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import numpy as np
from typing import List, Tuple
from tqdm import tqdm
from axengine import InferenceSession
from axengine import axclrt_provider_name, axengine_provider_name
from ml_dtypes import bfloat16
class InferManager:
def __init__(self, config, model_dir, model_type="qwen3", device_id=0):
self.provider_options = [{"device_id": device_id}]
self.config = config
self.max_seq_len = 2559
self.sub_dim = config.hidden_size // config.num_attention_heads if not hasattr(config, "head_dim") else config.head_dim
self.kv_dim = self.sub_dim * config.num_key_value_heads
self.k_caches = [
np.zeros((1, self.max_seq_len, self.kv_dim), dtype=bfloat16)
for _ in range(config.num_hidden_layers)
]
self.v_caches = [
np.zeros((1, self.max_seq_len, self.kv_dim), dtype=bfloat16)
for _ in range(config.num_hidden_layers)
]
self.decoder_sessions = []
for layer_idx in tqdm(range(config.num_hidden_layers), desc="Init InferenceSession"):
print(f"Loading {model_dir}/{model_type}_p128_l{layer_idx}_together.axmodel, device_id={device_id}")
try:
session = InferenceSession(
f"{model_dir}/{model_type}_p128_l{layer_idx}_together.axmodel", providers=[axclrt_provider_name], provider_options=self.provider_options # axcl
)
except:
session = InferenceSession(
f"{model_dir}/{model_type}_p128_l{layer_idx}_together.axmodel", providers=["AxEngineExecutionProvider"] # ax650
)
self.decoder_sessions.append(session)
try:
self.post_process_session = InferenceSession(
f"{model_dir}/{model_type}_post.axmodel", providers=[axclrt_provider_name], provider_options=self.provider_options # axcl
)
except:
self.post_process_session = InferenceSession(
f"{model_dir}/{model_type}_post.axmodel", providers=["AxEngineExecutionProvider"] # ax650
)
print("Model loaded successfully!")
@staticmethod
def _top_p(probs: np.ndarray, p: float) -> np.ndarray:
sorted_indices = np.argsort(probs)
filtered = probs.copy()
cumulative = 0
for idx in sorted_indices[::-1]:
if cumulative >= p:
filtered[idx] = 0
cumulative += filtered[idx]
return filtered / cumulative
@staticmethod
def _softmax(logits: np.ndarray) -> np.ndarray:
logits = logits - logits.max()
exp_logits = np.exp(logits)
return (exp_logits / np.sum(exp_logits)).astype(np.float64)
def post_process(self, logits, top_k=1, top_p=0.9, temperature=0.6):
logits = logits.astype(np.float32).flatten()
candidate_indices = np.argpartition(logits, -top_k)[-top_k:]
candidate_logits = logits[candidate_indices] / temperature
candidate_probs = self._softmax(candidate_logits)
candidate_probs = self._top_p(candidate_probs, top_p)
candidate_probs = candidate_probs.astype(np.float64) / candidate_probs.sum()
chosen_idx = np.random.multinomial(1, candidate_probs).argmax()
next_token = candidate_indices[chosen_idx]
return next_token, candidate_indices, candidate_probs
def gen_slice_indices(self, token_len, prefill=128, expand=128):
remaining = max(0, token_len - prefill)
extra_blocks = (remaining + expand - 1) // expand
return list(range(extra_blocks + 1))
def prefill(
self,
tokenizer,
token_ids,
embed_data,
slice_len=128,
return_last_hidden_state=False
):
"""
Prefill step for chunked inference.
"""
seq_len = len(token_ids)
slice_indices = [i for i in range(seq_len // slice_len + 1)]
print(f"slice_indices: {slice_indices}")
# total_prefill_len = (
# slice_len * slice_indices[-1]
# if slice_indices[-1] != 0
# else slice_len
# )
total_prefill_len = slice_len * (slice_indices[-1] + 1)
# slice_indices = self.gen_slice_indices(seq_len)
last_hidden_state = np.zeros((1, len(token_ids), self.config.hidden_size), dtype=bfloat16)
if total_prefill_len > 0:
for slice_idx in slice_indices:
indices = np.arange(
slice_idx * slice_len,
(slice_idx + 1) * slice_len,
dtype=np.uint32
).reshape((1, slice_len))
mask = (
np.zeros((1, slice_len, slice_len * (slice_idx + 1)))
- 65536
)
data = np.zeros((1, slice_len, self.config.hidden_size)).astype(bfloat16)
for i, t in enumerate(
range(
slice_idx * slice_len,
(slice_idx + 1) * slice_len,
)
):
if t < len(token_ids):
mask[:, i, : slice_idx * slice_len + i + 1] = 0
data[:, i : i + 1, :] = (
embed_data[t]
.reshape((1, 1, self.config.hidden_size))
.astype(bfloat16)
)
remain_len = (
seq_len - slice_idx * slice_len
if slice_idx == slice_indices[-1]
else slice_len
)
mask = mask.astype(bfloat16)
for layer_idx in range(self.config.num_hidden_layers):
input_feed = {
"K_cache": (
self.k_caches[layer_idx][:, 0 : slice_len * slice_idx, :]
if slice_idx
else np.zeros((1, 1, self.config.hidden_size), dtype=bfloat16)
),
"V_cache": (
self.v_caches[layer_idx][:, 0 : slice_len * slice_idx, :]
if slice_idx
else np.zeros((1, 1, self.config.hidden_size), dtype=bfloat16)
),
"indices": indices,
"input": data,
"mask": mask,
}
try:
outputs = self.decoder_sessions[layer_idx].run(None, input_feed, shape_group=slice_idx + 1)
except:
print(f"layer_idx is {layer_idx}, self.decoder_sessions len is {len(self.decoder_sessions)}")
raise RuntimeError("InferenceSession run failed.")
self.k_caches[layer_idx][
:,
slice_idx * slice_len : slice_idx * slice_len + remain_len,
:,
] = outputs[0][:, :remain_len, :]
self.v_caches[layer_idx][
:,
slice_idx * slice_len : slice_idx * slice_len + remain_len,
:,
] = outputs[1][:, :remain_len, :]
data = outputs[2]
last_hidden_state[:, slice_idx * slice_len : slice_idx * slice_len + remain_len, :] = data[:, :remain_len, :]
print("Slice prefill done:", slice_idx)
if return_last_hidden_state:
# Return the last hidden state for further processing
print("Returning last hidden state.")
return last_hidden_state
post_out = self.post_process_session.run(
None,
{
"input": data[
:, seq_len - (len(slice_indices) - 1) * slice_len - 1, None, :
]
}
)[0]
next_token, possible_tokens, possible_probs = self.post_process(post_out)
possible_decoded = [tokenizer.decode([t]) for t in possible_tokens]
possible_probs_str = [str((t, p)) for t, p in zip(possible_decoded, possible_probs)]
token_ids.append(next_token)
return token_ids
def decode(
self,
tokenizer,
token_ids,
embed_matrix,
prefill_len=128,
slice_len=128
):
# print("answer >>", tokenizer.decode(token_ids[-1], skip_special_tokens=True), end='', flush=True)
self.max_seq_len = 2559
mask = np.zeros((1, 1, self.max_seq_len + 1), dtype=np.float32).astype(bfloat16)
mask[:, :, :self.max_seq_len] -= 65536
seq_len = len(token_ids) - 1
if prefill_len > 0:
mask[:, :, :seq_len] = 0
for step_idx in range(self.max_seq_len):
if prefill_len > 0 and step_idx < seq_len:
continue
cur_token = token_ids[step_idx]
indices = np.array([step_idx], np.uint32).reshape((1, 1))
data = embed_matrix[cur_token, :].reshape((1, 1, self.config.hidden_size)).astype(bfloat16)
for layer_idx in range(self.config.num_hidden_layers):
input_feed = {
"K_cache": self.k_caches[layer_idx],
"V_cache": self.v_caches[layer_idx],
"indices": indices,
"input": data,
"mask": mask,
}
outputs = self.decoder_sessions[layer_idx].run(None, input_feed, shape_group=0)
self.k_caches[layer_idx][:, step_idx, :] = outputs[0][:, :, :]
self.v_caches[layer_idx][:, step_idx, :] = outputs[1][:, :, :]
data = outputs[2]
mask[..., step_idx] = 0
if step_idx < seq_len - 1:
continue
else:
post_out = self.post_process_session.run(None, {"input": data})[0]
next_token, possible_tokens, possible_probs = self.post_process(post_out)
token_ids.append(next_token)
if next_token == tokenizer.eos_token_id and next_token > seq_len:
break
# print(tokenizer.decode(next_token, skip_special_tokens=True), end='', flush=True)
return tokenizer.decode(token_ids, skip_special_tokens=True) if len(token_ids) > 1 else ""
def decode_next_token(
self,
tokenizer,
token_ids,
embed_matrix,
prefill_len=128,
slice_len=128,
step_idx=0,
):
# print("answer >>", tokenizer.decode(token_ids[-1], skip_special_tokens=True), end='', flush=True)
self.max_seq_len = 2559
mask = np.zeros((1, 1, self.max_seq_len + 1), dtype=np.float32).astype(bfloat16)
mask[:, :, :self.max_seq_len] -= 65536
seq_len = len(token_ids) - 1
if prefill_len > 0:
mask[:, :, :seq_len] = 0
cur_token = token_ids[step_idx]
indices = np.array([step_idx], np.uint32).reshape((1, 1))
data = embed_matrix[cur_token, :].reshape((1, 1, self.config.hidden_size)).astype(bfloat16)
for layer_idx in range(self.config.num_hidden_layers):
input_feed = {
"K_cache": self.k_caches[layer_idx],
"V_cache": self.v_caches[layer_idx],
"indices": indices,
"input": data,
"mask": mask,
}
outputs = self.decoder_sessions[layer_idx].run(None, input_feed, shape_group=0)
self.k_caches[layer_idx][:, step_idx, :] = outputs[0][:, :, :]
self.v_caches[layer_idx][:, step_idx, :] = outputs[1][:, :, :]
data = outputs[2]
mask[..., step_idx] = 0
if step_idx < seq_len - 1:
next_token = None
else:
post_out = self.post_process_session.run(None, {"input": data})[0]
next_token, possible_tokens, possible_probs = self.post_process(post_out)
token_ids.append(next_token)
# if next_token == tokenizer.eos_token_id and next_token > seq_len:
return token_ids, next_token
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