keep improving

Signed-off-by: youkaichao <youkaichao@gmail.com>

inference/configs/config_671B_v3.1.json

@@ -0,0 +1,23 @@
+{
+    "vocab_size": 129280,
+    "dim": 7168,
+    "inter_dim": 18432,
+    "moe_inter_dim": 2048,
+    "n_layers": 61,
+    "n_dense_layers": 3,
+    "n_heads": 128,
+    "n_routed_experts": 256,
+    "n_shared_experts": 1,
+    "n_activated_experts": 8,
+    "n_expert_groups": 8,
+    "n_limited_groups": 4,
+    "route_scale": 2.5,
+    "score_func": "sigmoid",
+    "q_lora_rank": 1536,
+    "kv_lora_rank": 512,
+    "qk_nope_head_dim": 128,
+    "qk_rope_head_dim": 64,
+    "v_head_dim": 128,
+    "dtype": "fp8",
+    "scale_fmt": "ue8m0"
+}

inference/generate.py

@@ -112,10 +112,6 @@ def main(
     with open(config) as f:
         config_dict = json.load(f)
         args = ModelArgs(**config_dict)
-        quantization_config = config_dict.get("quantization_config", None)
-        if quantization_config is not None:
-            args.scale_fmt = quantization_config.get("scale_fmt", None)
-        set_global_args(args)
     print(args)
     with torch.device("cuda"):
         model = Transformer(args)

inference/model.py

@@ -85,12 +85,6 @@ class ModelArgs:
     beta_slow: int = 1
     mscale: float = 1.
 
-global_args: Optional[ModelArgs] = None
-
-def set_global_args(args: ModelArgs):
-    global global_args
-    global_args = args
-
 
 class ParallelEmbedding(nn.Module):
     """
@@ -134,7 +128,7 @@ class ParallelEmbedding(nn.Module):
         return y
 
 
-def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None) -> torch.Tensor:
+def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] = None, scale_fmt: Optional[str] = None) -> torch.Tensor:
     """
     Applies a linear transformation to the incoming data: y = xA^T + b.
     This function supports specialized implementations based on quantization
@@ -162,8 +156,7 @@ def linear(x: torch.Tensor, weight: torch.Tensor, bias: Optional[torch.Tensor] =
         weight = weight_dequant(weight, weight.scale)
         return F.linear(x, weight, bias)
     else:
-        assert global_args is not None, "global_args is required for fp8_gemm"
-        x, scale = act_quant(x, block_size, global_args.scale_fmt)
+        x, scale = act_quant(x, block_size, scale_fmt)
         y = fp8_gemm(x, scale, weight, weight.scale)
         if bias is not None:
             y += bias
@@ -181,6 +174,7 @@ class Linear(nn.Module):
         dtype (optional): Data type for the layer. Defaults to `torch.bfloat16`.
     """
     dtype = torch.bfloat16
+    scale_fmt: Optional[str] = None
 
     def __init__(self, in_features: int, out_features: int, bias: bool = False, dtype = None):
         super().__init__()
@@ -208,7 +202,7 @@ class Linear(nn.Module):
         Returns:
             torch.Tensor: Transformed tensor after linear computation.
         """
-        return linear(x, self.weight, self.bias)
+        return linear(x, self.weight, self.bias, self.scale_fmt)
 
 
 class ColumnParallelLinear(Linear):
@@ -764,6 +758,7 @@ class Transformer(nn.Module):
         world_size = dist.get_world_size() if dist.is_initialized() else 1
         rank = dist.get_rank() if dist.is_initialized() else 0
         Linear.dtype = torch.float8_e4m3fn if args.dtype == "fp8" else torch.bfloat16
+        Linear.scale_fmt = args.scale_fmt
         super().__init__()
         self.max_seq_len = args.max_seq_len
         self.embed = ParallelEmbedding(args.vocab_size, args.dim)