File size: 5,585 Bytes
8020c75 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 | from __future__ import annotations
import json
from pathlib import Path
import numpy as np
import onnxruntime as ort
import torch
from export_decoder_fp32 import (
HEAD_DIM,
HIDDEN_SIZE,
NUM_KV_HEADS,
NUM_LAYERS,
OPSET,
OUTPUT_DIR,
REPORT_DIR,
VISION_TOKENS,
VOCAB_SIZE,
DecoderBase,
compare_outputs,
flatten_cache,
inspect_graph,
load_model,
make_one_hot,
make_past,
output_names,
past_names,
)
from export_decoder_qdq_int8 import rewrite_graph
class DecoderUnified(DecoderBase):
def forward(
self,
vision_embeds: torch.Tensor,
token_one_hot: torch.Tensor,
position_ids: torch.Tensor,
past_k0: torch.Tensor,
past_k1: torch.Tensor,
past_k2: torch.Tensor,
past_k3: torch.Tensor,
past_k4: torch.Tensor,
past_k5: torch.Tensor,
past_v0: torch.Tensor,
past_v1: torch.Tensor,
past_v2: torch.Tensor,
past_v3: torch.Tensor,
past_v4: torch.Tensor,
past_v5: torch.Tensor,
):
keys = (past_k0, past_k1, past_k2, past_k3, past_k4, past_k5)
values = (past_v0, past_v1, past_v2, past_v3, past_v4, past_v5)
token_embed = torch.matmul(token_one_hot, self.lm_head.weight)
inputs_embeds = torch.cat((vision_embeds, token_embed), dim=1)
outputs = self.decoder(
inputs_embeds=inputs_embeds,
position_ids=position_ids,
past_key_values=tuple(zip(keys, values)),
use_cache=True,
return_dict=True,
)
final_hidden_state = outputs.last_hidden_state[:, -1:, :]
return (self.project_logits(final_hidden_state),) + flatten_cache(
outputs.past_key_values
)
def empty_vision() -> torch.Tensor:
return torch.empty(1, 0, HIDDEN_SIZE, dtype=torch.float32)
def empty_past() -> tuple[torch.Tensor, ...]:
return tuple(
torch.empty(1, NUM_KV_HEADS, 0, HEAD_DIM, dtype=torch.float32)
for _ in range(NUM_LAYERS * 2)
)
def export_graph(wrapper: DecoderUnified, destination: Path) -> None:
torch.manual_seed(31)
vision = torch.randn(1, VISION_TOKENS, HIDDEN_SIZE, dtype=torch.float32)
token = make_one_hot(1)
past = make_past(1)
positions = torch.arange(1, VISION_TOKENS + 2, dtype=torch.int32).unsqueeze(0)
dynamic_axes = {
"vision_embeds": {1: "vision_len"},
"position_ids": {1: "input_len"},
**{name: {2: "past_len"} for name in past_names()},
**{name: {2: "total_len"} for name in output_names()[1:]},
}
torch.onnx.export(
wrapper,
(vision, token, positions, *past),
destination,
input_names=["vision_embeds", "token_one_hot", "position_ids", *past_names()],
output_names=output_names(),
dynamic_axes=dynamic_axes,
opset_version=OPSET,
do_constant_folding=True,
dynamo=False,
)
def feeds(values: tuple[torch.Tensor, ...]) -> dict[str, np.ndarray]:
return {name: value.numpy() for name, value in zip(
["vision_embeds", "token_one_hot", "position_ids", *past_names()], values
)}
def validate_mode(
wrapper: DecoderUnified,
session: ort.InferenceSession,
label: str,
values: tuple[torch.Tensor, ...],
) -> dict:
with torch.inference_mode():
expected = wrapper(*values)
actual = session.run(None, feeds(values))
return compare_outputs(label, expected, actual)
def validate_graph(wrapper: DecoderUnified, path: Path) -> dict:
session = ort.InferenceSession(str(path), providers=["CPUExecutionProvider"])
torch.manual_seed(37)
prefill = (
torch.randn(1, VISION_TOKENS, HIDDEN_SIZE, dtype=torch.float32),
make_one_hot(1),
torch.arange(VISION_TOKENS + 1, dtype=torch.int32).unsqueeze(0),
*empty_past(),
)
step = (
empty_vision(),
make_one_hot(4),
torch.tensor([[VISION_TOKENS + 1]], dtype=torch.int32),
*make_past(VISION_TOKENS + 1),
)
return {
"prefill_zero_past": validate_mode(wrapper, session, "unified-prefill", prefill),
"step_empty_vision": validate_mode(wrapper, session, "unified-step", step),
}
def main() -> None:
OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
REPORT_DIR.mkdir(parents=True, exist_ok=True)
torch.manual_seed(0)
torch.set_grad_enabled(False)
wrapper = DecoderUnified(load_model()).eval()
fp32_path = OUTPUT_DIR / "decoder_unified_fp32.onnx"
qdq_path = OUTPUT_DIR / "decoder_unified_qdq_int8.onnx"
print(f"Exporting {fp32_path}", flush=True)
export_graph(wrapper, fp32_path)
fp32_parity = validate_graph(wrapper, fp32_path)
print(f"Quantizing {qdq_path}", flush=True)
qdq_metadata = rewrite_graph(fp32_path, qdq_path)
qdq_parity = validate_graph(wrapper, qdq_path)
report = {
"description": (
"One complete six-layer Baberu decoder graph shared by prefill and token "
"steps; weights, dimensions, vocabulary, and generation logic are unchanged"
),
"fp32": {"graph": inspect_graph(fp32_path), "parity": fp32_parity},
"qdq_int8": {
"graph": inspect_graph(qdq_path),
"quantization": qdq_metadata,
"parity": qdq_parity,
},
}
destination = REPORT_DIR / "decoder-unified-report.json"
destination.write_text(json.dumps(report, indent=2) + "\n", encoding="utf-8")
print(json.dumps(report, indent=2))
if __name__ == "__main__":
main()
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