tarekziade HF Staff
Publish MIC usage, slim fixture and reviewed Transformers expectations
c9fce36 verified | # CLIP text encoder: projected text embeddings with Transformers | |
| This page is the Transformers contract of the CLIP text encoder MIC and also a usage example. [doc-builder](https://github.com/huggingface/doc-builder/blob/main/docs/runnable-code-blocks.md) renders the `runnable` blocks below as documentation, and its pytest plugin runs them as written against `peft-internal-testing/tiny-clip-text-2`. The MIC runner runs the same blocks against each slim fixture and each checkpoint in [`integration.json`](../../integration.json). Lines marked `# doc-builder: hide` only matter there: they swap in the pinned checkpoint or variant and print the result record. | |
| ## Embed a batch of texts | |
| ```py runnable:text_embed | |
| import torch | |
| from transformers import CLIPTextModelWithProjection, AutoTokenizer, set_seed | |
| import json # doc-builder: hide | |
| import os # doc-builder: hide | |
| model_id = "peft-internal-testing/tiny-clip-text-2" | |
| model_id = os.environ.get("MIC_MODEL", model_id) # doc-builder: hide | |
| variant = json.loads(os.environ.get("MIC_MODEL_KWARGS", "{}")) # doc-builder: hide | |
| device = "cuda" if torch.cuda.is_available() else "cpu" | |
| set_seed(0) # seeds torch, NumPy, and Python: loading and dropout draw random numbers | |
| tokenizer = AutoTokenizer.from_pretrained(model_id) | |
| model = CLIPTextModelWithProjection.from_pretrained(model_id).to(device) | |
| if variant: # doc-builder: hide | |
| del model # doc-builder: hide | |
| model = CLIPTextModelWithProjection.from_pretrained(model_id, **variant).to(device) # doc-builder: hide | |
| # Two distinct texts so batch perturbation is meaningful; right padding is the CLIP default. | |
| texts = ["a photo of a cat", "a photo of a red bus on a street"] | |
| inputs = tokenizer(texts, return_tensors="pt", padding=True).to(device) | |
| model.eval() | |
| with torch.no_grad(): | |
| outputs = model(**inputs) | |
| text_embeds = outputs.text_embeds | |
| print(text_embeds.shape) | |
| print(text_embeds[0, :4]) | |
| ``` | |
| ## One training step | |
| ```py runnable:text_embed:2 | |
| # A tiny linear probe on the projected text embeddings gives a real supervised loss. | |
| probe = torch.nn.Linear(model.config.projection_dim, 2).to(device, model.dtype) | |
| labels = torch.tensor([0, 1], device=device) | |
| model.train() | |
| optimizer = torch.optim.SGD(list(model.parameters()) + list(probe.parameters()), lr=0.01) | |
| embeds = model(**inputs).text_embeds | |
| loss = torch.nn.functional.cross_entropy(probe(embeds), labels) | |
| loss.backward() | |
| before = {n: p.detach().cpu().clone() for n, p in model.named_parameters() if p.requires_grad} # doc-builder: hide | |
| optimizer.step() | |
| print(f"loss: {loss.item():.4f}") | |
| if os.environ.get("MIC_RUNNER"): # doc-builder: hide | |
| named = dict(model.named_parameters()) | |
| checked = ("text_model.encoder.layers.0.self_attn.q_proj.weight", "text_model.encoder.layers.0.mlp.fc1.weight", "text_projection.weight") | |
| result = { | |
| "runtime": { | |
| "device": model.device.type, | |
| "dtype": str(model.dtype).removeprefix("torch."), | |
| "attn_implementation": model.config._attn_implementation, | |
| }, | |
| "text_embeds": text_embeds.float().tolist(), | |
| "probe_logits": probe(embeds).float().tolist(), | |
| "training": { | |
| "loss": loss.item(), | |
| "gradients": {n: bool(named[n].grad is not None and torch.count_nonzero(named[n].grad) > 0 and torch.isfinite(named[n].grad).all()) for n in checked}, | |
| "updated": any(not torch.equal(p.detach().cpu(), before[n]) for n, p in model.named_parameters() if n in before), | |
| }, | |
| } | |
| print(json.dumps(result)) | |
| ``` | |