + { + "seed": 0, + "dropout_prob": 0.10000000149011612, + "dropout_implementation": "upscale_in_train", + "fix_seed": False, + "is_test": True, + }, + { + "alpha": 1.0, + "transpose_X": False, + "transpose_Y": False, + "fused_reshape_X": [], + "fused_reshape_Y": [], + "fused_transpose_X": [], + "fused_transpose_Y": [], + "fused_reshape_Out": [], + "fused_transpose_Out": [], + }, + {"axis": [0, 2, 1, 3]}, + {"shape": [0, 0, 768]}, + {"x_num_col_dims": 2, "y_num_col_dims": 1}, + ] ops_config = [ { "op_type": "mul", "op_inputs": { "X": ["input_data1"], - "Y": ["mul1_weight"] - }, - "op_outputs": { - "Out": ["mul1_output"] + "Y": ["mul1_weight"], }, - "op_attrs": dics[0] + "op_outputs": {"Out": ["mul1_output"]}, + "op_attrs": dics[0], }, { "op_type": "elementwise_add", "op_inputs": { "X": ["mul1_output"], - "Y": ["elementwise_add1_weight"] + "Y": ["elementwise_add1_weight"], }, "op_outputs": { "Out": ["elementwise_add1_output"] }, - "op_attrs": dics[1] + "op_attrs": dics[1], }, { "op_type": "reshape2", @@ -607,42 +570,38 @@ class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest): }, "op_outputs": { "Out": ["reshape21_output"], - "XShape": ["reshape21_output_xshape"] + "XShape": ["reshape21_output_xshape"], }, - "op_attrs": dics[2] + "op_attrs": dics[2], }, { "op_type": "transpose2", - "op_inputs": { - "X": ["reshape21_output"] - }, + "op_inputs": {"X": ["reshape21_output"]}, "op_outputs": { "Out": ["transpose21_output"], - "XShape": ["transpose21_output_xshape"] + "XShape": ["transpose21_output_xshape"], }, - "op_attrs": dics[3] + "op_attrs": dics[3], }, { "op_type": "mul", "op_inputs": { "X": ["input_data1"], - "Y": ["mul2_weight"] + "Y": ["mul2_weight"], }, - "op_outputs": { - "Out": ["mul2_output"] - }, - "op_attrs": dics[4] + "op_outputs": {"Out": ["mul2_output"]}, + "op_attrs": dics[4], }, { "op_type": "elementwise_add", "op_inputs": { "X": ["mul2_output"], - "Y": ["elementwise_add2_weight"] + "Y": ["elementwise_add2_weight"], }, "op_outputs": { "Out": ["elementwise_add2_output"] }, - "op_attrs": dics[5] + "op_attrs": dics[5], }, { "op_type": "reshape2", @@ -651,42 +610,38 @@ class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest): }, "op_outputs": { "Out": ["reshape22_output"], - "XShape": ["reshape22_output_xshape"] + "XShape": ["reshape22_output_xshape"], }, - "op_attrs": dics[6] + "op_attrs": dics[6], }, { "op_type": "transpose2", - "op_inputs": { - "X": ["reshape22_output"] - }, + "op_inputs": {"X": ["reshape22_output"]}, "op_outputs": { "Out": ["transpose22_output"], - "XShape": ["transpose22_output_xshape"] + "XShape": ["transpose22_output_xshape"], }, - "op_attrs": dics[7] + "op_attrs": dics[7], }, { "op_type": "mul", "op_inputs": { "X": ["input_data1"], - "Y": ["mul3_weight"] + "Y": ["mul3_weight"], }, - "op_outputs": { - "Out": ["mul3_output"] - }, - "op_attrs": dics[8] + "op_outputs": {"Out": ["mul3_output"]}, + "op_attrs": dics[8], }, { "op_type": "elementwise_add", "op_inputs": { "X": ["mul3_output"], - "Y": ["elementwise_add3_weight"] + "Y": ["elementwise_add3_weight"], }, "op_outputs": { "Out": ["elementwise_add3_output"] }, - "op_attrs": dics[9] + "op_attrs": dics[9], }, { "op_type": "reshape2", @@ -695,30 +650,26 @@ class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest): }, "op_outputs": { "Out": ["reshape23_output"], - "XShape": ["reshape23_output_xshape"] + "XShape": ["reshape23_output_xshape"], }, - "op_attrs": dics[10] + "op_attrs": dics[10], }, { "op_type": "transpose2", - "op_inputs": { - "X": ["reshape23_output"] - }, + "op_inputs": {"X": ["reshape23_output"]}, "op_outputs": { "Out": ["transpose23_output"], - "XShape": ["transpose23_output_xshape"] + "XShape": ["transpose23_output_xshape"], }, - "op_attrs": dics[11] + "op_attrs": dics[11], }, { "op_type": "scale", "op_inputs": { "X": ["transpose23_output"], }, - "op_outputs": { - "Out": ["scale_output"] - }, - "op_attrs": dics[12] + "op_outputs": {"Out": ["scale_output"]}, + "op_attrs": dics[12], }, { "op_type": "matmul", @@ -726,41 +677,35 @@ class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest): "X": ["scale_output"], "Y": ["transpose22_output"], }, - "op_outputs": { - "Out": ["matmul1_output"] - }, - "op_attrs": dics[13] + "op_outputs": {"Out": ["matmul1_output"]}, + "op_attrs": dics[13], }, { "op_type": "elementwise_add", "op_inputs": { "X": ["matmul1_output"], - "Y": ["input_data2"] + "Y": ["input_data2"], }, "op_outputs": { "Out": ["elementwise_add4_output"] }, - "op_attrs": dics[14] + "op_attrs": dics[14], }, { "op_type": "softmax", "op_inputs": { "X": ["elementwise_add4_output"] }, - "op_outputs": { - "Out": ["softmax_output"] - }, - "op_attrs": dics[15] + "op_outputs": {"Out": ["softmax_output"]}, + "op_attrs": dics[15], }, { "op_type": "dropout", "op_inputs": { "X": ["softmax_output"], }, - "op_outputs": { - "Out": ["dropout3_output"] - }, - "op_attrs": dics[16] + "op_outputs": {"Out": ["dropout3_output"]}, + "op_attrs": dics[16], }, { "op_type": "matmul", @@ -768,32 +713,26 @@ class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest): "X": ["dropout3_output"], "Y": ["transpose21_output"], }, - "op_outputs": { - "Out": ["matmul2_output"] - }, - "op_attrs": dics[17] + "op_outputs": {"Out": ["matmul2_output"]}, + "op_attrs": dics[17], }, { "op_type": "transpose2", - "op_inputs": { - "X": ["matmul2_output"] - }, + "op_inputs": {"X": ["matmul2_output"]}, "op_outputs": { "Out": ["transpose24_output"], - "XShape": ["transpose24_output_xshape"] + "XShape": ["transpose24_output_xshape"], }, - "op_attrs": dics[18] + "op_attrs": dics[18], }, { "op_type": "reshape2", - "op_inputs": { - "X": ["transpose24_output"] - }, + "op_inputs": {"X": ["transpose24_output"]}, "op_outputs": { "Out": ["reshape24_output"], - "XShape": ["reshape24_output_xshape"] + "XShape": ["reshape24_output_xshape"], }, - "op_attrs": dics[19] + "op_attrs": dics[19], }, # In order to fuse ops with # multihead_matmul_fuse_pass_v2, the last op @@ -802,61 +741,62 @@ class TrtConvertMultiHeadMatmulTestInt8(TrtConvertMultiHeadMatmulTest): "op_type": "mul", "op_inputs": { "X": ["reshape24_output"], - "Y": ["mul4_weight"] + "Y": ["mul4_weight"], }, - "op_outputs": { - "Out": ["mul4_output"] - }, - "op_attrs": dics[20] - } + "op_outputs": {"Out": ["mul4_output"]}, + "op_attrs": dics[20], + }, ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "mul1_weight": - TensorConfig( - data_gen=partial(generate_weight1)), - "mul2_weight": - TensorConfig( - data_gen=partial(generate_weight1)), - "mul3_weight": - TensorConfig( - data_gen=partial(generate_weight1)), - "mul4_weight": - TensorConfig( - data_gen=partial(generate_weight1)), - "elementwise_add1_weight": - TensorConfig( - data_gen=partial(generate_weight2)), - "elementwise_add2_weight": - TensorConfig( - data_gen=partial(generate_weight2)), - "elementwise_add3_weight": - TensorConfig( - data_gen=partial(generate_weight2)), + "mul1_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "mul2_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "mul3_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "mul4_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "elementwise_add1_weight": TensorConfig( + data_gen=partial(generate_weight2) + ), + "elementwise_add2_weight": TensorConfig( + data_gen=partial(generate_weight2) + ), + "elementwise_add3_weight": TensorConfig( + data_gen=partial(generate_weight2) + ), }, inputs={ - "input_data1": - TensorConfig(data_gen=partial( - generate_input1, batch, dim1)), - "input_data2": - TensorConfig(data_gen=partial( - generate_input2, input2_shape)), + "input_data1": TensorConfig( + data_gen=partial( + generate_input1, batch, dim1 + ) + ), + "input_data2": TensorConfig( + data_gen=partial( + generate_input2, input2_shape + ) + ), }, - outputs=["mul4_output"]) + outputs=["mul4_output"], + ) yield program_config class TrtConvertVitToMultiHeadMatmulTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(batch, length): return np.zeros((batch, length, 768), dtype=np.float32) @@ -870,216 +810,190 @@ class TrtConvertVitToMultiHeadMatmulTest(TrtLayerAutoScanTest): self.batch = batch for length in [64, 384]: self.length = length - ops_config = [{ - "op_type": "matmul_v2", - "op_inputs": { - "X": ["input_data1"], - "Y": ["matmul1_weight"] - }, - "op_outputs": { - "Out": ["matmul1_output"] - }, - "op_attrs": { - "trans_x": False, - "trans_y": False - } - }, { - "op_type": "elementwise_add", - "op_inputs": { - "X": ["matmul1_output"], - "Y": ["elementwise_add1_weight"] - }, - "op_outputs": { - "Out": ["elementwise_add1_output"] + ops_config = [ + { + "op_type": "matmul_v2", + "op_inputs": { + "X": ["input_data1"], + "Y": ["matmul1_weight"], + }, + "op_outputs": {"Out": ["matmul1_output"]}, + "op_attrs": {"trans_x": False, "trans_y": False}, }, - "op_attrs": { - "Scale_out": 1.0, - "Scale_x": 1.0, - "Scale_y": 1.0, - "axis": 2 - } - }, { - "op_type": "reshape2", - "op_inputs": { - "X": ["elementwise_add1_output"], + { + "op_type": "elementwise_add", + "op_inputs": { + "X": ["matmul1_output"], + "Y": ["elementwise_add1_weight"], + }, + "op_outputs": {"Out": ["elementwise_add1_output"]}, + "op_attrs": { + "Scale_out": 1.0, + "Scale_x": 1.0, + "Scale_y": 1.0, + "axis": 2, + }, }, - "op_outputs": { - "Out": ["reshape1_output"], - "XShape": ["reshape1_output_xshape"] + { + "op_type": "reshape2", + "op_inputs": { + "X": ["elementwise_add1_output"], + }, + "op_outputs": { + "Out": ["reshape1_output"], + "XShape": ["reshape1_output_xshape"], + }, + "op_attrs": {"shape": [-1, self.length, 3, 12, 64]}, }, - "op_attrs": { - "shape": [-1, self.length, 3, 12, 64] - } - }, { - "op_type": "transpose2", - "op_inputs": { - "X": ["reshape1_output"] + { + "op_type": "transpose2", + "op_inputs": {"X": ["reshape1_output"]}, + "op_outputs": { + "Out": ["transpose1_output"], + "XShape": ["transpose1_output_xshape"], + }, + "op_attrs": { + "axis": [2, 0, 3, 1, 4], + "data_format": "AnyLayout", + }, }, - "op_outputs": { - "Out": ["transpose1_output"], - "XShape": ["transpose1_output_xshape"] + { + "op_type": "slice", + "op_inputs": { + "Input": ["transpose1_output"], + }, + "op_outputs": {"Out": ["slice1_output"]}, + "op_attrs": { + "axes": [0], + "starts": [0], + "ends": [1], + "decrease_axis": [0], + "infer_flags": [1], + }, }, - "op_attrs": { - "axis": [2, 0, 3, 1, 4], - "data_format": "AnyLayout" - } - }, { - "op_type": "slice", - "op_inputs": { - "Input": ["transpose1_output"], + { + "op_type": "slice", + "op_inputs": { + "Input": ["transpose1_output"], + }, + "op_outputs": {"Out": ["slice2_output"]}, + "op_attrs": { + "axes": [0], + "starts": [1], + "ends": [2], + "decrease_axis": [0], + "infer_flags": [1], + }, }, - "op_outputs": { - "Out": ["slice1_output"] + { + "op_type": "slice", + "op_inputs": { + "Input": ["transpose1_output"], + }, + "op_outputs": {"Out": ["slice3_output"]}, + "op_attrs": { + "axes": [0], + "starts": [2], + "ends": [3], + "decrease_axis": [0], + "infer_flags": [1], + }, }, - "op_attrs": { - "axes": [0], - "starts": [0], - "ends": [1], - "decrease_axis": [0], - "infer_flags": [1] - } - }, { - "op_type": "slice", - "op_inputs": { - "Input": ["transpose1_output"], + { + "op_type": "transpose2", + "op_inputs": {"X": ["slice2_output"]}, + "op_outputs": { + "Out": ["transpose2_output"], + }, + "op_attrs": { + "axis": [0, 1, 3, 2], + "data_format": "AnyLayout", + }, }, - "op_outputs": { - "Out": ["slice2_output"] + { + "op_type": "matmul_v2", + "op_inputs": { + "X": ["slice1_output"], + "Y": ["transpose2_output"], + }, + "op_outputs": {"Out": ["matmul2_output"]}, + "op_attrs": {"trans_x": False, "trans_y": False}, }, - "op_attrs": { - "axes": [0], - "starts": [1], - "ends": [2], - "decrease_axis": [0], - "infer_flags": [1] - } - }, { - "op_type": "slice", - "op_inputs": { - "Input": ["transpose1_output"], + { + "op_type": "scale", + "op_inputs": { + "X": ["matmul2_output"], + }, + "op_outputs": {"Out": ["scale_output"]}, + "op_attrs": { + "scale": 0.125, + "bias": 0.0, + "bias_after_scale": True, + }, }, - "op_outputs": { - "Out": ["slice3_output"] + { + "op_type": "softmax", + "op_inputs": {"X": ["scale_output"]}, + "op_outputs": {"Out": ["softmax_output"]}, + "op_attrs": {"axis": -1, "data_format": "AnyLayout"}, }, - "op_attrs": { - "axes": [0], - "starts": [2], - "ends": [3], - "decrease_axis": [0], - "infer_flags": [1] - } - }, { - "op_type": "transpose2", - "op_inputs": { - "X": ["slice2_output"] + { + "op_type": "matmul_v2", + "op_inputs": { + "X": ["softmax_output"], + "Y": ["slice3_output"], + }, + "op_outputs": {"Out": ["matmul3_output"]}, + "op_attrs": {"trans_x": False, "trans_y": False}, }, - "op_outputs": { - "Out": ["transpose2_output"], + { + "op_type": "transpose2", + "op_inputs": {"X": ["matmul3_output"]}, + "op_outputs": { + "Out": ["transpose3_output"], + "XShape": ["transpose3_output_xshape"], + }, + "op_attrs": { + "axis": [0, 2, 1, 3], + "data_format": "AnyLayout", + }, }, - "op_attrs": { - "axis": [0, 1, 3, 2], - "data_format": "AnyLayout" - } - }, { - "op_type": "matmul_v2", - "op_inputs": { - "X": ["slice1_output"], - "Y": ["transpose2_output"] + { + "op_type": "reshape2", + "op_inputs": {"X": ["transpose3_output"]}, + "op_outputs": { + "Out": ["reshape2_output"], + "XShape": ["reshape2_output_xshape"], + }, + "op_attrs": {"shape": [-1, self.length, 768]}, }, - "op_outputs": { - "Out": ["matmul2_output"] - }, - "op_attrs": { - "trans_x": False, - "trans_y": False - } - }, { - "op_type": "scale", - "op_inputs": { - "X": ["matmul2_output"], - }, - "op_outputs": { - "Out": ["scale_output"] - }, - "op_attrs": { - "scale": 0.125, - "bias": 0.0, - "bias_after_scale": True - } - }, { - "op_type": "softmax", - "op_inputs": { - "X": ["scale_output"] - }, - "op_outputs": { - "Out": ["softmax_output"] - }, - "op_attrs": { - "axis": -1, - "data_format": "AnyLayout" - } - }, { - "op_type": "matmul_v2", - "op_inputs": { - "X": ["softmax_output"], - "Y": ["slice3_output"] - }, - "op_outputs": { - "Out": ["matmul3_output"] - }, - "op_attrs": { - "trans_x": False, - "trans_y": False - } - }, { - "op_type": "transpose2", - "op_inputs": { - "X": ["matmul3_output"] - }, - "op_outputs": { - "Out": ["transpose3_output"], - "XShape": ["transpose3_output_xshape"] - }, - "op_attrs": { - "axis": [0, 2, 1, 3], - "data_format": "AnyLayout" - } - }, { - "op_type": "reshape2", - "op_inputs": { - "X": ["transpose3_output"] - }, - "op_outputs": { - "Out": ["reshape2_output"], - "XShape": ["reshape2_output_xshape"] - }, - "op_attrs": { - "shape": [-1, self.length, 768] - } - }] + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={ - "matmul1_weight": - TensorConfig(data_gen=partial(generate_weight1)), - "elementwise_add1_weight": - TensorConfig(data_gen=partial(generate_weight2)) + "matmul1_weight": TensorConfig( + data_gen=partial(generate_weight1) + ), + "elementwise_add1_weight": TensorConfig( + data_gen=partial(generate_weight2) + ), }, inputs={ - "input_data1": - TensorConfig( - data_gen=partial(generate_input1, batch, length)) + "input_data1": TensorConfig( + data_gen=partial(generate_input1, batch, length) + ) }, - outputs=["reshape2_output"]) + outputs=["reshape2_output"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): # The last dim of input1 and input2 should be static. self.dynamic_shape.min_input_shape = { @@ -1111,11 +1025,15 @@ class TrtConvertVitToMultiHeadMatmulTest(TrtLayerAutoScanTest): generate_dynamic_shape(attrs) self.trt_param.workspace_size = 2013265920 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), generate_trt_nodes_num(), (1e-3, - 1e-3) + yield self.create_inference_config(), generate_trt_nodes_num(), ( + 1e-3, + 1e-3, + ) self.trt_param.precision = paddle_infer.PrecisionType.Float32 - yield self.create_inference_config(), generate_trt_nodes_num(), (1e-5, - 1e-5) + yield self.create_inference_config(), generate_trt_nodes_num(), ( + 1e-5, + 1e-5, + ) def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pad3d.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pad3d.py index 5740be91c657494570588f56dab6cb9c13c7b8b3..02429bed44c0420abd819a894c838a5f0cac3152 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pad3d.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pad3d.py @@ -22,30 +22,30 @@ import unittest class TrtConvertPad3d(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.ones([1, 1, 3, 64, 64]).astype(np.float32) for value in [True, False]: - for paddings in [[0, 0, 0, 0, 1, 1], [0, 0, 1, 2, 3, 4], - [1, 1, 1, 1, 1, 1], [0, 0, -1, -1, 1, 1]]: + for paddings in [ + [0, 0, 0, 0, 1, 1], + [0, 0, 1, 2, 3, 4], + [1, 1, 1, 1, 1, 1], + [0, 0, -1, -1, 1, 1], + ]: dics = [{"value": value, "paddings": paddings}, {}] - ops_config = [{ - "op_type": "pad3d", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "pad3d", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) for i in range(10): @@ -53,16 +53,18 @@ class TrtConvertPad3d(TrtLayerAutoScanTest): ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1)), + "input_data": TensorConfig( + data_gen=partial(generate_input1) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 1, 3, 64, 64] @@ -88,14 +90,14 @@ class TrtConvertPad3d(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 3), 1e-5 + yield self.create_inference_config(), (0, 3), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), 1e-5 + yield self.create_inference_config(), (1, 2), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pool2d.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pool2d.py index b543484d89251f766c078866865943154a6ee3f7..03392554a7ffa77946d0495a455f353fa082eaab 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pool2d.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_pool2d.py @@ -20,10 +20,10 @@ from functools import partial from typing import Optional, List, Callable, Dict, Any, Set import unittest import itertools +import copy class TrtConvertPool2dTest(TrtLayerAutoScanTest): - def is_paddings_valid(self, program_config: ProgramConfig) -> bool: exclusive = program_config.ops[0].attrs['exclusive'] paddings = program_config.ops[0].attrs['paddings'] @@ -65,39 +65,54 @@ class TrtConvertPool2dTest(TrtLayerAutoScanTest): ceil_mode_options = [True, False] configurations = [ - strides_options, paddings_options, pooling_type_options, - padding_algorithm_options, ksize_options, data_format_options, - global_pooling_options, exclusive_options, adaptive_option, - ceil_mode_options + strides_options, + paddings_options, + pooling_type_options, + padding_algorithm_options, + ksize_options, + data_format_options, + global_pooling_options, + exclusive_options, + adaptive_option, + ceil_mode_options, ] - for (strides, paddings, pooling_type, padding_algorithm, ksize, - data_format, global_pooling, exclusive, adaptive, - ceil_mode) in itertools.product(*configurations): - - attrs = [{ - "strides": strides, - "paddings": paddings, - "pooling_type": pooling_type, - "padding_algorithm": padding_algorithm, - "ksize": ksize, - "data_format": data_format, - "global_pooling": global_pooling, - "exclusive": exclusive, - "adaptive": adaptive, - "ceil_mode": ceil_mode, - }] - - ops_config = [{ - "op_type": "pool2d", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": attrs[0] - }] + for ( + strides, + paddings, + pooling_type, + padding_algorithm, + ksize, + data_format, + global_pooling, + exclusive, + adaptive, + ceil_mode, + ) in itertools.product(*configurations): + + attrs = [ + { + "strides": strides, + "paddings": paddings, + "pooling_type": pooling_type, + "padding_algorithm": padding_algorithm, + "ksize": ksize, + "data_format": data_format, + "global_pooling": global_pooling, + "exclusive": exclusive, + "adaptive": adaptive, + "ceil_mode": ceil_mode, + } + ] + + ops_config = [ + { + "op_type": "pool2d", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": attrs[0], + } + ] ops = self.generate_op_config(ops_config) @@ -105,16 +120,18 @@ class TrtConvertPool2dTest(TrtLayerAutoScanTest): ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1, attrs)) + "input_data": TensorConfig( + data_gen=partial(generate_input1, attrs) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]} self.dynamic_shape.max_input_shape = {"input_data": [1, 3, 64, 64]} @@ -136,36 +153,75 @@ class TrtConvertPool2dTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-3, 1e-3) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-3, 1e-3) + attrs, True + ), (1e-3, 1e-3) def add_skip_trt_case(self): - def teller(program_config, predictor_config): - if program_config.ops[0].attrs['pooling_type'] == 'avg' and \ - program_config.ops[0].attrs['global_pooling'] == False and \ - program_config.ops[0].attrs['exclusive'] == True and \ - program_config.ops[0].attrs['adaptive'] == False and \ - program_config.ops[0].attrs['ceil_mode'] == True: + if ( + program_config.ops[0].attrs['pooling_type'] == 'avg' + and program_config.ops[0].attrs['global_pooling'] == False + and program_config.ops[0].attrs['exclusive'] == True + and program_config.ops[0].attrs['adaptive'] == False + and program_config.ops[0].attrs['ceil_mode'] == True + ): return True return False self.add_skip_case( - teller, SkipReasons.TRT_NOT_IMPLEMENTED, - "The results of some cases are Nan, but the results of TensorRT and GPU are the same." + teller, + SkipReasons.TRT_NOT_IMPLEMENTED, + "The results of some cases are Nan, but the results of TensorRT and GPU are the same.", ) + def assert_tensors_near( + self, + atol: float, + rtol: float, + tensor: Dict[str, np.array], + baseline: Dict[str, np.array], + ): + for key, arr in tensor.items(): + self.assertEqual( + baseline[key].shape, + arr.shape, + 'The output shapes are not equal, the baseline shape is ' + + str(baseline[key].shape) + + ', but got ' + + str(arr.shape), + ) + + # The result of Pool2d may have some elements that is the least value (-65504 for FP16), + # but for FP32 and FP16 precision, their least value are different. + # We set a threshold that is the least value of FP16, + # and make the values less than the threshold to be the threshold. + def align_less_threshold(arr, threshold): + return np.clip(arr, threshold, None) + + fp16_min = np.finfo(np.float16).min + baseline_threshold = align_less_threshold( + copy.deepcopy(baseline[key]), fp16_min + ) + arr_threshold = align_less_threshold(copy.deepcopy(arr), fp16_min) + np.testing.assert_allclose( + baseline_threshold, arr_threshold, rtol=rtol, atol=atol + ) + def test(self): self.add_skip_trt_case() self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reduce_sum.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reduce_sum.py index 42b234827b1e720d8e7a4b6bfa7fac8768dd604e..eb640ac54029a9e8148f15f92e54707646de000d 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reduce_sum.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reduce_sum.py @@ -23,7 +23,6 @@ import unittest class TrtConvertReduceSumTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs attrs = [ @@ -41,7 +40,6 @@ class TrtConvertReduceSumTest(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input1(dtype, attrs: List[Dict[str, Any]]): if dtype == -1 or dtype == 5: return np.random.random([1, 3, 32, 32]).astype(np.float32) @@ -49,39 +47,52 @@ class TrtConvertReduceSumTest(TrtLayerAutoScanTest): return np.random.random([1, 3, 32, 32]).astype(np.int32) for keep_dim in [True, False]: - for dim in [[], [1], [0], [0, 1], [1, 2, 3], [-2, 0, 3], [-3], - [-4, 1], [3, 4, 5]]: + for dim in [ + [], + [1], + [0], + [0, 1], + [1, 2, 3], + [-2, 0, 3], + [-3], + [-4, 1], + [3, 4, 5], + ]: for reduce_all in [True, False]: for out_dtype in [-1, 2, 5]: - dics = [{ - "keep_dim": keep_dim, - "dim": dim, - "reduce_all": reduce_all, - "out_dtype": out_dtype, - "in_dtype": out_dtype, - }, {}] - - ops_config = [{ - "op_type": "reduce_sum", - "op_inputs": { - "X": ["input_data"] + dics = [ + { + "keep_dim": keep_dim, + "dim": dim, + "reduce_all": reduce_all, + "out_dtype": out_dtype, + "in_dtype": out_dtype, }, - "op_outputs": { - "Out": ["reduce_output_data"] - }, - "op_attrs": dics[0] - }] + {}, + ] + + ops_config = [ + { + "op_type": "reduce_sum", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["reduce_output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, out_dtype, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, out_dtype, dics + ) + ) }, - outputs=["reduce_output_data"]) + outputs=["reduce_output_data"], + ) if not self.is_program_valid(program_config): continue @@ -89,7 +100,6 @@ class TrtConvertReduceSumTest(TrtLayerAutoScanTest): yield program_config def sample_predictor_configs(self, program_config): - def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]} self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]} @@ -120,19 +130,23 @@ class TrtConvertReduceSumTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-5, 1e-5) + attrs, False + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), (1e-4, 1e-4) + attrs, False + ), (1e-3, 1e-3) # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-5, 1e-5) + attrs, True + ), (1e-5, 1e-5) self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), (1e-4, 1e-4) + attrs, True + ), (1e-3, 1e-3) pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reshape.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reshape.py index 7902a35a9a6b47dd5aa9bb4b5b40918fdb809a25..3dfca41b3e35615be18fef47ea282e61016405c3 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reshape.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_reshape.py @@ -22,7 +22,6 @@ import unittest class TrtConvertReshapeTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) @@ -31,7 +30,7 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): if len(attrs[0]['shape']) != 1: return False - #To test if the shape contains 0 + # To test if the shape contains 0 if len(attrs[0]['shape']) == 3: if attrs[0]['shape'][1] == 0: if self.dims != 3: @@ -45,7 +44,6 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 4: self.input_shape = [1, 2, 4, 6] @@ -70,9 +68,18 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): return np.array([24]).astype(np.int32) for dims in [4, 3, 2, 1]: - for shape in [[1, 6, 8], [1, 2, 4, 6], [1, 1, 0, 12], [1, 0, 6], - [1, -1, 12], [2, -1], [3, 16], [3, 4, 4], [48], - [-1, 48]]: + for shape in [ + [1, 6, 8], + [1, 2, 4, 6], + [1, 1, 0, 12], + [1, 0, 6], + [1, -1, 12], + [2, -1], + [3, 16], + [3, 4, 4], + [48], + [-1, 48], + ]: dics = [ { "shape": shape, @@ -81,29 +88,31 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): self.dims = dims dics_intput = [{"X": ["reshape_input"]}] - ops_config = [{ - "op_type": "reshape", - "op_inputs": dics_intput[0], - "op_outputs": { - "Out": ["reshape_out"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "reshape", + "op_inputs": dics_intput[0], + "op_outputs": {"Out": ["reshape_out"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "reshape_input": - TensorConfig(data_gen=partial(generate_input1, dics)) + "reshape_input": TensorConfig( + data_gen=partial(generate_input1, dics) + ) }, - outputs=["reshape_out"]) + outputs=["reshape_out"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 4: self.dynamic_shape.min_input_shape = { @@ -141,13 +150,14 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): def generate_trt_nodes_num(attrs, dynamic_shape): # in static shape mode, here is consistent with op_teller.cc - if (not dynamic_shape): - if (attrs[0]['shape'][0] == 0): + if not dynamic_shape: + if attrs[0]['shape'][0] == 0: return 1, 2 - elif (len(attrs[0]['shape']) == 1): + elif len(attrs[0]['shape']) == 1: return 0, 3 - elif (np.prod(attrs[0]['shape'][1:]) == np.prod( - self.input_shape[1:])): + elif np.prod(attrs[0]['shape'][1:]) == np.prod( + self.input_shape[1:] + ): return 1, 2 else: return 0, 3 @@ -161,19 +171,23 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): pass @@ -185,12 +199,10 @@ class TrtConvertReshapeTest(TrtLayerAutoScanTest): # reshape having three inputs. class TrtConvertReshapeTest2(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 4: return np.random.random([1, 2, 4, 6]).astype(np.float32) @@ -203,9 +215,12 @@ class TrtConvertReshapeTest2(TrtLayerAutoScanTest): for dims in [4, 3, 2, 1]: for shape in [[-1, 48]]: - dics = [{ - "shape": shape, - }, {}] + dics = [ + { + "shape": shape, + }, + {}, + ] self.dims = dims dics_intput = [ { @@ -217,9 +232,7 @@ class TrtConvertReshapeTest2(TrtLayerAutoScanTest): { "op_type": "fill_constant", "op_inputs": {}, - "op_outputs": { - "Out": ["shapeT1_data"] - }, + "op_outputs": {"Out": ["shapeT1_data"]}, "op_attrs": { "dtype": 2, "str_value": "2", @@ -229,9 +242,7 @@ class TrtConvertReshapeTest2(TrtLayerAutoScanTest): { "op_type": "fill_constant", "op_inputs": {}, - "op_outputs": { - "Out": ["shapeT2_data"] - }, + "op_outputs": {"Out": ["shapeT2_data"]}, "op_attrs": { "dtype": 2, "str_value": "24", @@ -241,10 +252,8 @@ class TrtConvertReshapeTest2(TrtLayerAutoScanTest): { "op_type": "reshape", "op_inputs": dics_intput[0], - "op_outputs": { - "Out": ["reshape_out"] - }, - "op_attrs": dics[0] + "op_outputs": {"Out": ["reshape_out"]}, + "op_attrs": dics[0], }, ] ops = self.generate_op_config(ops_config) @@ -252,16 +261,18 @@ class TrtConvertReshapeTest2(TrtLayerAutoScanTest): ops=ops, weights={}, inputs={ - "reshape_input": - TensorConfig(data_gen=partial(generate_input1, dics)) + "reshape_input": TensorConfig( + data_gen=partial(generate_input1, dics) + ) }, - outputs=["reshape_out"]) + outputs=["reshape_out"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): if self.dims == 4: self.dynamic_shape.min_input_shape = { @@ -297,7 +308,7 @@ class TrtConvertReshapeTest2(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), 1e-5 + yield self.create_inference_config(), (1, 2), 1e-3 def add_skip_trt_case(self): pass @@ -309,12 +320,10 @@ class TrtConvertReshapeTest2(TrtLayerAutoScanTest): # reshape having 2 inputs. class TrtConvertReshapeTest3(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]]): if self.dims == 4: return np.random.random([1, 2, 12, 6]).astype(np.float32) @@ -327,9 +336,12 @@ class TrtConvertReshapeTest3(TrtLayerAutoScanTest): for dims in [4, 3, 2, 1]: for shape in [[-1, 144]]: - dics = [{ - "shape": shape, - }, {}] + dics = [ + { + "shape": shape, + }, + {}, + ] self.dims = dims dics_intput = [ { @@ -341,9 +353,7 @@ class TrtConvertReshapeTest3(TrtLayerAutoScanTest): { "op_type": "fill_constant", "op_inputs": {}, - "op_outputs": { - "Out": ["shape_data"] - }, + "op_outputs": {"Out": ["shape_data"]}, "op_attrs": { "dtype": 2, "str_value": "12", @@ -353,10 +363,8 @@ class TrtConvertReshapeTest3(TrtLayerAutoScanTest): { "op_type": "reshape", "op_inputs": dics_intput[0], - "op_outputs": { - "Out": ["reshape_out"] - }, - "op_attrs": dics[0] + "op_outputs": {"Out": ["reshape_out"]}, + "op_attrs": dics[0], }, ] ops = self.generate_op_config(ops_config) @@ -364,16 +372,18 @@ class TrtConvertReshapeTest3(TrtLayerAutoScanTest): ops=ops, weights={}, inputs={ - "reshape_input": - TensorConfig(data_gen=partial(generate_input1, dics)) + "reshape_input": TensorConfig( + data_gen=partial(generate_input1, dics) + ) }, - outputs=["reshape_out"]) + outputs=["reshape_out"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): if self.dims == 4: self.dynamic_shape.min_input_shape = { @@ -409,7 +419,7 @@ class TrtConvertReshapeTest3(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), 1e-5 + yield self.create_inference_config(), (1, 2), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roi_align.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roi_align.py index ca12fe876ca394c13903e87a5ba289fd6bc87bda..f59ce47e97d39551a11a24342f04582f2e8b5a31 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roi_align.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roi_align.py @@ -22,12 +22,10 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertRoiAlignTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]], batch): return np.ones([batch, 256, 32, 32]).astype(np.float32) @@ -47,92 +45,111 @@ class TrtConvertRoiAlignTest(TrtLayerAutoScanTest): self.num_input = num_input if num_input == 1: batch = 1 - dics = [{ - "spatial_scale": spatial_scale, - "pooled_height": pooled_height, - "pooled_width": pooled_width, - "sampling_ratio": sampling_ratio, - "aligned": aligned - }, {}] - dics_input = [{ - "X": ["roi_align_input"], - "ROIs": ["ROIs"], - "RoisNum": ["RoisNum"] - }, { - "X": ["roi_align_input"], - "ROIs": ["ROIs"] - }] - program_input = [{ - "roi_align_input": - TensorConfig(data_gen=partial( - generate_input1, dics, batch)), - "ROIs": - TensorConfig(data_gen=partial( - generate_input2, dics, batch)), - "RoisNum": - TensorConfig(data_gen=partial( - generate_input3, dics, batch)) - }, { - "roi_align_input": - TensorConfig(data_gen=partial( - generate_input1, dics, batch)), - "ROIs": - TensorConfig(data_gen=partial( - generate_input2, dics, batch), - lod=[[32, 3]]) - }] - ops_config = [{ - "op_type": - "roi_align", - "op_inputs": - dics_input[num_input], - "op_outputs": { - "Out": ["roi_align_out"] + dics = [ + { + "spatial_scale": spatial_scale, + "pooled_height": pooled_height, + "pooled_width": pooled_width, + "sampling_ratio": sampling_ratio, + "aligned": aligned, + }, + {}, + ] + dics_input = [ + { + "X": ["roi_align_input"], + "ROIs": ["ROIs"], + "RoisNum": ["RoisNum"], + }, + { + "X": ["roi_align_input"], + "ROIs": ["ROIs"], + }, + ] + program_input = [ + { + "roi_align_input": TensorConfig( + data_gen=partial( + generate_input1, dics, batch + ) + ), + "ROIs": TensorConfig( + data_gen=partial( + generate_input2, dics, batch + ) + ), + "RoisNum": TensorConfig( + data_gen=partial( + generate_input3, dics, batch + ) + ), + }, + { + "roi_align_input": TensorConfig( + data_gen=partial( + generate_input1, dics, batch + ) + ), + "ROIs": TensorConfig( + data_gen=partial( + generate_input2, dics, batch + ), + lod=[[32, 3]], + ), }, - "op_attrs": - dics[0] - }] + ] + ops_config = [ + { + "op_type": "roi_align", + "op_inputs": dics_input[num_input], + "op_outputs": { + "Out": ["roi_align_out"] + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs=program_input[num_input], - outputs=["roi_align_out"]) + outputs=["roi_align_out"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.num_input == 0: self.dynamic_shape.min_input_shape = { "roi_align_input": [1, 256, 32, 32], "ROIs": [3, 4], - "RoisNum": [1] + "RoisNum": [1], } self.dynamic_shape.max_input_shape = { "roi_align_input": [1, 256, 64, 64], "ROIs": [3, 4], - "RoisNum": [1] + "RoisNum": [1], } self.dynamic_shape.opt_input_shape = { "roi_align_input": [1, 256, 64, 64], "ROIs": [3, 4], - "RoisNum": [1] + "RoisNum": [1], } elif self.num_input == 1: self.dynamic_shape.min_input_shape = { "roi_align_input": [1, 256, 32, 32], - "ROIs": [3, 4] + "ROIs": [3, 4], } self.dynamic_shape.max_input_shape = { "roi_align_input": [1, 256, 64, 64], - "ROIs": [3, 4] + "ROIs": [3, 4], } self.dynamic_shape.opt_input_shape = { "roi_align_input": [1, 256, 64, 64], - "ROIs": [3, 4] + "ROIs": [3, 4], } def clear_dynamic_shape(): @@ -159,29 +176,33 @@ class TrtConvertRoiAlignTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if len(program_config.inputs) == 3: return True return False - self.add_skip_case(teller1, SkipReasons.TRT_NOT_SUPPORT, - "INPUT RoisNum NOT SUPPORT") + self.add_skip_case( + teller1, SkipReasons.TRT_NOT_SUPPORT, "INPUT RoisNum NOT SUPPORT" + ) def test(self): self.add_skip_trt_case() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roll.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roll.py index 675054317d9b17cab6f65bc66e5aea5880352861..8217b3e8d8506e602abcb0f77eb96e4962dcf5c5 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roll.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_roll.py @@ -22,7 +22,6 @@ import unittest class TrtConvertRollTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -32,43 +31,44 @@ class TrtConvertRollTest(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]]): return np.ones([1, 56, 56, 192]).astype(np.float32) for axis in [[1, 2]]: for shifts in [[-1, -1], [-3, -3]]: - dics = [{ - "axis": axis, - "shifts": shifts, - }] - - ops_config = [{ - "op_type": "roll", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["roll_output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "axis": axis, + "shifts": shifts, + } + ] + + ops_config = [ + { + "op_type": "roll", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["roll_output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1, dics)) + "input_data": TensorConfig( + data_gen=partial(generate_input1, dics) + ) }, - outputs=["roll_output_data"]) + outputs=["roll_output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 56, 56, 192] @@ -103,19 +103,23 @@ class TrtConvertRollTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-4 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-4 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scatter_nd_add.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scatter_nd_add.py index 4756c62ae887b263e53e8880382d903627696765..9376ade22a2be5314a7d9e8f58d8bb0948335636 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scatter_nd_add.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_scatter_nd_add.py @@ -22,12 +22,10 @@ import unittest class TrtConvertScatterNd(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([6]).astype(np.float32) @@ -37,38 +35,42 @@ class TrtConvertScatterNd(TrtLayerAutoScanTest): def generate_input3(): return np.random.random([4]).astype(np.float32) - ops_config = [{ - "op_type": "scatter_nd_add", - "op_inputs": { - "X": ["input_data"], - "Index": ["index_data"], - "Updates": ["update_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "scatter_nd_add", + "op_inputs": { + "X": ["input_data"], + "Index": ["index_data"], + "Updates": ["update_data"], + }, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) for i in range(10): program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1)), - "index_data": - TensorConfig(data_gen=partial(generate_input2)), - "update_data": - TensorConfig(data_gen=partial(generate_input3)), + "input_data": TensorConfig( + data_gen=partial(generate_input1) + ), + "index_data": TensorConfig( + data_gen=partial(generate_input2) + ), + "update_data": TensorConfig( + data_gen=partial(generate_input3) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1], @@ -100,14 +102,14 @@ class TrtConvertScatterNd(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 5), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 5), 1e-5 + yield self.create_inference_config(), (0, 5), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 4), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 4), 1e-5 + yield self.create_inference_config(), (1, 4), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shape.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shape.py index 610f4a9425fbaf3ecda649e89df1b21d9b1984a2..363152a4d00ec2c79fc76cbd4096a4c40a4fbe6d 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shape.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shape.py @@ -22,12 +22,10 @@ import unittest class TrtConvertSumTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(batch): if self.dims == 4: return np.ones([batch, 3, 24, 24]).astype(np.float32) @@ -41,31 +39,31 @@ class TrtConvertSumTest(TrtLayerAutoScanTest): for dims in [1, 2, 3, 4]: for batch in [1, 4]: self.dims = dims - ops_config = [{ - "op_type": "shape", - "op_inputs": { - "Input": ["input1"] - }, - "op_outputs": { - "Out": ["output"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "shape", + "op_inputs": {"Input": ["input1"]}, + "op_outputs": {"Out": ["output"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1": - TensorConfig(data_gen=partial(generate_input1, batch)) + "input1": TensorConfig( + data_gen=partial(generate_input1, batch) + ) }, - outputs=["output"]) + outputs=["output"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): if self.dims == 4: self.dynamic_shape.min_input_shape = {"input1": [1, 3, 24, 24]} @@ -87,7 +85,7 @@ class TrtConvertSumTest(TrtLayerAutoScanTest): } def generate_trt_nodes_num(dynamic_shape): - if (not dynamic_shape): + if not dynamic_shape: return 0, 3 return 1, 2 @@ -100,17 +98,19 @@ class TrtConvertSumTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - False), 1e-5 + False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - False), 1e-5 + False + ), 1e-3 # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5 + yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shuffle_channel.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shuffle_channel.py index a53b61a00727bc954a3f829843e95d273d93d1e4..f27bed02771029b64dbfd22aa4f18bfcb9e5169a 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shuffle_channel.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_shuffle_channel.py @@ -22,44 +22,41 @@ import unittest class TrtConvertShuffleChannelTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]], batch): return np.ones([batch, 6, 24, 24]).astype(np.float32) for batch in [1, 2, 4]: for group in [1, 2, 3]: dics = [{"group": group}, {}] - ops_config = [{ - "op_type": "shuffle_channel", - "op_inputs": { - "X": ["shuffle_channel_input"] - }, - "op_outputs": { - "Out": ["shuffle_channel_out"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "shuffle_channel", + "op_inputs": {"X": ["shuffle_channel_input"]}, + "op_outputs": {"Out": ["shuffle_channel_out"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "shuffle_channel_input": - TensorConfig( - data_gen=partial(generate_input1, dics, batch)) + "shuffle_channel_input": TensorConfig( + data_gen=partial(generate_input1, dics, batch) + ) }, - outputs=["shuffle_channel_out"]) + outputs=["shuffle_channel_out"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "shuffle_channel_input": [1, 6, 24, 24] @@ -78,8 +75,10 @@ class TrtConvertShuffleChannelTest(TrtLayerAutoScanTest): def generate_trt_nodes_num(attrs, dynamic_shape): ver = paddle_infer.get_trt_compile_version() - if ver[0] * 1000 + ver[1] * 100 + ver[ - 2] * 10 < 8000 and dynamic_shape == True: + if ( + ver[0] * 1000 + ver[1] * 100 + ver[2] * 10 < 8000 + and dynamic_shape == True + ): return 0, 3 else: return 1, 2 @@ -92,19 +91,23 @@ class TrtConvertShuffleChannelTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_slice.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_slice.py index deac7ef9d2a14c8e48d970a5912ff740692ddef5..a41af25b1a6b4487ce1b410b0bc08986522dcc53 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_slice.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_slice.py @@ -22,7 +22,6 @@ import unittest class TrtConvertSliceTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -34,13 +33,17 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest): start = 0 end = 0 if attrs[0]["starts"][x] < 0: - start = attrs[0]["starts"][x] + inputs['input_data'].shape[ - attrs[0]["axes"][x]] + start = ( + attrs[0]["starts"][x] + + inputs['input_data'].shape[attrs[0]["axes"][x]] + ) else: start = attrs[0]["starts"][x] if attrs[0]["ends"][x] < 0: - end = attrs[0]["ends"][x] + inputs['input_data'].shape[ - attrs[0]["axes"][x]] + end = ( + attrs[0]["ends"][x] + + inputs['input_data'].shape[attrs[0]["axes"][x]] + ) else: end = attrs[0]["ends"][x] start = max(0, start) @@ -51,12 +54,11 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest): for x in attrs[0]["decrease_axis"]: if x < 0: return False - if (out_shape[x] != 1): + if out_shape[x] != 1: return False return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]]): return np.random.random([6, 6, 64, 64]).astype(np.float32) @@ -65,41 +67,44 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest): for ends in [[2, 2], [5, 5], [1, -1]]: for decrease_axis in [[], [1], [2], [-1], [-100]]: for infer_flags in [[-1]]: - dics = [{ - "axes": axes, - "starts": starts, - "ends": ends, - "decrease_axis": decrease_axis, - "infer_flags": infer_flags - }] - - ops_config = [{ - "op_type": "slice", - "op_inputs": { - "Input": ["input_data"] - }, - "op_outputs": { - "Out": ["slice_output_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "axes": axes, + "starts": starts, + "ends": ends, + "decrease_axis": decrease_axis, + "infer_flags": infer_flags, + } + ] + + ops_config = [ + { + "op_type": "slice", + "op_inputs": {"Input": ["input_data"]}, + "op_outputs": { + "Out": ["slice_output_data"] + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig( - data_gen=partial(generate_input1, dics)) + "input_data": TensorConfig( + data_gen=partial(generate_input1, dics) + ) }, - outputs=["slice_output_data"]) + outputs=["slice_output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]} self.dynamic_shape.max_input_shape = {"input_data": [8, 8, 64, 64]} @@ -125,19 +130,23 @@ class TrtConvertSliceTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-4 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-4 + attrs, True + ), 1e-3 def test(self): # TODO(inference): fix. diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_split.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_split.py index e8c283acc3b8fe8a32fe130368afde6fccb40d4e..0d81712209bbb9b02335ade646f7009387f4f204 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_split.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_split.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertSplitTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -35,13 +34,13 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): if len(inputs['split_input'].shape) <= attrs[0]['axis']: return False - #Sections and num cannot both be equal to 0. + # Sections and num cannot both be equal to 0. if len(attrs[0]['sections']) == 0: if attrs[0]['num'] == 0: return False - #When sections and num are not both equal to 0, sections has higher priority. - #The sum of sections should be equal to the input size. + # When sections and num are not both equal to 0, sections has higher priority. + # The sum of sections should be equal to the input size. if len(attrs[0]['sections']) != 0: if attrs[0]['num'] != 0: return False @@ -53,16 +52,18 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): if sum != inputs['split_input'].shape[attrs[0]['axis']]: return False - #The size of num should be equal to the input dimension. + # The size of num should be equal to the input dimension. if attrs[0]['num'] != 0: if len(outputs) != attrs[0]['num']: return False - #Test AxisTensor and SectionsTensorList + # Test AxisTensor and SectionsTensorList if self.num_input == 0: - if self.dims == 2 and attrs[0]['sections'] == [ - 10, 14 - ] and len(outputs) == 2: + if ( + self.dims == 2 + and attrs[0]['sections'] == [10, 14] + and len(outputs) == 2 + ): return True else: return False @@ -70,7 +71,6 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]], batch): if self.dims == 4: return np.random.random([batch, 3, 3, 24]).astype(np.float32) @@ -93,72 +93,95 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): for num_input in [0, 1]: for dims in [1, 2, 3, 4]: for batch in [3, 6, 9]: - for Out in [["output_var0", "output_var1"], - ["output_var0", "output_var1", "output_var2"]]: - for sections in [[], [1, 2], [2, 1], [10, 14], - [1, 1, 1], [2, 2, 2], [3, 3, 3], - [3, 7, 14]]: + for Out in [ + ["output_var0", "output_var1"], + ["output_var0", "output_var1", "output_var2"], + ]: + for sections in [ + [], + [1, 2], + [2, 1], + [10, 14], + [1, 1, 1], + [2, 2, 2], + [3, 3, 3], + [3, 7, 14], + ]: for num in [0, 3]: for axis in [0, 1, 2, 3]: self.batch = batch self.num_input = num_input self.dims = dims - dics = [{ - "sections": sections, - "num": num, - "axis": axis - }, {}] - - dics_intput = [{ - "X": ["split_input"], - "AxisTensor": ["AxisTensor"], - "SectionsTensorList": [ - "SectionsTensorList1", - "SectionsTensorList2" - ] - }, { - "X": ["split_input"] - }] - dics_intputs = [{ - "AxisTensor": - TensorConfig(data_gen=partial( - generate_AxisTensor, dics)), - "SectionsTensorList1": - TensorConfig(data_gen=partial( - generate_SectionsTensorList1, - dics)), - "SectionsTensorList2": - TensorConfig(data_gen=partial( - generate_SectionsTensorList2, dics)) - }, {}] - - ops_config = [{ - "op_type": - "split", - "op_inputs": - dics_intput[num_input], - "op_outputs": { - "Out": Out + dics = [ + { + "sections": sections, + "num": num, + "axis": axis, + }, + {}, + ] + + dics_intput = [ + { + "X": ["split_input"], + "AxisTensor": ["AxisTensor"], + "SectionsTensorList": [ + "SectionsTensorList1", + "SectionsTensorList2", + ], }, - "op_attrs": - dics[0] - }] + {"X": ["split_input"]}, + ] + dics_intputs = [ + { + "AxisTensor": TensorConfig( + data_gen=partial( + generate_AxisTensor, dics + ) + ), + "SectionsTensorList1": TensorConfig( + data_gen=partial( + generate_SectionsTensorList1, + dics, + ) + ), + "SectionsTensorList2": TensorConfig( + data_gen=partial( + generate_SectionsTensorList2, + dics, + ) + ), + }, + {}, + ] + + ops_config = [ + { + "op_type": "split", + "op_inputs": dics_intput[num_input], + "op_outputs": {"Out": Out}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights=dics_intputs[num_input], inputs={ - "split_input": - TensorConfig(data_gen=partial( - generate_input1, dics, batch)) + "split_input": TensorConfig( + data_gen=partial( + generate_input1, dics, batch + ) + ) }, - outputs=Out) + outputs=Out, + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 4: self.dynamic_shape.min_input_shape = { @@ -216,30 +239,35 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): - def teller1(program_config, predictor_config): if len(program_config.weights) == 3: return True return False self.add_skip_case( - teller1, SkipReasons.TRT_NOT_SUPPORT, - "INPUT AxisTensor AND SectionsTensorList NOT SUPPORT.") + teller1, + SkipReasons.TRT_NOT_SUPPORT, + "INPUT AxisTensor AND SectionsTensorList NOT SUPPORT.", + ) def test(self): self.add_skip_trt_case() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_squeeze2.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_squeeze2.py index f82791a59123356c98a17d1287d1a2c1cc1d8352..b8a905f04711875b384fb088b3dc39e84b0583c4 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_squeeze2.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_squeeze2.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertSplitTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs attrs = [ @@ -40,25 +39,25 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): self.dims = dims self.axes = axes dics = [{"axes": axes}] - ops_config = [{ - "op_type": "squeeze2", - "op_inputs": { - "X": ["in_data"] - }, - "op_outputs": { - "Out": ["out_data"], - "XShape": ["XShape_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "squeeze2", + "op_inputs": {"X": ["in_data"]}, + "op_outputs": { + "Out": ["out_data"], + "XShape": ["XShape_data"], + }, + "op_attrs": dics[0], + } + ] # new_axes is the update of axes new_axes = list(axes) for i in range(len(new_axes)): - if (new_axes[i] < 0): + if new_axes[i] < 0: new_axes[i] += dims - if (max(new_axes) >= dims): + if max(new_axes) >= dims: continue - # generate input data + # generate input data self.input_shape = [1] * dims for i in range(dims): self.input_shape[i] = np.random.randint(1, 20) @@ -68,24 +67,26 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): for i in new_axes: self.input_shape[i] = 1 return np.random.random(self.input_shape).astype( - np.float32) + np.float32 + ) ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "in_data": - TensorConfig( - data_gen=partial(generate_input1, dics, batch)) + "in_data": TensorConfig( + data_gen=partial(generate_input1, dics, batch) + ) }, - outputs=["out_data"]) + outputs=["out_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): max_shape = list(self.input_shape) min_shape = list(self.input_shape) @@ -112,19 +113,23 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_stack.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_stack.py index cfae56fc2b630ec3f4c6fa954146c0a80caadf1f..90047400a337240efc514bc2a73c95dd914abd0d 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_stack.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_stack.py @@ -22,7 +22,6 @@ import unittest class TrtConvertStackTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -31,14 +30,13 @@ class TrtConvertStackTest(TrtLayerAutoScanTest): attrs = [ program_config.ops[i].attrs for i in range(len(program_config.ops)) ] - #The input dimension should be less than the set axis. + # The input dimension should be less than the set axis. if len(inputs['stack_input1'].shape) < attrs[0]['axis']: return False return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]], batch): if self.dims == 4: return np.random.random([batch, 3, 24, 24]).astype(np.float32) @@ -74,103 +72,107 @@ class TrtConvertStackTest(TrtLayerAutoScanTest): for axis in [-2, -1, 0, 1, 2, 3]: self.dims = dims dics = [{"axis": axis}, {}] - ops_config = [{ - "op_type": "stack", - "op_inputs": { - "X": - ["stack_input1", "stack_input2", "stack_input3"] - }, - "op_outputs": { - "Y": ["stack_output"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "stack", + "op_inputs": { + "X": [ + "stack_input1", + "stack_input2", + "stack_input3", + ] + }, + "op_outputs": {"Y": ["stack_output"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "stack_input1": - TensorConfig( - data_gen=partial(generate_input1, dics, batch)), - "stack_input2": - TensorConfig( - data_gen=partial(generate_input2, dics, batch)), - "stack_input3": - TensorConfig( - data_gen=partial(generate_input3, dics, batch)) + "stack_input1": TensorConfig( + data_gen=partial(generate_input1, dics, batch) + ), + "stack_input2": TensorConfig( + data_gen=partial(generate_input2, dics, batch) + ), + "stack_input3": TensorConfig( + data_gen=partial(generate_input3, dics, batch) + ), }, - outputs=["stack_output"]) + outputs=["stack_output"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 4: self.dynamic_shape.min_input_shape = { "stack_input1": [1, 3, 24, 24], "stack_input2": [1, 3, 24, 24], - "stack_input3": [1, 3, 24, 24] + "stack_input3": [1, 3, 24, 24], } self.dynamic_shape.max_input_shape = { "stack_input1": [4, 3, 48, 48], "stack_input2": [4, 3, 48, 48], - "stack_input3": [4, 3, 48, 48] + "stack_input3": [4, 3, 48, 48], } self.dynamic_shape.opt_input_shape = { "stack_input1": [1, 3, 24, 24], "stack_input2": [1, 3, 24, 24], - "stack_input3": [1, 3, 24, 24] + "stack_input3": [1, 3, 24, 24], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "stack_input1": [1, 3, 24], "stack_input2": [1, 3, 24], - "stack_input3": [1, 3, 24] + "stack_input3": [1, 3, 24], } self.dynamic_shape.max_input_shape = { "stack_input1": [4, 3, 48], "stack_input2": [4, 3, 48], - "stack_input3": [4, 3, 48] + "stack_input3": [4, 3, 48], } self.dynamic_shape.opt_input_shape = { "stack_input1": [1, 3, 24], "stack_input2": [1, 3, 24], - "stack_input3": [1, 3, 24] + "stack_input3": [1, 3, 24], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "stack_input1": [1, 24], "stack_input2": [1, 24], - "stack_input3": [1, 24] + "stack_input3": [1, 24], } self.dynamic_shape.max_input_shape = { "stack_input1": [4, 48], "stack_input2": [4, 48], - "stack_input3": [4, 48] + "stack_input3": [4, 48], } self.dynamic_shape.opt_input_shape = { "stack_input1": [1, 24], "stack_input2": [1, 24], - "stack_input3": [1, 24] + "stack_input3": [1, 24], } elif self.dims == 1: self.dynamic_shape.min_input_shape = { "stack_input1": [24], "stack_input2": [24], - "stack_input3": [24] + "stack_input3": [24], } self.dynamic_shape.max_input_shape = { "stack_input1": [48], "stack_input2": [48], - "stack_input3": [48] + "stack_input3": [48], } self.dynamic_shape.opt_input_shape = { "stack_input1": [24], "stack_input2": [24], - "stack_input3": [24] + "stack_input3": [24], } def clear_dynamic_shape(): @@ -191,19 +193,23 @@ class TrtConvertStackTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_sum.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_sum.py index a982a26362f473c629c6263b2b132e4fd1048dcb..819115fb5950269b51aeef94e25c2b9ddc306370 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_sum.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_sum.py @@ -22,12 +22,10 @@ import unittest class TrtConvertSumTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(batch): if self.dims == 4: return np.ones([batch, 3, 24, 24]).astype(np.float32) @@ -61,99 +59,101 @@ class TrtConvertSumTest(TrtLayerAutoScanTest): for dims in [1, 2, 3, 4]: for batch in [1, 4]: self.dims = dims - ops_config = [{ - "op_type": "sum", - "op_inputs": { - "X": ["input1", "input2", "input3"] - }, - "op_outputs": { - "Out": ["output"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "sum", + "op_inputs": {"X": ["input1", "input2", "input3"]}, + "op_outputs": {"Out": ["output"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1": - TensorConfig(data_gen=partial(generate_input1, batch)), - "input2": - TensorConfig(data_gen=partial(generate_input2, batch)), - "input3": - TensorConfig(data_gen=partial(generate_input3, batch)) + "input1": TensorConfig( + data_gen=partial(generate_input1, batch) + ), + "input2": TensorConfig( + data_gen=partial(generate_input2, batch) + ), + "input3": TensorConfig( + data_gen=partial(generate_input3, batch) + ), }, - outputs=["output"]) + outputs=["output"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): if self.dims == 4: self.dynamic_shape.min_input_shape = { "input1": [1, 3, 24, 24], "input2": [1, 3, 24, 24], - "input3": [1, 3, 24, 24] + "input3": [1, 3, 24, 24], } self.dynamic_shape.max_input_shape = { "input1": [4, 3, 48, 48], "input2": [4, 3, 48, 48], - "input3": [4, 3, 48, 48] + "input3": [4, 3, 48, 48], } self.dynamic_shape.opt_input_shape = { "input1": [1, 3, 24, 24], "input2": [1, 3, 24, 24], - "input3": [1, 3, 24, 24] + "input3": [1, 3, 24, 24], } elif self.dims == 3: self.dynamic_shape.min_input_shape = { "input1": [1, 3, 24], "input2": [1, 3, 24], - "input3": [1, 3, 24] + "input3": [1, 3, 24], } self.dynamic_shape.max_input_shape = { "input1": [4, 3, 48], "input2": [4, 3, 48], - "input3": [4, 3, 48] + "input3": [4, 3, 48], } self.dynamic_shape.opt_input_shape = { "input1": [1, 3, 24], "input2": [1, 3, 24], - "input3": [1, 3, 24] + "input3": [1, 3, 24], } elif self.dims == 2: self.dynamic_shape.min_input_shape = { "input1": [1, 24], "input2": [1, 24], - "input3": [1, 24] + "input3": [1, 24], } self.dynamic_shape.max_input_shape = { "input1": [4, 48], "input2": [4, 48], - "input3": [4, 48] + "input3": [4, 48], } self.dynamic_shape.opt_input_shape = { "input1": [1, 24], "input2": [1, 24], - "input3": [1, 24] + "input3": [1, 24], } elif self.dims == 1: self.dynamic_shape.min_input_shape = { "input1": [24], "input2": [24], - "input3": [24] + "input3": [24], } self.dynamic_shape.max_input_shape = { "input1": [48], "input2": [48], - "input3": [48] + "input3": [48], } self.dynamic_shape.opt_input_shape = { "input1": [24], "input2": [24], - "input3": [24] + "input3": [24], } def clear_dynamic_shape(): @@ -162,7 +162,7 @@ class TrtConvertSumTest(TrtLayerAutoScanTest): self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(dynamic_shape): - if (self.dims == 1 and not dynamic_shape): + if self.dims == 1 and not dynamic_shape: return 0, 5 return 1, 4 @@ -170,17 +170,19 @@ class TrtConvertSumTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - False), 1e-5 + False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - False), 1e-5 + False + ), 1e-3 # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5 + yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-3 def test(self): self.run_test() @@ -188,12 +190,10 @@ class TrtConvertSumTest(TrtLayerAutoScanTest): # special case when sum having olny one input class TrtConvertSumTest1(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(batch): if self.dims == 4: return np.ones([batch, 3, 24, 24]).astype(np.float32) @@ -207,31 +207,31 @@ class TrtConvertSumTest1(TrtLayerAutoScanTest): for dims in [1, 2, 3, 4]: for batch in [1, 4]: self.dims = dims - ops_config = [{ - "op_type": "sum", - "op_inputs": { - "X": ["input1"] - }, - "op_outputs": { - "Out": ["output"] - }, - "op_attrs": {} - }] + ops_config = [ + { + "op_type": "sum", + "op_inputs": {"X": ["input1"]}, + "op_outputs": {"Out": ["output"]}, + "op_attrs": {}, + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input1": - TensorConfig(data_gen=partial(generate_input1, batch)), + "input1": TensorConfig( + data_gen=partial(generate_input1, batch) + ), }, - outputs=["output"]) + outputs=["output"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(): if self.dims == 4: self.dynamic_shape.min_input_shape = {"input1": [1, 3, 24, 24]} @@ -268,7 +268,7 @@ class TrtConvertSumTest1(TrtLayerAutoScanTest): self.dynamic_shape.opt_input_shape = {} def generate_trt_nodes_num(dynamic_shape): - if (self.dims == 1 and not dynamic_shape): + if self.dims == 1 and not dynamic_shape: return 0, 3 return 1, 2 @@ -276,17 +276,19 @@ class TrtConvertSumTest1(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - False), 1e-5 + False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - False), 1e-5 + False + ), 1e-3 # for dynamic_shape generate_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-5 + yield self.create_inference_config(), generate_trt_nodes_num(True), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_tile.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_tile.py index 82c707869f88c1be19e02a822bd0193f5105f5cc..1176b3df3bac70dd721c4de90f238e35fccd45bc 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_tile.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_tile.py @@ -26,7 +26,6 @@ import hypothesis.strategies as st class TrtConvertTileTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs attrs = [ @@ -39,38 +38,37 @@ class TrtConvertTileTest(TrtLayerAutoScanTest): return True def sample_program_configs(self, *args, **kwargs): - def generate_input1(attrs: List[Dict[str, Any]]): return np.ones([1, 2, 3, 4]).astype(np.float32) dics = [{"repeat_times": kwargs['repeat_times']}] - ops_config = [{ - "op_type": "tile", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["tile_output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "tile", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["tile_output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1, dics)) + "input_data": TensorConfig( + data_gen=partial(generate_input1, dics) + ) }, - outputs=["tile_output_data"]) + outputs=["tile_output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = {"input_data": [1, 3, 32, 32]} self.dynamic_shape.max_input_shape = {"input_data": [4, 3, 64, 64]} @@ -99,19 +97,23 @@ class TrtConvertTileTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-4 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-4 + attrs, True + ), 1e-3 @given(repeat_times=st.sampled_from([[100], [1, 2], [0, 3], [1, 2, 100]])) def test(self, *args, **kwargs): diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k.py index 28509d42ee30b1de06b3c903f8587e75f6b514c1..8f779a64bf488dfc68812fce3ae9cce4dcbb986d 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertActivationTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True @@ -44,34 +43,37 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): for k in [1, 3]: self.dims = dims dics = [{"k": k}] - ops_config = [{ - "op_type": "top_k", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["output_data"], - "Indices": ["indices_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "top_k", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": { + "Out": ["output_data"], + "Indices": ["indices_data"], + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, dims, batch, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, dims, batch, dics + ) + ) }, - outputs=["output_data", "indices_data"]) + outputs=["output_data", "indices_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [1]} @@ -114,19 +116,23 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 ## for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k_v2.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k_v2.py index 651cc00d2cd7a65eef61afb317a1d81d74848327..33d6ca0a74eb78d6fa2a0ed6cb80117ceca88788 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k_v2.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_top_k_v2.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertActivationTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs attrs = [ @@ -53,40 +52,48 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): for sort in [True, False]: self.dims = dims self.sort = sort - dics = [{ - "k": k, - "axis": axis, - "largest": largest, - "sorted": sort - }] - ops_config = [{ - "op_type": "top_k_v2", - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["output_data"], - "Indices": ["indices_data"] - }, - "op_attrs": dics[0] - }] + dics = [ + { + "k": k, + "axis": axis, + "largest": largest, + "sorted": sort, + } + ] + ops_config = [ + { + "op_type": "top_k_v2", + "op_inputs": {"X": ["input_data"]}, + "op_outputs": { + "Out": ["output_data"], + "Indices": ["indices_data"], + }, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, dims, batch, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, + dims, + batch, + dics, + ) + ) }, - outputs=["output_data", "indices_data"]) + outputs=["output_data", "indices_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [1]} @@ -131,19 +138,23 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_transpose.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_transpose.py index e9604925e4ac5040c42453992a80b2a07e3e5a4c..5766f939396d439588a96e1cb7e416403bb22741 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_transpose.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_transpose.py @@ -22,7 +22,6 @@ import unittest class TrtConvertTransposeTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: inputs = program_config.inputs weights = program_config.weights @@ -32,14 +31,13 @@ class TrtConvertTransposeTest(TrtLayerAutoScanTest): program_config.ops[i].attrs for i in range(len(program_config.ops)) ] - #The shape of input and axis should be equal. + # The shape of input and axis should be equal. if len(inputs['transpose_input'].shape) != len(attrs[0]['axis']): return False return True def sample_program_configs(self): - def generate_input1(attrs: List[Dict[str, Any]], batch): if self.dims == 4: return np.ones([batch, 3, 24, 24]).astype(np.float32) @@ -50,37 +48,43 @@ class TrtConvertTransposeTest(TrtLayerAutoScanTest): for dims in [2, 3, 4]: for batch in [1, 2, 4]: - for axis in [[0, 1, 3, 2], [0, 3, 2, 1], [3, 2, 0, 1], - [0, 1, 2, 3], [0, 1, 2], [2, 0, 1], [1, 0], [0, - 1]]: + for axis in [ + [0, 1, 3, 2], + [0, 3, 2, 1], + [3, 2, 0, 1], + [0, 1, 2, 3], + [0, 1, 2], + [2, 0, 1], + [1, 0], + [0, 1], + ]: self.dims = dims dics = [{"axis": axis}, {}] - ops_config = [{ - "op_type": "transpose", - "op_inputs": { - "X": ["transpose_input"] - }, - "op_outputs": { - "Out": ["transpose_out"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "transpose", + "op_inputs": {"X": ["transpose_input"]}, + "op_outputs": {"Out": ["transpose_out"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "transpose_input": - TensorConfig( - data_gen=partial(generate_input1, dics, batch)) + "transpose_input": TensorConfig( + data_gen=partial(generate_input1, dics, batch) + ) }, - outputs=["transpose_out"]) + outputs=["transpose_out"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 4: self.dynamic_shape.min_input_shape = { @@ -134,19 +138,23 @@ class TrtConvertTransposeTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unary.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unary.py index ca4231a3561bcf91548c2fff6f08dcb8bc243095..40326fc8ca4bda60d70a6420ce65ded177b7a545 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unary.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unary.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertActivationTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True @@ -42,40 +41,54 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): for dims in [1, 2, 3, 4]: for batch in [1, 4]: for op_type in [ - "exp", "log", "sqrt", "abs", "sin", "cos", "tan", - "sinh", "cosh", "asin", "acos", "atan", "asinh", - "atanh", "ceil", "floor" + "exp", + "log", + "sqrt", + "abs", + "sin", + "cos", + "tan", + "sinh", + "cosh", + "asin", + "acos", + "atan", + "asinh", + "atanh", + "ceil", + "floor", ]: self.dims = dims dics = [{}] - ops_config = [{ - "op_type": op_type, - "op_inputs": { - "X": ["input_data"] - }, - "op_outputs": { - "Out": ["output_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": op_type, + "op_inputs": {"X": ["input_data"]}, + "op_outputs": {"Out": ["output_data"]}, + "op_attrs": dics[0], + } + ] ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial( - generate_input1, dims, batch, dics)) + "input_data": TensorConfig( + data_gen=partial( + generate_input1, dims, batch, dics + ) + ) }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): if self.dims == 1: self.dynamic_shape.min_input_shape = {"input_data": [1]} @@ -118,19 +131,23 @@ class TrtConvertActivationTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unfold.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unfold.py index 1a5e8cd88371c82c590be489f3a071dcc5a51d7a..5ec187daef4e91f938648e25a0e2b84b782db665 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unfold.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unfold.py @@ -22,46 +22,46 @@ import unittest class TrtConvertUnfold(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True def sample_program_configs(self): - def generate_input1(): return np.random.random([1, 3, 24, 24]).astype(np.float32) - ops_config = [{ - "op_type": "unfold", - "op_inputs": { - "X": ["input_data"], - }, - "op_outputs": { - "Y": ["output_data"] - }, - "op_attrs": { - "dilations": [1, 1], - "kernel_sizes": [4, 4], - "paddings": [0, 0, 0, 0], - "strides": [1, 1], + ops_config = [ + { + "op_type": "unfold", + "op_inputs": { + "X": ["input_data"], + }, + "op_outputs": {"Y": ["output_data"]}, + "op_attrs": { + "dilations": [1, 1], + "kernel_sizes": [4, 4], + "paddings": [0, 0, 0, 0], + "strides": [1, 1], + }, } - }] + ] ops = self.generate_op_config(ops_config) for i in range(10): program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "input_data": - TensorConfig(data_gen=partial(generate_input1)), + "input_data": TensorConfig( + data_gen=partial(generate_input1) + ), }, - outputs=["output_data"]) + outputs=["output_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): self.dynamic_shape.min_input_shape = { "input_data": [1, 3, 4, 4], @@ -87,14 +87,14 @@ class TrtConvertUnfold(TrtLayerAutoScanTest): self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (0, 3), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (0, 3), 1e-5 + yield self.create_inference_config(), (0, 3), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), (1, 2), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half - yield self.create_inference_config(), (1, 2), 1e-5 + yield self.create_inference_config(), (1, 2), 1e-3 def test(self): self.run_test() diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unsqueeze2.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unsqueeze2.py index fc99da714f6846c27f4cd8f516d8b639f1eb59dc..9c92ea5493cf082b9ae619d002788bb0b2c318ad 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unsqueeze2.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_unsqueeze2.py @@ -22,7 +22,6 @@ from typing import Optional, List, Callable, Dict, Any, Set class TrtConvertSplitTest(TrtLayerAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: return True @@ -34,17 +33,17 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): self.dims = dims self.axes = axes dics = [{"axes": axes}] - ops_config = [{ - "op_type": "unsqueeze2", - "op_inputs": { - "X": ["in_data"] - }, - "op_outputs": { - "Out": ["out_data"], - "XShape": ["XShape_data"] - }, - "op_attrs": dics[0] - }] + ops_config = [ + { + "op_type": "unsqueeze2", + "op_inputs": {"X": ["in_data"]}, + "op_outputs": { + "Out": ["out_data"], + "XShape": ["XShape_data"], + }, + "op_attrs": dics[0], + } + ] # generate input data self.input_shape = [1] * dims @@ -54,24 +53,26 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): def generate_input1(attrs: List[Dict[str, Any]], batch): self.input_shape[0] = batch return np.random.random(self.input_shape).astype( - np.float32) + np.float32 + ) ops = self.generate_op_config(ops_config) program_config = ProgramConfig( ops=ops, weights={}, inputs={ - "in_data": - TensorConfig( - data_gen=partial(generate_input1, dics, batch)) + "in_data": TensorConfig( + data_gen=partial(generate_input1, dics, batch) + ) }, - outputs=["out_data"]) + outputs=["out_data"], + ) yield program_config def sample_predictor_configs( - self, program_config) -> (paddle_infer.Config, List[int], float): - + self, program_config + ) -> (paddle_infer.Config, List[int], float): def generate_dynamic_shape(attrs): max_shape = list(self.input_shape) min_shape = list(self.input_shape) @@ -98,19 +99,23 @@ class TrtConvertSplitTest(TrtLayerAutoScanTest): clear_dynamic_shape() self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, False), 1e-5 + attrs, False + ), 1e-3 # for dynamic_shape generate_dynamic_shape(attrs) self.trt_param.precision = paddle_infer.PrecisionType.Float32 yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-5 self.trt_param.precision = paddle_infer.PrecisionType.Half yield self.create_inference_config(), generate_trt_nodes_num( - attrs, True), 1e-5 + attrs, True + ), 1e-3 def add_skip_trt_case(self): pass diff --git a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_squeeze2_matmul_fuse_pass.py b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_squeeze2_matmul_fuse_pass.py index a52dd0aed846595113f1543c2d3d01134787738b..0dfb24fde660ce5e65d7ecd7f5baa9e58e934a6e 100644 --- a/python/paddle/fluid/tests/unittests/ir/inference/test_trt_squeeze2_matmul_fuse_pass.py +++ b/python/paddle/fluid/tests/unittests/ir/inference/test_trt_squeeze2_matmul_fuse_pass.py @@ -47,7 +47,8 @@ class TestSqueeze2MatmulFusePass(PassAutoScanTest): min_subgraph_size=0, precision_mode=paddle_infer.PrecisionType.Float32, use_static=False, - use_calib_mode=False) + use_calib_mode=False, + ) yield config, ['mul', 'elementwise_add'], (1e-4, 1e-1) def add_ignore_pass_case(self): @@ -70,9 +71,10 @@ class TestSqueeze2MatmulFusePass(PassAutoScanTest): def sample_program_config(self, draw): # 1. Generate shape of input:X of squeeze2 x_shape = draw( - st.lists(st.integers(min_value=1, max_value=8), - min_size=2, - max_size=2)) + st.lists( + st.integers(min_value=1, max_value=8), min_size=2, max_size=2 + ) + ) # axes of squeeze2 == [2, 3] x_shape += [1, 1] axes = [2, 3] @@ -84,9 +86,10 @@ class TestSqueeze2MatmulFusePass(PassAutoScanTest): # 3. Generate legal shape of input:Y of matmul y_shape = draw( - st.lists(st.integers(min_value=1, max_value=8), - min_size=2, - max_size=2)) + st.lists( + st.integers(min_value=1, max_value=8), min_size=2, max_size=2 + ) + ) y_shape[0] = x_shape[1] # 4. Generate legal attr:axis of elementwise_add @@ -108,17 +111,11 @@ class TestSqueeze2MatmulFusePass(PassAutoScanTest): "X": ["squeeze2_x"], }, axes=axes, - outputs={ - "Out": ["squeeze2_out"], - "XShape": ["xshape"] - }, + outputs={"Out": ["squeeze2_out"], "XShape": ["xshape"]}, ) matmul_op = OpConfig( "matmul", - inputs={ - "X": ["squeeze2_out"], - "Y": ["matmul_y"] - }, + inputs={"X": ["squeeze2_out"], "Y": ["matmul_y"]}, outputs={"Out": ["matmul_out"]}, alpha=alpha, transpose_X=transpose_X, @@ -133,10 +130,7 @@ class TestSqueeze2MatmulFusePass(PassAutoScanTest): add_op = OpConfig( "elementwise_add", - inputs={ - "X": ["matmul_out"], - "Y": ["bias"] - }, + inputs={"X": ["matmul_out"], "Y": ["bias"]}, outputs={"Out": ["add_out"]}, axis=axis, ) @@ -157,9 +151,11 @@ class TestSqueeze2MatmulFusePass(PassAutoScanTest): return program_config def test(self): - self.run_and_statis(quant=False, - max_examples=50, - passes=["trt_squeeze2_matmul_fuse_pass"]) + self.run_and_statis( + quant=False, + max_examples=25, + passes=["trt_squeeze2_matmul_fuse_pass"], + ) if __name__ == "__main__": diff --git a/python/paddle/fluid/tests/unittests/ir/test_fuse_resnet_unit.py b/python/paddle/fluid/tests/unittests/ir/test_fuse_resnet_unit.py index 40697f0a6e3b15fa0af8715da4a0f1c624e58414..0411432aa369ef74f04cbdac83e313cd19dd41ef 100644 --- a/python/paddle/fluid/tests/unittests/ir/test_fuse_resnet_unit.py +++ b/python/paddle/fluid/tests/unittests/ir/test_fuse_resnet_unit.py @@ -24,13 +24,15 @@ paddle.enable_static() np.random.seed(0) -@unittest.skipIf(not paddle.is_compiled_with_cuda() - or paddle.get_cudnn_version() < 8000 - or paddle.device.cuda.get_device_capability()[0] < 7, - "only support with cuda and cudnn version is at least 8.0 " - "and device's compute capability is at least 7.0") +@unittest.skipIf( + not paddle.is_compiled_with_cuda() + or paddle.get_cudnn_version() < 8000 + or paddle.device.cuda.get_device_capability()[0] < 7 + or paddle.device.cuda.get_device_capability()[0] >= 9, + "only support with cuda and cudnn version is at least 8.0 " + "and device's compute capability is at least 7.0 and less than 9.0", +) class TestFuseResNetUnit(unittest.TestCase): - def test_fuse_resenet_unit(self): place = paddle.CUDAPlace(0) program = paddle.static.Program() @@ -38,14 +40,12 @@ class TestFuseResNetUnit(unittest.TestCase): with paddle.static.amp.fp16_guard(): with paddle.static.program_guard(program, startup_program): x = paddle.static.data("x", [1, 64, 64, 8]) - conv2d = paddle.nn.Conv2D(8, - 32, - 1, - bias_attr=False, - data_format='NHWC') - batch_norm = paddle.nn.BatchNorm(32, - act='relu', - data_layout='NHWC') + conv2d = paddle.nn.Conv2D( + 8, 32, 1, bias_attr=False, data_format='NHWC' + ) + batch_norm = paddle.nn.BatchNorm( + 32, act='relu', data_layout='NHWC' + ) out = batch_norm(conv2d(x)) graph = core.Graph(program.desc) core.get_pass("fuse_resnet_unit").apply(graph) @@ -54,15 +54,15 @@ class TestFuseResNetUnit(unittest.TestCase): after_params = paddle.static.amp.cast_model_to_fp16(after_program) exe = paddle.static.Executor(place) exe.run(startup_program) - paddle.static.amp.cast_parameters_to_fp16(place, - program, - to_fp16_var_names=params) paddle.static.amp.cast_parameters_to_fp16( - place, after_program, to_fp16_var_names=after_params) + place, program, to_fp16_var_names=params + ) + paddle.static.amp.cast_parameters_to_fp16( + place, after_program, to_fp16_var_names=after_params + ) feed = {"x": np.random.randn(1, 64, 64, 8).astype("float16")} before_out = exe.run(program, feed=feed, fetch_list=[out.name]) after_out = exe.run(after_program, feed=feed, fetch_list=[out.name]) - np.testing.assert_allclose(before_out[0], - after_out[0], - rtol=1e-05, - atol=0.005) + np.testing.assert_allclose( + before_out[0], after_out[0], rtol=1e-05, atol=0.005 + )