improvements

server/text_generation_server/layers/attention/kv_cache.py

@@ -53,20 +53,21 @@ class KVCache:
     ):
         """Construct the key-value cache for a layer."""
         if dtype in {torch.float8_e5m2, torch.float8_e4m3fn}:
-            if (ATTENTION == "flashinfer" and SYSTEM == "cuda") or not (
-                ATTENTION == "paged" and SYSTEM == "rocm"
+            if not (
+                (ATTENTION == "flashinfer" and SYSTEM == "cuda")
+                or (ATTENTION == "paged" and SYSTEM == "rocm")
             ):
                 raise ValueError(
-                    "FP8 KV cache is currently only supported for flashinfer on CUDA and paged attention on ROCM"
+                    "FP8 KV cache is currently only supported for flashinfer on CUDA and paged attention on ROCm. "
                 )
             if SYSTEM == "rocm" and dtype == torch.float8_e5m2:
                 raise ValueError(
-                    "float8_e5m2 FP8 KV cache is not supported on AMD Rocm"
+                    "float8_e5m2 FP8 KV cache is not supported on AMD ROCm"
                 )
 
-        self.kv_cache_dtype_str = "auto"
+        self.kv_cache_dtype = "auto"
         if SYSTEM == "rocm" and dtype == torch.float8_e4m3fn:
-            self.kv_cache_dtype_str = "fp8"
+            self.kv_cache_dtype = "fp8"
             dtype = torch.uint8
 
         element_size = torch.tensor([], dtype=dtype).element_size()
@@ -123,27 +124,16 @@ class KVCache:
             self.dtype == torch.float8_e4m3fn
             and ATTENTION == "flashinfer"
             and SYSTEM == "cuda"
+        ) or (
+            self.kv_cache_dtype == "fp8" and ATTENTION == "paged" and SYSTEM == "rocm"
         ):
-            log_once(
-                logger.info,
-                "Using FP8 KV cache scales",
-            )
-            return True
-        elif (
-            self.kv_cache_dtype_str == "fp8"
-            and ATTENTION == "paged"
-            and SYSTEM == "rocm"
-        ):
-            log_once(
-                logger.info,
-                "Using FP8 KV cache scales",
-            )
+            log_once(logger.info, "Using FP8 KV cache scales")
             return True
         else:
             # We have scales, but not the correct FP8 cache type, so warn once.
             log_once(
                 logger.info,
-                "Ignoring FP8 KV cache scales, only float8_e4m3fn KV cache on flashinfer is supported",
+                "Ignoring FP8 KV cache scales, supported only for flashinfer on CUDA and paged attention on ROCm",
             )
             return False
 
@@ -213,7 +203,7 @@ class KVCache:
                 key_cache,
                 value_cache,
                 slots,
-                self.kv_cache_dtype_str,
+                self.kv_cache_dtype,
                 kv_scales.key_scale_cpu,
                 kv_scales.value_scale_cpu,
             )

server/text_generation_server/layers/attention/rocm.py

@@ -119,7 +119,7 @@ def paged_attention(
             block_size,
             max_s,
             None,
-            kv_cache.kv_cache_dtype_str,
+            kv_cache.kv_cache_dtype,
             kv_scales.key_scale_cpu,
             kv_scales.value_scale_cpu,
         )
@@ -154,7 +154,7 @@ def paged_attention(
                 block_size,
                 max_s,
                 None,
-                kv_cache.kv_cache_dtype_str,
+                kv_cache.kv_cache_dtype,
                 kv_scales.key_scale_cpu,
                 kv_scales.value_scale_cpu,
             )
@@ -174,7 +174,7 @@ def paged_attention(
                 block_size,
                 max_s,
                 None,
-                kv_cache.kv_cache_dtype_str,
+                kv_cache.kv_cache_dtype,
                 kv_scales.key_scale_cpu,
                 kv_scales.value_scale_cpu,
                 None,

server/text_generation_server/models/custom_modeling/flash_llama_modeling.py

@@ -398,16 +398,10 @@ class LlamaMLP(nn.Module):
             return self.down_proj(out, adapter_data)
         else:
             gate_up_states = self.gate_up_proj(hidden_states, adapter_data)
-            output_shape = gate_up_states.shape[:-1] + (self.intermediate_size,)
-            out = torch.empty(
-                output_shape, dtype=gate_up_states.dtype, device=gate_up_states.device
+            gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
+            return self.down_proj(
+                self.act(gate_up_states[:, 0]) * gate_up_states[:, 1], adapter_data
             )
-            ops.silu_and_mul(out, gate_up_states)
-            return self.down_proj(out, adapter_data)
-            # gate_up_states = gate_up_states.view(-1, 2, self.intermediate_size)
-            # return self.down_proj(
-            #     self.act(gate_up_states[:, 0]) * gate_up_states[:, 1], adapter_data
-            # )
 
 
 class FlashLlamaLayer(nn.Module):

server/text_generation_server/models/custom_modeling/flash_mixtral_modeling.py

@@ -520,46 +520,6 @@ class FlashMixtralForCausalLM(torch.nn.Module):
         lm_head_indices: Optional[torch.Tensor] = None,
         adapter_data: Optional[torch.Tensor] = None,
     ) -> torch.Tensor:
-
-        if (
-            torch.distributed.get_rank() == 0
-            and input_ids.shape[0] == 262144
-            and cu_seqlen_prefill is not None
-        ):
-            with torch.profiler.profile(
-                activities=[
-                    torch.profiler.ProfilerActivity.CPU,
-                    torch.profiler.ProfilerActivity.CUDA,
-                ],
-                record_shapes=True,
-            ) as prof:
-                true_max_s = max_s
-                if prefill_cache_indices is not None:
-                    # Slots also need to be sliced as it has the same size as the whole kv tensor
-                    slots = slots[prefill_cache_indices]
-                elif self.max_past is not None:
-                    # Clamp in decode mode as paged attention requires clamped values whereas the flash attention
-                    # kernel requires the true values
-                    seqlen = seqlen.clamp(max=self.max_past_tensor)
-
-                hidden_states = self.model(
-                    input_ids,
-                    position_ids,
-                    cu_seqlen_prefill,
-                    kv_cache,
-                    block_tables,
-                    slots,
-                    seqlen,
-                    max_s,
-                    true_max_s,
-                    prefill_cache_indices,
-                )
-                if lm_head_indices is not None:
-                    hidden_states = hidden_states[lm_head_indices]
-                logits = self.lm_head(hidden_states)
-
-            prof.export_chrome_trace("/tgi/trace_mistral_prefill.json")
-        else:
         true_max_s = max_s
         if prefill_cache_indices is not None:
             # Slots also need to be sliced as it has the same size as the whole kv tensor