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# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
# --------------------------------------------------------------------------
import time
import numpy as np
import torch
from transformers import AutoTokenizer
import onnxruntime as ort
pt_to_np = {
"torch.int32": np.int32,
"torch.int64": np.int64,
"torch.float32": np.float32,
"torch.float16": np.float16,
}
def cuda_memcpy(dst, src):
from cuda import cudart # noqa: PLC0415
cudart.cudaMemcpy(
dst.data_ptr(),
src.data_ptr(),
src.element_size() * src.nelement(),
cudart.cudaMemcpyKind.cudaMemcpyDeviceToDevice,
)
class ORTGenerator:
def __init__(self, decoder_path):
self.onnx_decoder_path = decoder_path
self.num_heads = 32
self.head_size = 80
self.num_layers = 32
self.max_sequence_length = 2048
self.device_id = 0
self.use_cuda_graph = False
self.use_traced_inputs = False
self.static_inputs_map = {}
def append_static_inputs(self, batch_size):
# Only use this function with GQA and with use_cuda_graph=True
if batch_size in self.static_inputs_map:
return
cpu_device = torch.device("cpu")
cuda_device = torch.device("cuda", self.device_id)
static_io = {}
static_io["input_ids"] = torch.zeros((batch_size, 1), dtype=torch.int32, device=cuda_device)
static_io["step"] = torch.tensor([0], dtype=torch.int64, device=cuda_device)
static_io["seqlens_k"] = torch.tensor(batch_size * [0], dtype=torch.int32, device=cuda_device)
static_io["total_sequence_length"] = torch.tensor([0], dtype=torch.int32, device=cpu_device)
cache_shape = (batch_size, self.num_heads, self.max_sequence_length, self.head_size)
for i in range(self.num_layers):
cache = torch.zeros(cache_shape, device=cuda_device, dtype=torch.float16)
static_io.update({f"past_key_{i}": cache.contiguous(), f"past_value_{i}": cache.clone().contiguous()})
static_io["logits"] = torch.zeros((batch_size, 1, 51200), dtype=torch.float16, device=cuda_device)
self.static_inputs_map[batch_size] = static_io
def get_initial_inputs_and_outputs(self, encodings_dict):
self.torch_dtype = torch.float16 if self.use_fp16 else torch.float32
input_ids = torch.tensor(encodings_dict["input_ids"], device=self.device, dtype=torch.int32)
attention_mask = torch.tensor(encodings_dict["attention_mask"], device=self.device, dtype=torch.int32)
batch_size, sequence_length = input_ids.shape
self.use_traced_inputs = (
self.use_cuda_graph
and (batch_size in self.static_inputs_map)
and self.use_buffer_share
and not self.packed_kv
)
step = (
torch.tensor([0], device=self.device, dtype=torch.int64)
if not self.use_traced_inputs
else self.static_inputs_map[batch_size]["step"]
)
seqlens_k = (
torch.tensor(batch_size * [0], device=self.device, dtype=torch.int32)
if not self.use_traced_inputs
else self.static_inputs_map[batch_size]["seqlens_k"]
)
cuda_memcpy(seqlens_k, attention_mask.sum(1).sub(1).to(torch.int32))
total_seq_length = (
torch.tensor([0], device=torch.device("cpu"), dtype=torch.int32)
if not self.use_traced_inputs
else self.static_inputs_map[batch_size]["total_sequence_length"]
)
total_seq_length[0] = sequence_length
inputs = {
"input_ids": input_ids.contiguous(),
"attention_mask": attention_mask.contiguous(),
}
if self.use_step:
inputs["step"] = step.contiguous()
if self.use_cuda_graph:
inputs["seqlens_k"] = seqlens_k.contiguous()
inputs["total_sequence_length"] = total_seq_length.contiguous()
del inputs["attention_mask"]
past_seq_length = self.max_sequence_length if self.use_buffer_share else 0
past_shape = (
(2, batch_size, self.num_heads, past_seq_length, self.head_size)
if self.packed_kv
else (batch_size, self.num_heads, past_seq_length, self.head_size)
)
if not self.use_traced_inputs:
for i in range(self.num_layers):
past = torch.zeros(past_shape, device=self.device, dtype=self.torch_dtype)
(
inputs.update({f"past_key_{i}": past.contiguous(), f"past_value_{i}": past.clone().contiguous()})
if not self.packed_kv
else inputs.update({f"past_{i}": past.contiguous()})
)
else:
for i in range(self.num_layers):
inputs.update(
{
f"past_key_{i}": self.static_inputs_map[batch_size][f"past_key_{i}"].contiguous(),
f"past_value_{i}": self.static_inputs_map[batch_size][f"past_value_{i}"].contiguous(),
}
)
logits = torch.zeros(batch_size, sequence_length, 51200, device=self.device, dtype=self.torch_dtype)
outputs = {"logits": logits.contiguous()}
if not self.use_buffer_share:
present_shape = (
(2, batch_size, self.num_heads, sequence_length, self.head_size)
if self.packed_kv
else (batch_size, self.num_heads, sequence_length, self.head_size)
)
for i in range(self.num_layers):
present = torch.zeros(present_shape, device=self.device, dtype=self.torch_dtype)
(
outputs.update(
{f"present_key_{i}": present.contiguous(), f"present_value_{i}": present.contiguous()}
)
if not self.packed_kv
else outputs.update({f"present_{i}": present.contiguous()})
)
return inputs, outputs
def apply_io_binding(self, model: ort.InferenceSession, inputs: dict, outputs: dict):
io_binding = model.io_binding()
device = None
for k, v in inputs.items():
io_binding.bind_input(
name=k,
device_type=v.device.type,
device_id=0 if v.device.type == "cpu" else v.device.index,
element_type=pt_to_np[repr(v.dtype)],
shape=tuple(v.shape),
buffer_ptr=v.data_ptr(),
)
device = v.device
for output in model.get_outputs():
name = output.name
if self.use_buffer_share and "present" in name:
v = inputs[name.replace("present", "past")]
io_binding.bind_output(
name=name,
device_type=v.device.type,
device_id=v.device.index,
element_type=(np.float16 if self.use_fp16 else np.float32),
shape=tuple(v.shape),
buffer_ptr=v.data_ptr(),
)
else:
v = outputs[name]
io_binding.bind_output(
name=name,
device_type=device.type,
device_id=0 if device.type == "cpu" else device.index,
element_type=(np.float16 if self.use_fp16 else np.float32),
shape=tuple(v.shape),
buffer_ptr=v.data_ptr(),
)
return io_binding
def create_session(
self, device_id, use_fp16=True, use_buffer_share=True, packed_kv=False, use_step=False, use_cuda_graph=False
):
self.device_id = device_id
sess_options = ort.SessionOptions()
sess_options.log_verbosity_level = 4
sess_options.log_severity_level = 4
self.use_cuda_graph = use_cuda_graph
ep = (
("CUDAExecutionProvider", {"device_id": self.device_id, "enable_cuda_graph": self.use_cuda_graph})
if self.device_id >= 0
else "CPUExecutionProvider"
)
self.sess = ort.InferenceSession(self.onnx_decoder_path, sess_options=sess_options, providers=[ep])
self.ro = ort.RunOptions()
self.device = torch.device("cuda", self.device_id) if torch.cuda.is_available() else torch.device("cpu")
self.use_fp16 = use_fp16
self.use_buffer_share = use_buffer_share
self.packed_kv = packed_kv
self.use_step = use_step
self.tokenizer = AutoTokenizer.from_pretrained("microsoft/phi-2", trust_remote_code=True)
self.tokenizer.pad_token = "[PAD]"
def generate_impl(self, encodings_dict, max_length, cuda_graph_annotation, benchmark=False):
inputs, outputs = self.get_initial_inputs_and_outputs(encodings_dict)
all_token_ids = inputs["input_ids"].clone()
batch_size, sequence_length = all_token_ids.shape
current_length = sequence_length
has_eos = torch.zeros(batch_size, device=self.device, dtype=torch.bool)
if benchmark:
latency = []
prompt_run = True
while current_length < max_length:
io_binding = self.apply_io_binding(self.sess, inputs, outputs)
if benchmark:
start = time.time()
io_binding.synchronize_inputs()
if prompt_run:
if self.use_cuda_graph:
# Disable CUDA graph for the prompt run
self.ro.add_run_config_entry("gpu_graph_id", "-1")
self.sess.run_with_iobinding(io_binding, self.ro)
if self.use_cuda_graph:
# Enable CUDA graph for the decoding run
self.ro.add_run_config_entry(
"gpu_graph_id", str(cuda_graph_annotation) if self.use_traced_inputs else "-1"
)
prompt_run = False
else:
self.sess.run_with_iobinding(io_binding, self.ro)
io_binding.synchronize_outputs()
if benchmark:
end = time.time()
latency.append(end - start)
# Sample with argmax (greedy search)
next_token_logits = outputs["logits"][:, -1, :]
next_tokens = torch.argmax(next_token_logits, dim=-1)
# Check if we previously reached EOS token id or if generated token id is EOS token id
has_eos = has_eos | next_tokens == self.tokenizer.eos_token_id
# Determine which new tokens to add to list of all token ids
# Add EOS token ids for batch entries that ended early (ragged batching scenario where some batch entries ended early and some haven't)
tokens_to_add = next_tokens.masked_fill(has_eos, self.tokenizer.eos_token_id).reshape([batch_size, 1])
all_token_ids = torch.cat([all_token_ids, tokens_to_add], dim=-1)
# Return early if all batch entries have reached EOS token id
if torch.all(has_eos):
break
# Update inputs for next inference run
current_length += 1
inputs["input_ids"] = tokens_to_add.to(torch.int32)
if self.use_traced_inputs:
cuda_memcpy(self.static_inputs_map[batch_size]["input_ids"], inputs["input_ids"])
inputs["input_ids"] = self.static_inputs_map[batch_size]["input_ids"]
if self.use_step:
inputs["step"] = torch.tensor([current_length - 1], device=self.device, dtype=torch.int64)
if self.use_traced_inputs:
cuda_memcpy(self.static_inputs_map[batch_size]["step"], inputs["step"])
inputs["step"] = self.static_inputs_map[batch_size]["step"]
if self.use_cuda_graph:
previous_seqlens_k = inputs["seqlens_k"]
inputs["seqlens_k"] = (previous_seqlens_k + (~has_eos).reshape(batch_size, 1)).to(torch.int32)
inputs["total_sequence_length"][0] = current_length
if self.use_traced_inputs:
cuda_memcpy(self.static_inputs_map[batch_size]["seqlens_k"], inputs["seqlens_k"])
inputs["seqlens_k"] = self.static_inputs_map[batch_size]["seqlens_k"]
self.static_inputs_map[batch_size]["total_sequence_length"][0] = inputs["total_sequence_length"][0]
inputs["total_sequence_length"] = self.static_inputs_map[batch_size]["total_sequence_length"]
else:
inputs["attention_mask"] = torch.cat(
[inputs["attention_mask"], (~has_eos).reshape(batch_size, 1)], 1
).to(torch.int32)
# Set logits to zeros for next inference run and re-use memory buffer
if outputs["logits"].shape[1] != 1:
outputs["logits"] = outputs["logits"][:, :1, :].contiguous()
if self.use_traced_inputs:
outputs["logits"] = self.static_inputs_map[batch_size]["logits"]
outputs["logits"].zero_()
if not self.use_buffer_share:
for i in range(self.num_layers):
if not self.packed_kv:
inputs[f"past_key_{i}"] = outputs[f"present_key_{i}"]
inputs[f"past_value_{i}"] = outputs[f"present_value_{i}"]
else:
inputs[f"past_{i}"] = outputs[f"present_{i}"]
new_sequence_length = inputs["attention_mask"].shape[1]
present_shape = (
(2, batch_size, self.num_heads, new_sequence_length, self.head_size)
if self.packed_kv
else (batch_size, self.num_heads, new_sequence_length, self.head_size)
)
for i in range(self.num_layers):
present = torch.zeros(present_shape, device=self.device, dtype=self.torch_dtype)
(
outputs.update(
{
f"present_key_{i}": present.contiguous(),
f"present_value_{i}": present.clone().contiguous(),
}
)
if not self.packed_kv
else outputs.update({f"present_{i}": present.contiguous()})
)
if benchmark:
print(
f"Batch size: {batch_size}, Sequence length: {sequence_length}, Token num: {max_length - sequence_length}"
)
print(f"Prompt letency: {1000 * latency[0]}ms, Token latency: {1000 * np.mean(latency[1:])}ms")
return
texts = self.tokenizer.batch_decode(all_token_ids, skip_special_tokens=True)
return texts
def generate(self, prompt, max_length, cuda_graph_annotation):
encodings_dict = self.tokenizer.batch_encode_plus(prompt, padding=True)
return self.generate_impl(encodings_dict, max_length, cuda_graph_annotation)
def generate_benchmark(self, prompt_shape, token_num, cuda_graph_annotation):
batch_size, sequence_length = prompt_shape
max_length = sequence_length + token_num
encodings_dict = {}
encodings_dict["input_ids"] = torch.randint(0, 50264, (batch_size, sequence_length), dtype=torch.int32).tolist()
encodings_dict["attention_mask"] = torch.ones((batch_size, sequence_length), dtype=torch.int32).tolist()
# Warm up run
self.generate_impl(encodings_dict, max_length, cuda_graph_annotation, benchmark=False)
# Benchmark run
self.generate_impl(encodings_dict, max_length, cuda_graph_annotation, benchmark=True)
def run_phi2(
onnx_model_path,
use_buffer_share,
device_id,
packed_kv=False,
use_fp16=True,
use_step=False,
use_cuda_graph=False,
run_benchmark=False,
):
generator = ORTGenerator(onnx_model_path)
generator.create_session(device_id, use_fp16, use_buffer_share, packed_kv, use_step, use_cuda_graph)
def simple_run(prompt):
example_batch_size = len(prompt)
if use_cuda_graph:
generator.append_static_inputs(batch_size=example_batch_size)
texts = generator.generate(prompt, max_length=210, cuda_graph_annotation=example_batch_size)
for i in range(len(texts)):
print("Prompt: ", prompt[i])
print("Texts: ", texts[i])
prompt = [
'''```python
def print_prime(n):
"""
Print all primes between 1 and n
"""'''
]
if not run_benchmark:
simple_run(prompt)
# Run simple benchmark. Time the decoder only.
if run_benchmark:
token_num = 32
for batch_size in [1, 2, 4, 8]:
generator.append_static_inputs(batch_size)
for sequence_length in [16, 512]:
prompt_shape = (batch_size, sequence_length)
generator.generate_benchmark(prompt_shape, token_num, cuda_graph_annotation=batch_size)
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