Granite support? (#1882)

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Fixes # (issue)


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server/requirements_cuda.txt

@@ -11,7 +11,7 @@ googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version <
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
 grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
 grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
 hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
 idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
@@ -32,15 +32,15 @@ prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
 protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
 py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
-regex==2024.4.28 ; python_version >= "3.9" and python_version < "3.13"
+regex==2024.5.10 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
 safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
 setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13"
 tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
-tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.40.1 ; python_version >= "3.9" and python_version < "3.13"
+tqdm==4.66.4 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.40.2 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13"
 urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"

server/requirements_rocm.txt

@@ -11,7 +11,7 @@ googleapis-common-protos==1.63.0 ; python_version >= "3.9" and python_version <
 grpc-interceptor==0.15.4 ; python_version >= "3.9" and python_version < "3.13"
 grpcio-reflection==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
 grpcio-status==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
-grpcio==1.62.2 ; python_version >= "3.9" and python_version < "3.13"
+grpcio==1.63.0 ; python_version >= "3.9" and python_version < "3.13"
 hf-transfer==0.1.6 ; python_version >= "3.9" and python_version < "3.13"
 huggingface-hub==0.19.4 ; python_version >= "3.9" and python_version < "3.13"
 idna==3.7 ; python_version >= "3.9" and python_version < "3.13"
@@ -32,15 +32,15 @@ prometheus-client==0.20.0 ; python_version >= "3.9" and python_version < "3.13"
 protobuf==4.25.3 ; python_version >= "3.9" and python_version < "3.13"
 py-cpuinfo==9.0.0 ; python_version >= "3.9" and python_version < "3.13"
 pyyaml==6.0.1 ; python_version >= "3.9" and python_version < "3.13"
-regex==2024.4.28 ; python_version >= "3.9" and python_version < "3.13"
+regex==2024.5.10 ; python_version >= "3.9" and python_version < "3.13"
 requests==2.31.0 ; python_version >= "3.9" and python_version < "3.13"
 safetensors==0.4.3 ; python_version >= "3.9" and python_version < "3.13"
 scipy==1.13.0 ; python_version >= "3.9" and python_version < "3.13"
 sentencepiece==0.1.99 ; python_version >= "3.9" and python_version < "3.13"
 setuptools==69.5.1 ; python_version >= "3.9" and python_version < "3.13"
 tokenizers==0.19.1 ; python_version >= "3.9" and python_version < "3.13"
-tqdm==4.66.2 ; python_version >= "3.9" and python_version < "3.13"
-transformers==4.40.1 ; python_version >= "3.9" and python_version < "3.13"
+tqdm==4.66.4 ; python_version >= "3.9" and python_version < "3.13"
+transformers==4.40.2 ; python_version >= "3.9" and python_version < "3.13"
 typer==0.6.1 ; python_version >= "3.9" and python_version < "3.13"
 typing-extensions==4.11.0 ; python_version >= "3.9" and python_version < "3.13"
 urllib3==2.2.1 ; python_version >= "3.9" and python_version < "3.13"

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -23,7 +23,6 @@ import torch.distributed
 
 from torch import nn
 from transformers.activations import ACT2FN
-from transformers.configuration_utils import PretrainedConfig
 from typing import Optional, List, Tuple
 
 from text_generation_server.utils import paged_attention, flash_attn
@@ -32,7 +31,6 @@ from text_generation_server.layers import (
     TensorParallelColumnLinear,
     TensorParallelEmbedding,
     SpeculativeHead,
-    get_linear,
 )
 from text_generation_server.layers.rotary import PositionRotaryEmbedding
 from text_generation_server.layers.layernorm import (
@@ -41,22 +39,29 @@ from text_generation_server.layers.layernorm import (
 
 
 def load_attention(config, prefix, weights):
+    bias = config.attention_bias
     if config.num_attention_heads != config.num_key_value_heads:
-        return _load_gqa(config, prefix, weights)
+        return TensorParallelColumnLinear.load_multi(
+            config,
+            prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
+            dim=0,
+            weights=weights,
+            bias=bias,
+        )
     else:
         if config.model_type == "baichuan":
             return TensorParallelColumnLinear.load_qkv(
                 config,
                 prefix=f"{prefix}.W_pack",
                 weights=weights,
-                bias=False,
+                bias=bias,
             )
         elif config.model_type == "phi3":
             return TensorParallelColumnLinear.load_qkv(
                 config,
                 prefix=f"{prefix}.qkv_proj",
                 weights=weights,
-                bias=False,
+                bias=bias,
             )
         else:
             return TensorParallelColumnLinear.load_multi(
@@ -64,33 +69,7 @@ def load_attention(config, prefix, weights):
                 prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
                 dim=0,
                 weights=weights,
-                bias=False,
-            )
-
-
-def _load_gqa(config, prefix: str, weights):
-    assert config.hidden_size % config.num_attention_heads == 0
-    assert config.num_attention_heads % weights.process_group.size() == 0
-
-    weight = weights.get_multi_weights_col(
-        prefixes=[f"{prefix}.q_proj", f"{prefix}.k_proj", f"{prefix}.v_proj"],
-        quantize=config.quantize,
-        dim=0,
-    )
-
-    if config.quantize not in ["gptq", "awq"]:
-        weight = weight.to(dtype=weights.dtype).to(device=weights.device)
-
-        head_size = config.hidden_size // config.num_attention_heads
-        num_heads = config.num_attention_heads // weights.process_group.size()
-        num_key_value_heads = config.num_key_value_heads // weights.process_group.size()
-        assert list(weight.shape) == [
-            (num_heads + 2 * num_key_value_heads) * head_size,
-            config.hidden_size,
-        ], f"{list(weight.shape)} != {[(num_heads + 2 * config.num_key_value_heads) * head_size, config.hidden_size]}"
-
-    return TensorParallelColumnLinear(
-        get_linear(weight, bias=None, quantize=config.quantize)
+                bias=bias,
             )
 
 
@@ -214,12 +193,13 @@ class LlamaMLP(nn.Module):
             )
         )
         # Fuse gate and up proj
+        bias = getattr(config, "mlp_bias", False)
         if config.model_type == "phi3":
             self.gate_up_proj = TensorParallelColumnLinear.load_gate_up(
                 config,
                 prefix=f"{prefix}.gate_up_proj",
                 weights=weights,
-                bias=False,
+                bias=bias,
             )
         else:
             self.gate_up_proj = TensorParallelColumnLinear.load_multi(
@@ -227,13 +207,13 @@ class LlamaMLP(nn.Module):
                 prefixes=[f"{prefix}.gate_proj", f"{prefix}.up_proj"],
                 weights=weights,
                 dim=0,
-                bias=False,
+                bias=bias,
             )
         self.down_proj = TensorParallelRowLinear.load(
             config,
             prefix=f"{prefix}.down_proj",
             weights=weights,
-            bias=False,
+            bias=bias,
         )
         self.intermediate_size = (
             config.intermediate_size // weights.process_group.size()
@@ -385,9 +365,14 @@ class FlashLlamaForCausalLM(torch.nn.Module):
             weights=weights,
         )
         self.model = FlashLlamaModel(prefix, config, weights)
+        if config.tie_word_embeddings:
+            suffix = "model.embed_tokens"
+        else:
+            suffix = "lm_head"
+
         self.lm_head = SpeculativeHead.load(
             config,
-            prefix="lm_head" if not prefix else f"{prefix}.lm_head",
+            prefix=suffix if not prefix else f"{prefix}.suffix",
             weights=weights,
         )