Santacoder GPTQ support (quantized model seems awful, not sure if it's

prompting or the quantization itself).

server/text_generation_server/cli.py

@@ -160,6 +160,8 @@ def quantize(
     json_output: bool = False,
     trust_remote_code: bool = False,
     upload_to_model_id: Optional[str] = None,
+    percdamp: float = 0.01,
+    act_order: bool = False,
 ):
     download_weights(
         model_id=model_id,
@@ -176,6 +178,8 @@ def quantize(
         output_dir=output_dir,
         trust_remote_code=trust_remote_code,
         upload_to_model_id=upload_to_model_id,
+        percdamp=percdamp,
+        act_order=act_order,
     )
 
 

server/text_generation_server/models/custom_modeling/flash_santacoder_modeling.py

@@ -21,6 +21,81 @@ from text_generation_server.utils.layers import (
 def load_multi_mqa(
     config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
 ):
+
+    if config.quantize == "gptq":
+        return _load_multi_mqa_gptq(
+            config, prefix, weights, bias, head_size, num_heads, hidden_size
+        )
+    else:
+        return _load_multi_mqa(
+            config, prefix, weights, bias, head_size, num_heads, hidden_size
+        )
+
+
+def _load_multi_mqa_gptq(
+    config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
+):
+    if any("c_attn" in k for k in weights.routing.keys()) and not config.transpose:
+        world_size = weights.process_group.size()
+        rank = weights.process_group.rank()
+
+        slice_ = weights._get_slice(f"{prefix}.c_attn.qweight")
+        shape = slice_.get_shape()
+        block_size = (shape[1] - 2 * head_size) // world_size
+        start = rank * block_size
+        stop = (rank + 1) * block_size
+        assert (shape[1] - 2 * head_size) % world_size == 0
+        q_tensor = slice_[:, start:stop]
+        kv_tensor = slice_[:, -2 * head_size :]
+        qweight = torch.cat([q_tensor, kv_tensor], dim=1)
+
+        slice_ = weights._get_slice(f"{prefix}.c_attn.scales")
+        shape = slice_.get_shape()
+        block_size = (shape[1] - 2 * head_size) // world_size
+        start = rank * block_size
+        stop = (rank + 1) * block_size
+        assert (shape[1] - 2 * head_size) % world_size == 0
+        q_tensor = slice_[:, start:stop]
+        kv_tensor = slice_[:, -2 * head_size :]
+        scales = torch.cat([q_tensor, kv_tensor], dim=1)
+
+        slice_ = weights._get_slice(f"{prefix}.c_attn.qzeros")
+        shape = slice_.get_shape()
+        block_size = (shape[1] - (2 * head_size) * 4 // 32) // world_size
+        start = rank * block_size
+        stop = (rank + 1) * block_size
+        assert 2 * head_size % (32 // 4) == 0
+        q_tensor = slice_[:, start:stop]
+        kv_tensor = slice_[:, -2 * head_size * 4 // 32 :]
+        qzeros = torch.cat([q_tensor, kv_tensor], dim=1)
+
+        g_idx = weights.get_tensor(f"{prefix}.c_attn.g_idx")
+        bits = weights.get_tensor("gptq_bits").item()
+        groupsize = weights.get_tensor("gptq_groupsize").item()
+
+        weight = (qweight, qzeros, scales, g_idx, bits, groupsize)
+
+        if bias:
+            slice_ = weights._get_slice(f"{prefix}.c_attn.bias")
+            shape = slice_.get_shape()
+            block_size = (shape[0] - 2 * head_size) // world_size
+            assert (shape[0] - 2 * head_size) % world_size == 0
+            q_tensor = slice_[start:stop]
+            start = rank * block_size
+            stop = (rank + 1) * block_size
+            q_tensor = slice_[start:stop]
+            kv_tensor = slice_[-2 * head_size :]
+            bias = torch.cat([q_tensor, kv_tensor], dim=0)
+
+        return TensorParallelColumnLinear(get_linear(weight, bias, config.quantize))
+    else:
+        raise NotImplementedError("Gptq loading with santacoder is not implemented")
+
+
+def _load_multi_mqa(
+    config, prefix: str, weights, bias: bool, head_size, num_heads, hidden_size
+):
+
     if any("c_attn" in k for k in weights.routing.keys()):
         slice_ = weights._get_slice(f"{prefix}.c_attn.weight")
         shape = slice_.get_shape()
@@ -93,7 +168,9 @@ def load_col(config, prefix: str, weights, bias: bool):
     if config.transpose:
         weight = weights.get_sharded(f"{prefix}.weight", dim=1).T
     else:
-        weight = weights.get_sharded(f"{prefix}.weight", dim=0)
+        weight = weights.get_multi_weights_col(
+            [prefix], quantize=config.quantize, dim=0
+        )
 
     if bias:
         bias = weights.get_sharded(f"{prefix}.bias", dim=0)
@@ -106,7 +183,7 @@ def load_row(config, prefix: str, weights, bias: bool):
     if config.transpose:
         weight = weights.get_sharded(f"{prefix}.weight", dim=0).T
     else:
-        weight = weights.get_sharded(f"{prefix}.weight", dim=1)
+        weight = weights.get_multi_weights_row(prefix, quantize=config.quantize)
 
     if bias and weights.process_group.rank() == 0:
         # Rank is only on the first rank process

server/text_generation_server/models/flash_llama.py

@@ -3,7 +3,7 @@ import torch.distributed
 
 from opentelemetry import trace
 from transformers import AutoConfig
-from transformers.models.llama import LlamaTokenizer
+from transformers.models.llama import LlamaTokenizer, LlamaTokenizerFast
 from typing import Optional
 
 from text_generation_server.models import FlashCausalLM
@@ -34,13 +34,22 @@ class FlashLlama(FlashCausalLM):
         else:
             raise NotImplementedError("FlashLlama is only available on GPU")
 
-        tokenizer = LlamaTokenizer.from_pretrained(
-            model_id,
-            revision=revision,
-            padding_side="left",
-            truncation_side="left",
-            trust_remote_code=trust_remote_code,
-        )
+        try:
+            tokenizer = LlamaTokenizer.from_pretrained(
+                model_id,
+                revision=revision,
+                padding_side="left",
+                truncation_side="left",
+                trust_remote_code=trust_remote_code,
+            )
+        except Exception:
+            tokenizer = LlamaTokenizerFast.from_pretrained(
+                model_id,
+                revision=revision,
+                padding_side="left",
+                truncation_side="left",
+                trust_remote_code=trust_remote_code,
+            )
 
         config = AutoConfig.from_pretrained(
             model_id, revision=revision, trust_remote_code=trust_remote_code

server/text_generation_server/utils/gptq/quantize.py

@@ -240,7 +240,7 @@ class GPTQ:
         print(table.draw().split("\n")[-2])
 
     def fasterquant(
-        self, blocksize=128, percdamp=0.01, groupsize=-1, actorder=False, name=""
+        self, blocksize=128, percdamp=0.01, groupsize=-1, act_order=False, name=""
     ):
         self.layer.to(self.dev)
 
@@ -263,7 +263,7 @@ class GPTQ:
         H[dead, dead] = 1
         W[:, dead] = 0
 
-        if actorder:
+        if act_order:
             perm = torch.argsort(torch.diag(H), descending=True)
             W = W[:, perm]
             H = H[perm][:, perm]
@@ -334,7 +334,7 @@ class GPTQ:
         groupsize = groupsize if groupsize != -1 else self.columns
         g_idx = [i // groupsize for i in range(self.columns)]
         g_idx = torch.tensor(g_idx, dtype=torch.int32, device=Q.device)
-        if actorder:
+        if act_order:
             invperm = torch.argsort(perm)
             Q = Q[:, invperm]
             g_idx = g_idx[invperm]
@@ -697,7 +697,7 @@ def sequential(
                 scale, zero, g_idx, error = gptq[name].fasterquant(
                     percdamp=percdamp,
                     groupsize=groupsize,
-                    actorder=act_order,
+                    act_order=act_order,
                     name=name,
                 )
                 quantizers[f"{prefix}.{i}.{name}"] = (
@@ -775,6 +775,8 @@ def quantize(
     output_dir: str,
     trust_remote_code: bool,
     upload_to_model_id: Optional[str],
+    percdamp: float,
+    act_order: bool,
 ):
     print("loading model")
     model = AutoModelForCausalLM.from_pretrained(
@@ -795,7 +797,16 @@ def quantize(
     )
 
     tick = time.time()
-    quantizers = sequential(model, dataloader, DEV, nsamples, bits, groupsize)
+    quantizers = sequential(
+        model,
+        dataloader,
+        DEV,
+        nsamples,
+        bits,
+        groupsize,
+        percdamp=percdamp,
+        act_order=act_order,
+    )
     print(time.time() - tick)
 
     pack(model, quantizers, bits, groupsize)