support minicpm3.0

awq/models/__init__.py

@@ -25,4 +25,5 @@
 from .deepseek_v2 import DeepseekV2AWQForCausalLM
 from .minicpm import MiniCPMAWQForCausalLM
 from .internlm2 import InternLM2AWQForCausalLM
-from .qwen2vl import Qwen2VLAWQForCausalLM
+from .minicpm3 import MiniCPM3AWQForCausalLM
+from .qwen2vl import Qwen2VLAWQForCausalLM

awq/models/auto.py

@@ -35,6 +35,7 @@
     "deepseek_v2": DeepseekV2AWQForCausalLM,
     "minicpm": MiniCPMAWQForCausalLM,
     "internlm2": InternLM2AWQForCausalLM,
+    "minicpm3": MiniCPM3AWQForCausalLM,
     "qwen2_vl": Qwen2VLAWQForCausalLM,
 }
 

awq/models/base.py

@@ -85,6 +85,7 @@
     "cohere": "AutoModelForCausalLM",
     "deepseek_v2": "AutoModelForCausalLM",
     "minicpm": "AutoModelForCausalLM",
+    "minicpm3":"AutoModelForCausalLM",
     "internlm2": "AutoModelForCausalLM",
     "qwen2_vl": "AutoModelForVision2Seq",
 }

awq/models/minicpm3.py

@@ -0,0 +1,69 @@
+from .base import BaseAWQForCausalLM
+
+class MiniCPM3AWQForCausalLM(BaseAWQForCausalLM):
+    layer_type = "MiniCPMDecoderLayer"
+    max_seq_len_key = "max_position_embeddings"
+
+    @staticmethod
+    def get_model_layers(model):
+        print(model.model.layers)
+        return model.model.layers
+
+    @staticmethod
+    def get_act_for_scaling(module):
+        return dict(is_scalable=False)
+
+    @staticmethod
+    def move_embed(model, device: str):
+        model.model.embed_tokens = model.model.embed_tokens.to(device)
+
+    @staticmethod
+    def get_layers_for_scaling(module, input_feat, module_kwargs):
+        layers = []
+
+        # mlp
+        layers.append(
+            dict(
+                prev_op=module.self_attn.q_a_layernorm,
+                layers=[
+                    module.self_attn.q_b_proj,
+
+                ],
+                inp=input_feat["self_attn.q_b_proj"],
+                module2inspect=module.self_attn.q_b_proj,
+                kwargs=module_kwargs,
+            )
+        )
+
+        layers.append(
+            dict(
+                prev_op=module.self_attn.kv_a_layernorm,
+                layers=[
+                    module.self_attn.kv_b_proj,
+                ],
+                inp=input_feat["self_attn.kv_b_proj"],
+                module2inspect=module.self_attn.kv_b_proj,
+                kwargs=module_kwargs,
+            )
+        )
+
+
+        # linear 2
+        layers.append(
+            dict(
+                prev_op=module.mlp.up_proj,
+                layers=[module.mlp.down_proj],
+                inp=input_feat["mlp.down_proj"],
+            )
+        )
+
+        layers.append(
+            dict(
+                prev_op=module.post_attention_layernorm,
+                layers=[module.mlp.gate_proj,module.mlp.up_proj],
+                inp=input_feat["mlp.gate_proj"],
+                module2inspect=module.mlp
+            )
+        )
+
+        return layers

docs/examples.md

@@ -424,6 +424,102 @@ model.model.config.use_cache = model.model.generation_config.use_cache = True
 model.save_quantized(quant_path, safetensors=True, shard_size="4GB")
 ```
 
+### Another Custom Quantizer (MiniCPM3 Example)
+
+Here we introduce another custom quantizer from the MiniCPM team at OpenBMB. We only
+modify the weight clipping mechanism to make quantization work.
+
+```python
+import torch
+from transformers import AutoTokenizer
+
+from awq import AutoAWQForCausalLM
+from awq.quantize.quantizer import AwqQuantizer, clear_memory
+
+class CPM3AwqQuantizer(AwqQuantizer):
+    @torch.no_grad()
+    def _compute_best_clip(
+        self,
+        w: torch.Tensor,
+        input_feat: torch.Tensor,
+        n_grid=20,
+        max_shrink=0.5,
+        n_sample_token=512,
+    ):
+        assert w.dim() == 2
+        org_w_shape = w.shape
+        # w           [co, ci]      -> [co, 1, n_group, group size]
+        # input_feat  [n_token, ci] -> [1, n_token, n_group, group size]
+        group_size = self.group_size if self.group_size > 0 else org_w_shape[1]
+        input_feat = input_feat.view(-1, input_feat.shape[-1])
+        input_feat = input_feat.reshape(1, input_feat.shape[0], -1, group_size)
+
+        # Compute input feature step size (minimum 1)
+        step_size = max(1, input_feat.shape[1] // n_sample_token)
+        input_feat = input_feat[:, ::step_size]
+
+        w = w.reshape(org_w_shape[0], 1, -1, group_size)
+
+        oc_batch_size = 256 if org_w_shape[0] % 256 == 0 else 64  # prevent OOM
+        if org_w_shape[0] % oc_batch_size != 0:
+            oc_batch_size = org_w_shape[0]
+        assert org_w_shape[0] % oc_batch_size == 0
+        w_all = w
+        best_max_val_all = []
+
+        for i_b in range(org_w_shape[0] // oc_batch_size):
+            w = w_all[i_b * oc_batch_size : (i_b + 1) * oc_batch_size]
+
+            org_max_val = w.abs().amax(dim=-1, keepdim=True)  # co, 1, n_group, 1
+
+            best_max_val = org_max_val.clone()
+            min_errs = torch.ones_like(org_max_val) * 1e9
+            input_feat = input_feat.to(w.device)
+            org_out = (input_feat * w).sum(dim=-1)  # co, n_token, n_group
+
+            for i_s in range(int(max_shrink * n_grid)):
+                max_val = org_max_val * (1 - i_s / n_grid)
+                min_val = -max_val
+                cur_w = torch.clamp(w, min_val, max_val)
+                q_w = self.pseudo_quantize_tensor(cur_w)[0]
+                cur_out = (input_feat * q_w).sum(dim=-1)
+
+                # co, 1, n_group, 1
+                err = (cur_out - org_out).pow(2).mean(dim=1).view(min_errs.shape)
+                del cur_w
+                del cur_out
+                cur_best_idx = err < min_errs
+                min_errs[cur_best_idx] = err[cur_best_idx]
+                best_max_val[cur_best_idx] = max_val[cur_best_idx]
+            best_max_val_all.append(best_max_val)
+
+        best_max_val = torch.cat(best_max_val_all, dim=0)
+
+        clear_memory(input_feat)
+        clear_memory(org_out)
+
+        return best_max_val.squeeze(1)
+
+model_path = 'openbmb/MiniCPM3-4B'
+quant_path = 'minicpm3-4b-awq'
+quant_config = { "zero_point": True, "q_group_size": 64, "w_bit": 4, "version": "GEMM" }
+
+# Load model
+model = AutoAWQForCausalLM.from_pretrained(
+    model_path, low_cpu_mem_usage=True, use_cache=False, safetensors=False
+)
+tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
+
+# Quantize
+model.quantize(tokenizer, quant_config=quant_config, quantizer_cls=CPM3AwqQuantizer)
+
+# Save quantized model
+model.save_quantized(quant_path)
+tokenizer.save_pretrained(quant_path)
+
+print(f'Model is quantized and saved at "{quant_path}"')
+```
+
 ## Basic Inference
 
 ### Inference With GPU