Merge branch 'add_siglip_converter' into 'main'

Add siglip converter to multimodal example

See merge request ADLR/megatron-lm!2214

Author: jon-barker

examples/multimodal/README.md

@@ -23,7 +23,7 @@ Follow the instructions in `megatron-lm/docs/llama_mistral.md` to download weigh
 This example uses the OpenAI CLIP `ViT-L/14@336px` Vision model. To download the weights from OpenAI and convert them to a format that can be loaded in megatron, please run the following:
 
 ```
-python examples/multimodal/clip_converter.py --download-root /some/download/folder --output /some/output/folder --tensor-parallel-size 4 --use-te
+python examples/multimodal/model_converter/clip_converter.py --download-root /some/download/folder --output /some/output/folder --tensor-parallel-size 4 --use-te
 ```
 
 ### Combined model checkpoint

examples/multimodal/clip_converter.py renamed to examples/multimodal/model_converter/clip_converter.py

@@ -0,0 +1,154 @@
+# Copyright (c) 2024, NVIDIA CORPORATION. All rights reserved.
+import argparse
+import os
+from transformers import PaliGemmaForConditionalGeneration
+import torch
+
+
+def convert(output_path, tensor_parallel_size, use_te):
+    device = "cuda"
+
+    model_id = "google/paligemma-3b-pt-448"
+    model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).eval()
+
+    model = model.to(device)
+
+    print(model.config)
+    for name, tensor in model.state_dict().items():
+        if "vision_model" not in name:
+            continue
+        shape_str = "(" + ", ".join([str(x) for x in tensor.shape]) + ")"
+        print(f"{name:<75} {shape_str:>20}")
+
+    state_dict = model.state_dict()
+    new_state_dicts = [{"model": dict()} for _ in range(tensor_parallel_size)]
+
+    def add_chunck_tensor(new_tensor, new_name, chunk_dim=None):
+        if chunk_dim is None:
+            new_tensors = [new_tensor for _ in range(tensor_parallel_size)]
+        else:
+            new_tensors = torch.chunk(new_tensor, tensor_parallel_size, dim=chunk_dim)
+
+        for i in range(tensor_parallel_size):
+            # chunk() creates a view of a bigger tensor. clone() is used here to avoid excessive storage.
+            new_state_dicts[i]["model"][new_name] = new_tensors[i].clone()
+
+            # TE sets _extra_state (for FP8 purposes), so set an empty one here for compatibility.
+            extra_state_layers = ("linear_qkv", "linear_proj", "linear_fc1", "linear_fc2")
+            is_extra_state_layer = any([l in new_name for l in extra_state_layers])
+            if use_te and is_extra_state_layer:
+                layer = new_name.split(".")[-2]
+                if layer in extra_state_layers:
+                    extra_state_name = (
+                        new_name[: new_name.rfind(".") + 1] + "_extra_state"
+                    )  # Replace the weight name.
+                    new_state_dicts[i]["model"][extra_state_name] = None
+
+    for name, tensor in state_dict.items():
+        if tensor.dtype == torch.float16:
+            state_dict[name] = tensor.to(torch.float32)
+
+    add_chunck_tensor(
+        state_dict["vision_tower.vision_model.embeddings.position_embedding.weight"],
+        "position_embeddings.weight")
+    add_chunck_tensor(
+        state_dict["vision_tower.vision_model.embeddings.patch_embedding.weight"],
+        "conv1.weight")
+    add_chunck_tensor(
+        state_dict["vision_tower.vision_model.embeddings.patch_embedding.bias"],
+        "conv1.bias")
+
+    head_dim = 72
+    num_head = 16
+    for layer_idx in range(27):
+        origin_base = f"vision_tower.vision_model.encoder.layers.{layer_idx}" 
+        target_base = f"decoder.layers.{layer_idx}"
+        
+        for param_type in ["weight", "bias"]:
+            # QKV
+            q_proj_params = state_dict[f"{origin_base}.self_attn.q_proj.{param_type}"]
+            k_proj_params = state_dict[f"{origin_base}.self_attn.k_proj.{param_type}"]
+            v_proj_params = state_dict[f"{origin_base}.self_attn.v_proj.{param_type}"]
+            # Do some tensor manipulation because megatron expect one tensor
+            # projection for the QKV in the order
+            # [(Q1, K1, V1), (Q2, K2, V2), ...] where Qi is the query of the
+            # i-th head with dimension num_head.
+            new_tensor = torch.concatenate([
+                q_proj_params.view(num_head, head_dim, -1),
+                k_proj_params.view(num_head, head_dim, -1),
+                v_proj_params.view(num_head, head_dim, -1)], axis=1).view(
+                    3*head_dim*num_head, -1)
+            if param_type == "bias":
+                new_tensor = new_tensor[:, 0]
+            new_name = f"{target_base}.self_attention.linear_qkv.{param_type}"
+            add_chunck_tensor(new_tensor, new_name, chunk_dim=0)
+            # linear_proj
+            add_chunck_tensor(
+                state_dict[f"{origin_base}.self_attn.out_proj.{param_type}"],
+                f"{target_base}.self_attention.linear_proj.{param_type}",
+                chunk_dim=1 if param_type == "weight" else None)
+            # layer_norm
+            new_name = f"{target_base}.input_layernorm.{param_type}"
+            if use_te:
+                new_name = f"{target_base}.self_attention.linear_qkv.layer_norm_{param_type}"
+            add_chunck_tensor(
+                state_dict[f"{origin_base}.layer_norm1.{param_type}"],
+                new_name)
+            # FC 1
+            add_chunck_tensor(
+                state_dict[f"{origin_base}.mlp.fc1.{param_type}"],
+                f"{target_base}.mlp.linear_fc1.{param_type}",
+                chunk_dim=0)
+            # FC 2
+            add_chunck_tensor(
+                state_dict[f"{origin_base}.mlp.fc2.{param_type}"],
+                f"{target_base}.mlp.linear_fc2.{param_type}",
+                chunk_dim=1 if param_type=="weight" else None)
+            # layer_norm
+            new_name = f"{target_base}.pre_mlp_layernorm.{param_type}"
+            if use_te:
+                new_name = f"{target_base}.mlp.linear_fc1.layer_norm_{param_type}"
+            add_chunck_tensor(
+                state_dict[f"{origin_base}.layer_norm2.{param_type}"],
+                new_name)
+
+    add_chunck_tensor(
+        state_dict["vision_tower.vision_model.post_layernorm.weight"],
+        "ln_post.weight")
+    add_chunck_tensor(
+        state_dict["vision_tower.vision_model.post_layernorm.bias"],
+        "ln_post.bias")
+
+    for i in range(tensor_parallel_size):
+        output_dir_tp = os.path.join(output_path, "iter_0000001", f"mp_rank_0{i}")
+        os.makedirs(output_dir_tp)
+        output_path_tp = os.path.join(output_dir_tp, "model_optim_rng.pt")
+        torch.save(new_state_dicts[i], output_path_tp)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(
+        description="""
+Convert SigLIP weights to megatron format.
+
+
+Example usage:
+python siglip_converter.py --tensor-parallel-size 4 --output google_paligemma_3b_pt_44_mcore_tp_4 --use-te
+
+examples/multimodal/combine_mistral_clip.sh /lustre/fsw/portfolios/llmservice/users/jbarker/workspace/checkpoints/Mistral-7B-Instruct-v0.3-mcore-tp4 google_paligemma_3b_pt_44_mcore_tp_4 mistral_7b_instruct_v0p3_google_paligemma_3b_pt_44_mcore_tp_4
+""",
+        formatter_class=argparse.RawDescriptionHelpFormatter,
+    )
+    parser.add_argument(
+        "--output", type=str, required=True, help="output directory for megatron state dict file(s)"
+    )
+    parser.add_argument(
+        "--tensor-parallel-size", type=int, default=1, help="model tensor parallel size"
+    )
+    parser.add_argument("--use-te", action="store_true", help="Use Transformer Engine")
+
+    args = parser.parse_args()
+
+    convert(args.output, args.tensor_parallel_size, args.use_te)
+
+    print("done.")