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import pycuda.driver as cuda
import pycuda.autoinit
import numpy as np
import tensorrt as trt
import torch
TRT_LOGGER = trt.Logger()
# Simple helper data class that's a little nicer to use than a 2-tuple.
class HostDeviceMem(object):
def __init__(self, host_mem, device_mem):
self.host = host_mem
self.device = device_mem
def __str__(self):
return "Host:\n" + str(self.host) + "\nDevice:\n" + str(self.device)
def __repr__(self):
return self.__str__()
# Allocates all buffers required for an engine, i.e. host/device inputs/outputs.
def allocate_buffers(engine):
inputs = []
outputs = []
bindings = []
stream = cuda.Stream()
out_shapes = []
input_shapes = []
out_names = []
max_batch_size = engine.get_profile_shape(0, 0)[2][0]
# max_batch_size = 1
for binding in engine:
binding_shape = engine.get_binding_shape(binding)
#Fix -1 dimension for proper memory allocation for batch_size > 1
if binding_shape[0] == -1:
binding_shape = (1,) + binding_shape[1:]
size = trt.volume(binding_shape) * max_batch_size
dtype = trt.nptype(engine.get_binding_dtype(binding))
# Allocate host and device buffers
host_mem = cuda.pagelocked_empty(size, dtype)
device_mem = cuda.mem_alloc(host_mem.nbytes)
# Append the device buffer to device bindings.
bindings.append(int(device_mem))
# Append to the appropriate list.
if engine.binding_is_input(binding):
inputs.append(HostDeviceMem(host_mem, device_mem))
input_shapes.append(engine.get_binding_shape(binding))
else:
outputs.append(HostDeviceMem(host_mem, device_mem))
#Collect original output shapes and names from engine
out_shapes.append(engine.get_binding_shape(binding))
out_names.append(binding)
return inputs, outputs, bindings, stream, input_shapes, out_shapes, out_names, max_batch_size
# This function is generalized for multiple inputs/outputs.
# inputs and outputs are expected to be lists of HostDeviceMem objects.
def do_inference(context, bindings, inputs, outputs, stream):
# Transfer input data to the GPU.
[cuda.memcpy_htod_async(inp.device, inp.host, stream) for inp in inputs]
# Run inference.
context.execute_async_v2(bindings=bindings, stream_handle=stream.handle)
# Transfer predictions back from the GPU.
[cuda.memcpy_dtoh_async(out.host, out.device, stream) for out in outputs]
# Synchronize the stream
stream.synchronize()
# Return only the host outputs.
return [out.host for out in outputs]
class TrtModel(object):
def __init__(self, model):
self.engine_file = model
self.engine = None
self.inputs = None
self.outputs = None
self.bindings = None
self.stream = None
self.context = None
self.input_shapes = None
self.out_shapes = None
self.max_batch_size = 1
def build(self):
with open(self.engine_file, 'rb') as f, trt.Runtime(TRT_LOGGER) as runtime:
self.engine = runtime.deserialize_cuda_engine(f.read())
self.inputs, self.outputs, self.bindings, self.stream, self.input_shapes, self.out_shapes, self.out_names, self.max_batch_size = allocate_buffers(
self.engine)
# print(self.inputs, self.outputs, self.bindings, self.stream, self.input_shapes, self.out_shapes, self.out_names, self.max_batch_size)
self.context = self.engine.create_execution_context()
self.context.active_optimization_profile = 0
def run(self, input, deflatten: bool = False, as_dict = False, use_token_type_ids=None):
# assert len(input) == 2, "You must input 2 ndarrays [attention_mask, input_ids]"
# assert len(input[0]) == len(input[1]), "Invalid input shape: {} vs {}".format(input[0].shape, input[1].shape)
# lazy load implementation
if self.engine is None:
self.build()
batch_size = input[0].shape[0]
# Allocate mem for first input
input_ids = np.array(input[0], dtype = np.int32)
allocate_place = np.prod(input_ids.shape)
self.inputs[0].host[:allocate_place] = input_ids.flatten(order='C')
# Allocate mem for second input
attent_mask = np.array(input[1], dtype = np.int32)
allocate_place = np.prod(attent_mask.shape)
self.inputs[1].host[:allocate_place] = attent_mask.flatten(order='C')
# Set binding
self.context.set_binding_shape(0, input_ids.shape)
self.context.set_binding_shape(1, attent_mask.shape)
if use_token_type_ids:
token_type_ids = np.array(input[2], dtype = np.int32)
allocate_place = np.prod(token_type_ids.shape)
self.inputs[2].host[:allocate_place] = token_type_ids.flatten(order='C')
self.context.set_binding_shape(2, token_type_ids.shape)
trt_outputs = do_inference(
self.context, bindings=self.bindings,
inputs=self.inputs, outputs=self.outputs, stream=self.stream)
# print(self.inputs, self.outputs, self.bindings, self.stream, self.input_shapes, self.out_shapes, self.out_names, self.max_batch_size)
# Reshape TRT outputs to original shape instead of flattened array
# print(trt_outputs[0].shape)
if deflatten:
out_shapes = [(batch_size, ) + out_shape[1:] for out_shape in self.out_shapes]
trt_outputs = [output[:np.prod(shape)].reshape(shape) for output, shape in zip(trt_outputs, out_shapes)]
# if as_dict:
# return {name: trt_outputs[i] for i, name in enumerate(self.out_names)}
# trt_outputs = [trt_output.reshape(-1,512) for trt_output in trt_outputs]
trt_outputs = [trt_output[:batch_size] for trt_output in trt_outputs]
return trt_outputs
def mean_pooling(token_embeddings, attention_mask):
# print(token_embeddings.shape)
# token_embeddings = model_output[0] #First element of model_output contains all token embeddings
input_mask_expanded = attention_mask.unsqueeze(-1).expand(token_embeddings.size()).float()
return torch.sum(token_embeddings * input_mask_expanded, 1) / torch.clamp(input_mask_expanded.sum(1), min=1e-9)
def encode(sentences: list, tokenizer, trt_model:TrtModel, use_token_type_ids = False, max_lengh =512):
sentences = [sentences] if isinstance(sentences, str) else sentences
# inputs = {
# k: v.numpy()
# for k, v in tokenizer(
# sentences,
# padding='max_length',
# truncation=True,
# return_tensors="pt",
# ).items()
# }
x = tokenizer(sentences, padding='max_length', truncation=True, max_length = max_lengh)
input_ids = x["input_ids"]
attention_mask = x["attention_mask"]
input_ids = np.array(input_ids, dtype = np.int32)
attention_mask = np.array(attention_mask, dtype = np.int32)
if use_token_type_ids:
token_type_ids = x["token_type_ids"]
token_type_ids = np.array(token_type_ids, dtype = np.int32)
hidden_states = trt_model.run([input_ids, attention_mask, token_type_ids ], deflatten=True, use_token_type_ids=True)
else:
hidden_states = trt_model.run([input_ids, attention_mask], deflatten=True)
sentence_embeddings = mean_pooling(torch.from_numpy(hidden_states[0]), torch.from_numpy(attention_mask))
# import json
# with open("tensorRT/embs.json", 'w') as f:
# json.dump(sentence_embeddings.tolist(), f, ensure_ascii=False)
return sentence_embeddings.numpy()
if __name__ == "__main__":
import torch
import json
from transformers import AutoTokenizer
# tokenizer = AutoTokenizer.from_pretrained("tensorRT/models/paraphrase-mpnet-base-v2")
# model = TrtModel("tensorRT/models/paraphrase-mpnet-base-v2.engine")
# tokenizer = AutoTokenizer.from_pretrained("tensorRT/models/distiluse-base-multilingual-cased-v2")
# model = TrtModel("tensorRT/models/distiluse-base-multilingual-cased-v2.endgine")
tokenizer = AutoTokenizer.from_pretrained("tensorRT/models/paraphrase-multilingual-MiniLM-L12-v2")
model = TrtModel("tensorRT/models/paraphrase-multilingual-MiniLM-L12-v2.engine")
lst_input = ["Pham Minh Chinh is Vietnam's Prime Minister"] *2
encode(lst_input, tokenizer, model, use_token_type_ids=False) |