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Updated numpy and protobuf version #1321

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e358787
Updated numpy and protobug version
quic-rishinr Sep 10, 2026
5162a8a
Fix NumPy 2 scalar conversions in unit tests
quic-rishinr Sep 10, 2026
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3 changes: 1 addition & 2 deletions QEfficient/diffusers/README.md
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Expand Up @@ -34,7 +34,7 @@ QEfficient Diffusers brings the power of state-of-the-art diffusion models to Qu

### Prerequisites

Ensure you have Python 3.8+ and the required dependencies:
Ensure you have Python 3.10+ and the required dependencies:

```bash
# Create Python virtual environment (Recommended Python 3.10)
Expand Down Expand Up @@ -92,4 +92,3 @@ We welcome contributions! Please see our [Contributing Guide](../../CONTRIBUTING
- 🐛 **Issues**: [GitHub Issues](https://github.com/quic/efficient-transformers/issues)

---

2 changes: 1 addition & 1 deletion examples/dynamo/causal_lm/README.md
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Expand Up @@ -20,7 +20,7 @@ pip install -e .
pip install -r examples/dynamo/causal_lm/requirements.txt
```

> **Note:** `requirements.txt` installs PyTorch 2.13 CPU wheels and `onnxscript`/`compressed-tensors`. These override any existing torch installation. For x86_64 and aarch64 — Python 3.9–3.12.
> **Note:** `requirements.txt` installs PyTorch 2.13 CPU wheels and `onnxscript`/`compressed-tensors`. These override any existing torch installation. For x86_64 and aarch64 — Python 3.10–3.12.

### 3. HuggingFace authentication (gated models)
```bash
Expand Down
2 changes: 0 additions & 2 deletions examples/dynamo/causal_lm/requirements.txt
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Expand Up @@ -3,14 +3,12 @@

# torch — per Python version and architecture
torch==2.13.0 ; platform_machine=='aarch64'
torch @ https://download.pytorch.org/whl/cpu/torch-2.13.0%2Bcpu-cp39-cp39-manylinux_2_28_x86_64.whl ; python_version=='3.9' and platform_machine=='x86_64'
torch @ https://download.pytorch.org/whl/cpu/torch-2.13.0%2Bcpu-cp310-cp310-manylinux_2_28_x86_64.whl ; python_version=='3.10' and platform_machine=='x86_64'
torch @ https://download.pytorch.org/whl/cpu/torch-2.13.0%2Bcpu-cp311-cp311-manylinux_2_28_x86_64.whl ; python_version=='3.11' and platform_machine=='x86_64'
torch @ https://download.pytorch.org/whl/cpu/torch-2.13.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl ; python_version=='3.12' and platform_machine=='x86_64'

# torchvision — per Python version and architecture
torchvision==0.28.0 ; platform_machine=='aarch64'
torchvision @ https://download.pytorch.org/whl/cpu/torchvision-0.28.0%2Bcpu-cp39-cp39-manylinux_2_28_x86_64.whl ; python_version=='3.9' and platform_machine=='x86_64'
torchvision @ https://download.pytorch.org/whl/cpu/torchvision-0.28.0%2Bcpu-cp310-cp310-manylinux_2_28_x86_64.whl ; python_version=='3.10' and platform_machine=='x86_64'
torchvision @ https://download.pytorch.org/whl/cpu/torchvision-0.28.0%2Bcpu-cp311-cp311-manylinux_2_28_x86_64.whl ; python_version=='3.11' and platform_machine=='x86_64'
torchvision @ https://download.pytorch.org/whl/cpu/torchvision-0.28.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl ; python_version=='3.12' and platform_machine=='x86_64'
Expand Down
11 changes: 4 additions & 7 deletions pyproject.toml
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Expand Up @@ -17,7 +17,7 @@ classifiers = [
"Programming Language :: Python :: 3.12",
"Topic :: Scientific/Engineering :: Artificial Intelligence for Inference Accelerator",
]
requires-python = ">=3.8,<3.13"
requires-python = ">=3.10,<3.13"
dependencies = [
"transformers==5.5.4",
"diffusers==0.38.0",
Expand All @@ -29,8 +29,8 @@ dependencies = [
"sentencepiece==0.2.0",
"onnx==1.18.0",
"onnxruntime==1.22",
"numpy==1.26.4",
"protobuf==6.31.0",
"numpy>=2.2.6,<3",
"protobuf==6.33.6",
"onnxscript==0.2.5",
# for layerwise infra
"onnx-ir==0.2.1",
Expand All @@ -48,12 +48,9 @@ dependencies = [
"compressed-tensors==0.15.0",
"torch==2.7.0; platform_machine=='aarch64'",
# Specifying torch cpu package URL per python version, update the list once pytorch releases whl for python>3.11
"torch@https://download.pytorch.org/whl/cpu/torch-2.4.1%2Bcpu-cp38-cp38-linux_x86_64.whl ; python_version=='3.8' and platform_machine=='x86_64'",
"torch@https://download.pytorch.org/whl/cpu/torch-2.7.0%2Bcpu-cp39-cp39-manylinux_2_28_x86_64.whl ; python_version=='3.9' and platform_machine=='x86_64'",
"torch@https://download.pytorch.org/whl/cpu/torch-2.7.0%2Bcpu-cp310-cp310-manylinux_2_28_x86_64.whl ; python_version=='3.10' and platform_machine=='x86_64'",
"torch@https://download.pytorch.org/whl/cpu/torch-2.7.0%2Bcpu-cp311-cp311-manylinux_2_28_x86_64.whl ; python_version=='3.11' and platform_machine=='x86_64'",
"torch@https://download.pytorch.org/whl/cpu/torch-2.7.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl ; python_version=='3.12' and platform_machine=='x86_64'",
"torchvision@https://download.pytorch.org/whl/cpu/torchvision-0.22.0%2Bcpu-cp39-cp39-manylinux_2_28_x86_64.whl ; python_version=='3.9' and platform_machine=='x86_64'",
"torchvision@https://download.pytorch.org/whl/cpu/torchvision-0.22.0%2Bcpu-cp310-cp310-manylinux_2_28_x86_64.whl ; python_version=='3.10' and platform_machine=='x86_64'",
"torchvision@https://download.pytorch.org/whl/cpu/torchvision-0.22.0%2Bcpu-cp311-cp311-manylinux_2_28_x86_64.whl ; python_version=='3.11' and platform_machine=='x86_64'",
"torchvision@https://download.pytorch.org/whl/cpu/torchvision-0.22.0%2Bcpu-cp312-cp312-manylinux_2_28_x86_64.whl ; python_version=='3.12' and platform_machine=='x86_64'",
Expand Down Expand Up @@ -101,4 +98,4 @@ markers = [
"cli: marks CLI tests",
"finetune: marks finetune tests",
"vllm: marks vLLM tests"
]
]
4 changes: 2 additions & 2 deletions tests/unit_test/e2e/test_embedding_e2e.py
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Expand Up @@ -331,8 +331,8 @@ def test_bert_ort_mean_pooled_embedding_matches_qeff(self, tmp_export_dir):

if ort_hidden is not None:
ort_mean = ort_hidden.mean(axis=1)
pt_top = int(pt_mean.argmax(-1))
ort_top = int(ort_mean.argmax(-1))
pt_top = pt_mean.argmax(-1).item()
ort_top = ort_mean.argmax(-1).item()
assert pt_top == ort_top, f"Mean-pooled embedding argmax mismatch: QEff={pt_top}, ORT={ort_top}"


Expand Down
6 changes: 3 additions & 3 deletions tests/unit_test/e2e/test_seq_classification_e2e.py
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Expand Up @@ -194,7 +194,7 @@ def test_bert_ort_predicts_same_class_as_qeff(self, tmp_export_dir):
ort_inputs = {k: v.numpy() for k, v in inputs.items()}
output_names = [o.name for o in session.get_outputs()]
ort_out = dict(zip(output_names, session.run(output_names, ort_inputs)))
ort_class = int(ort_out["logits"].argmax(-1))
ort_class = ort_out["logits"].argmax(-1).item()
assert qeff_class == ort_class, f"Class mismatch QEff vs ORT: QEff={qeff_class}, ORT={ort_class}"

def test_bert_ort_predicts_same_class_as_hf(self, tmp_export_dir):
Expand All @@ -210,7 +210,7 @@ def test_bert_ort_predicts_same_class_as_hf(self, tmp_export_dir):
ort_inputs = {k: v.numpy() for k, v in inputs.items()}
output_names = [o.name for o in session.get_outputs()]
ort_out = dict(zip(output_names, session.run(output_names, ort_inputs)))
ort_class = int(ort_out["logits"].argmax(-1))
ort_class = ort_out["logits"].argmax(-1).item()
assert hf_class == ort_class, f"Full pipeline class mismatch: HF={hf_class}, ORT={ort_class}"

def test_bert_ort_logits_numerically_close_to_qeff(self, tmp_export_dir):
Expand Down Expand Up @@ -295,7 +295,7 @@ def test_deberta_ort_predicts_same_class_as_hf(self, tmp_export_dir):
ort_inputs = {k: v.numpy() for k, v in inputs.items()}
output_names = [o.name for o in session.get_outputs()]
ort_out = dict(zip(output_names, session.run(output_names, ort_inputs)))
ort_class = int(ort_out["logits"].argmax(-1))
ort_class = ort_out["logits"].argmax(-1).item()
assert hf_class == ort_class, f"DeBERTa pipeline mismatch: HF={hf_class}, ORT={ort_class}"
except Exception as e:
pytest.skip(f"DeBERTa-v2 not available or export failed: {e}")
20 changes: 10 additions & 10 deletions tests/unit_test/models/test_causal_lm_accuracy.py
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Expand Up @@ -703,8 +703,8 @@ def _check_ort_prefill_accuracy(self, factory, label, tmp_export_dir):
def test_gpt2_ort_prefill_produces_correct_logits(self, tmp_export_dir):
"""ORT prefill must produce logits matching QEff PyTorch."""
pt_logits, ort_logits, _, _, _, _ = self._check_ort_prefill_accuracy(make_tiny_gpt2, "GPT2", tmp_export_dir)
pt_token = int(pt_logits.argmax(-1))
ort_token = int(ort_logits.argmax(-1))
pt_token = pt_logits.argmax(-1).item()
ort_token = ort_logits.argmax(-1).item()
assert pt_token == ort_token, f"Token mismatch: PyTorch={pt_token}, ORT={ort_token}"

def test_llama_ort_session_creation_succeeds(self, tmp_export_dir):
Expand Down Expand Up @@ -786,31 +786,31 @@ def test_phi3_ort_session_creation_succeeds(self, tmp_export_dir):
def test_llama_ort_prefill_produces_correct_logits(self, tmp_export_dir):
"""ORT Llama prefill must produce logits matching QEff PyTorch."""
pt_logits, ort_logits, _, _, _, _ = self._check_ort_prefill_accuracy(make_tiny_llama, "Llama", tmp_export_dir)
pt_token = int(pt_logits.argmax(-1))
ort_token = int(ort_logits.argmax(-1))
pt_token = pt_logits.argmax(-1).item()
ort_token = ort_logits.argmax(-1).item()
assert pt_token == ort_token, f"[Llama] Token mismatch: PyTorch={pt_token}, ORT={ort_token}"

def test_mistral_ort_prefill_produces_correct_logits(self, tmp_export_dir):
"""ORT Mistral prefill must produce logits matching QEff PyTorch."""
pt_logits, ort_logits, _, _, _, _ = self._check_ort_prefill_accuracy(
make_tiny_mistral, "Mistral", tmp_export_dir
)
pt_token = int(pt_logits.argmax(-1))
ort_token = int(ort_logits.argmax(-1))
pt_token = pt_logits.argmax(-1).item()
ort_token = ort_logits.argmax(-1).item()
assert pt_token == ort_token, f"[Mistral] Token mismatch: PyTorch={pt_token}, ORT={ort_token}"

def test_qwen2_ort_prefill_produces_correct_logits(self, tmp_export_dir):
"""ORT Qwen2 prefill must produce logits matching QEff PyTorch."""
pt_logits, ort_logits, _, _, _, _ = self._check_ort_prefill_accuracy(make_tiny_qwen2, "Qwen2", tmp_export_dir)
pt_token = int(pt_logits.argmax(-1))
ort_token = int(ort_logits.argmax(-1))
pt_token = pt_logits.argmax(-1).item()
ort_token = ort_logits.argmax(-1).item()
assert pt_token == ort_token, f"[Qwen2] Token mismatch: PyTorch={pt_token}, ORT={ort_token}"

def test_phi3_ort_prefill_produces_correct_logits(self, tmp_export_dir):
"""ORT Phi3 prefill must produce logits matching QEff PyTorch."""
pt_logits, ort_logits, _, _, _, _ = self._check_ort_prefill_accuracy(make_tiny_phi3, "Phi3", tmp_export_dir)
pt_token = int(pt_logits.argmax(-1))
ort_token = int(ort_logits.argmax(-1))
pt_token = pt_logits.argmax(-1).item()
ort_token = ort_logits.argmax(-1).item()
assert pt_token == ort_token, f"[Phi3] Token mismatch: PyTorch={pt_token}, ORT={ort_token}"

def test_gpt2_ort_logits_are_finite(self, tmp_export_dir):
Expand Down
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