[gpt-oss] batched prefill and prefill tracing

models/demos/gpt_oss/demo/text_demo.py

@@ -36,6 +36,7 @@
 from models.tt_transformers.demo.simple_text_demo import create_tt_page_table, load_inputs
 from models.tt_transformers.tt.common import (
     PagedAttentionConfig,
+    copy_host_to_device,
     get_padded_prefill_len,
     preprocess_inputs_prefill,
     sample_host,
@@ -242,7 +243,7 @@ def prepare_gpt_oss_generator_args(
             {"page_block_size": 64, "page_max_num_blocks_per_dp": 128 * 1024 // 64},  # page_params
             {"temperature": 0, "top_p": 0.08},  # sampling_params (greedy decoding)
             True,  # enable_decode_trace
-            False,  # enable_prefill_trace
+            True,  # enable_prefill_trace
             True,  # users_row_sharded
             False,  # long_context_mode
         ),
@@ -572,33 +573,302 @@ def test_gpt_oss_demo(
             logger.info(f"Prefill finished for {num_real_users} real users")
             logger.info(f"First generated token (user 0): '{tokenizer.decode(prefilled_token[0])}'")
         else:
-            # Standard batch prefill (matching tt_transformers)
-            logger.info("Starting prefill warmup...")
-            profiler.start(f"compile_prefill", iteration=batch_idx)
-            generator.prefill_forward_text(
-                input_tokens_prefill_pt[:1],
-                page_table=page_table,
-                kv_cache=tt_kv_cache,
-                prompt_lens=decoding_pos,
-                enable_trace=enable_prefill_trace,
-                warmup_prefill=False,
-            )
-            profiler.end(f"compile_prefill", iteration=batch_idx)
-            logger.info("Finished prefill warmup")
+            # Row-parallel batched prefill: process 4 users at once (one per mesh row)
+            # This gives ~4x speedup over sequential per-user prefill
+            num_rows = mesh_device.shape[0]
+            users_per_row_prefill = global_batch_size // num_rows
+            users_per_row_per_iter = 1  # Users each mesh row processes per prefill iteration
+            # Increasing above 1 requires model changes:
+            #   - attention/prefill.py: relax batch_size!=1 check, loop paged_fill_cache
+            #   - model.py:process_output_prefill_batched: extract multiple logits per row
+            assert users_per_row_prefill % users_per_row_per_iter == 0
+            num_prefill_iters = users_per_row_prefill // users_per_row_per_iter
+            model_id = 0  # data_parallel=1, single model
+
+            prefilled_token = torch.zeros(global_batch_size, dtype=torch.long)
+
+            if enable_prefill_trace:
+                # === TRACED BATCHED PREFILL ===
+                # Trace captures device program once, then replays with input buffer updates.
+                # Eliminates per-iteration host dispatch overhead.
+
+                # Uniform padded_len for all users (required for tracing: fixed tensor shapes)
+                max_padded_len = max(get_padded_prefill_len(int(decoding_pos[uid])) for uid in range(global_batch_size))
+                block_size = page_params["page_block_size"]
+                max_num_blocks = (max_padded_len + block_size - 1) // block_size
+
+                # Compute fixed get_last_token for trace (all users must be in same 32-token tile)
+                all_last_idxs = [int(decoding_pos[uid]) - 1 for uid in range(global_batch_size)]
+                fixed_get_last_token = (min(all_last_idxs) // 32) * 32
+                max_tile_start = (max(all_last_idxs) // 32) * 32
+                if fixed_get_last_token != max_tile_start:
+                    logger.warning(
+                        f"Users span multiple 32-token tiles ({fixed_get_last_token} vs {max_tile_start}), "
+                        f"using get_last_token=-1 (slower)"
+                    )
+                    fixed_get_last_token = -1
+
+                def _prepare_batch_host(user_indices):
+                    """Prepare host-side tokens + page_table for a batch of users."""
+                    tokens_list, pt_list, last_idxs = [], [], []
+                    for uid in user_indices:
+                        plen = int(decoding_pos[uid])
+                        toks = torch.cat(
+                            [
+                                input_tokens_prefill_pt[uid : uid + 1, :plen],
+                                torch.zeros(1, max_padded_len - plen, dtype=torch.long),
+                            ],
+                            dim=-1,
+                        )
+                        tokens_list.append(toks)
+                        pt_list.append(page_table[uid : uid + 1, :max_num_blocks])
+                        last_idxs.append(plen - 1)
+                    return (torch.cat(tokens_list, dim=0), torch.cat(pt_list, dim=0), last_idxs)
+
+                # --- Warmup (compilation) ---
+                logger.info("Starting traced row-parallel prefill warmup (compilation)...")
+                warmup_indices = [
+                    row * users_per_row_prefill + u for row in range(num_rows) for u in range(users_per_row_per_iter)
+                ]
+                tokens_w, pt_w, last_w = _prepare_batch_host(warmup_indices)
+                tokens_w = tokens_w.reshape(num_rows, -1)  # [num_rows, N*S] for batch>1 concat
+
+                host_out = model[model_id].prepare_inputs_prefill(
+                    tokens_w, page_table=pt_w, trace_enabled=True, batched_prefill=True
+                )
+                rot_global = host_out[1]  # device-resident, fixed across iterations
+                rot_local = host_out[2]  # None
+                host_inputs = (host_out[0], host_out[3], host_out[4])  # tokens, pt, cpt
+
+                profiler.start(f"compile_prefill", iteration=batch_idx)
+                dev_inputs = copy_host_to_device(host_inputs, mesh_device=mesh_device)
+                transformed = model[model_id].transform_and_embed_prefill_inputs_device(*dev_inputs)
+                tt_logits = model[model_id].ttnn_prefill_forward(
+                    transformed[0],
+                    rot_mats_global=rot_global,
+                    rot_mats_local=rot_local,
+                    user_id=0,
+                    page_table=transformed[1],
+                    get_last_token=fixed_get_last_token,
+                    kv_cache=tt_kv_cache[model_id],
+                    batch_size=users_per_row_per_iter,
+                )
+
+                if fixed_get_last_token == -1:
+                    warmup_results = model[model_id].process_output_prefill_batched(
+                        tt_logits,
+                        last_w,
+                        users_per_row=users_per_row_per_iter,
+                        seq_len_per_user=max_padded_len,
+                    )
+                else:
+                    warmup_results = model[model_id].process_output_prefill_batched(
+                        tt_logits,
+                        [idx % 32 for idx in last_w],
+                        users_per_row=users_per_row_per_iter,
+                        seq_len_per_user=32,
+                    )
+                for row, uid in enumerate(warmup_indices):
+                    prefilled_token[uid] = torch.argmax(warmup_results[row].view(-1)).item()
+                profiler.end(f"compile_prefill", iteration=batch_idx)
+                logger.info("Finished traced row-parallel prefill warmup")
+
+                # Clear KV caches (warmup wrote to them)
+                for i in range(len(model)):
+                    for layer_obj in model[i].layers:
+                        k_cache, v_cache = layer_obj.self_attn.layer_past
+                        ttnn.mul(k_cache, 0, output_tensor=k_cache)
+                        ttnn.mul(v_cache, 0, output_tensor=v_cache)
+
+                # --- Trace capture ---
+                logger.info("Capturing prefill trace...")
+                iter0_indices = [
+                    row * users_per_row_prefill + u for row in range(num_rows) for u in range(users_per_row_per_iter)
+                ]
+                tokens_0, pt_0, last_0 = _prepare_batch_host(iter0_indices)
+                tokens_0 = tokens_0.reshape(num_rows, -1)
+                host_out = model[model_id].prepare_inputs_prefill(
+                    tokens_0, page_table=pt_0, trace_enabled=True, batched_prefill=True
+                )
+                host_inputs = (host_out[0], host_out[3], host_out[4])
+
+                trace_dev_inputs = copy_host_to_device(host_inputs, mesh_device=mesh_device)
+                trace_id = ttnn.begin_trace_capture(mesh_device, cq_id=0)
+                # Embed tokens on-device inside the trace (without deallocating input buffer,
+                # since we need to update it between trace executions)
+                tokens_embd = ttnn.embedding(
+                    trace_dev_inputs[0],
+                    model[model_id].embedding_weight,
+                    layout=ttnn.TILE_LAYOUT,
+                    dtype=ttnn.bfloat8_b,
+                )
+                if len(tokens_embd.shape) == 3:
+                    tokens_embd = ttnn.unsqueeze_to_4D(tokens_embd)
+                tt_out_trace = model[model_id].ttnn_prefill_forward(
+                    tokens_embd,
+                    rot_mats_global=rot_global,
+                    rot_mats_local=rot_local,
+                    user_id=0,
+                    page_table=trace_dev_inputs[1],
+                    get_last_token=fixed_get_last_token,
+                    kv_cache=tt_kv_cache[model_id],
+                    batch_size=users_per_row_per_iter,
+                )
+                ttnn.end_trace_capture(mesh_device, trace_id, cq_id=0)
+                logger.info("Prefill trace captured")
+
+                # --- Execute trace for all iterations ---
+                logger.info(
+                    f"Starting traced row-parallel prefill ({num_prefill_iters} iters, "
+                    f"{users_per_row_per_iter} user/row/iter, {global_batch_size} users)..."
+                )
+                profiler.start(f"inference_prefill", iteration=batch_idx)
+                for iter_idx in range(num_prefill_iters):
+                    user_indices = [
+                        row * users_per_row_prefill + iter_idx * users_per_row_per_iter + u
+                        for row in range(num_rows)
+                        for u in range(users_per_row_per_iter)
+                    ]
+                    tokens_i, pt_i, last_i = _prepare_batch_host(user_indices)
+                    tokens_i = tokens_i.reshape(num_rows, -1)
+                    host_out = model[model_id].prepare_inputs_prefill(
+                        tokens_i, page_table=pt_i, trace_enabled=True, batched_prefill=True
+                    )
+                    host_inputs = (host_out[0], host_out[3], host_out[4])
+                    copy_host_to_device(host_inputs, device_tensors=trace_dev_inputs, mesh_device=mesh_device)
+                    ttnn.execute_trace(mesh_device, trace_id, cq_id=0, blocking=False)
+
+                    if fixed_get_last_token == -1:
+                        row_results = model[model_id].process_output_prefill_batched(
+                            tt_out_trace,
+                            last_i,
+                            users_per_row=users_per_row_per_iter,
+                            seq_len_per_user=max_padded_len,
+                        )
+                    else:
+                        row_results = model[model_id].process_output_prefill_batched(
+                            tt_out_trace,
+                            [idx % 32 for idx in last_i],
+                            users_per_row=users_per_row_per_iter,
+                            seq_len_per_user=32,
+                        )
+                    for row, uid in enumerate(user_indices):
+                        prefilled_token[uid] = torch.argmax(row_results[row].view(-1)).item()
+                    if iter_idx % 8 == 0:
+                        logger.info(f"  Traced prefill batch {iter_idx+1}/{num_prefill_iters}")
+                profiler.end(f"inference_prefill", iteration=batch_idx)
+
+                ttnn.release_trace(mesh_device, trace_id)
+                logger.info(f"Traced row-parallel prefill finished ({num_prefill_iters} iterations)")
+
+            else:
+                # === NON-TRACED BATCHED PREFILL ===
+
+                # Helper to run one batched prefill iteration
+                def _run_batched_prefill_iter(iter_idx, user_indices):
+                    batch_tokens_list = []
+                    batch_page_tables = []
+                    batch_last_token_idxs = []
+
+                    for uid in user_indices:
+                        prefill_len = int(decoding_pos[uid])
+                        padded_len = get_padded_prefill_len(prefill_len)
+                        user_tokens = torch.cat(
+                            [
+                                input_tokens_prefill_pt[uid : uid + 1, :prefill_len],
+                                torch.zeros(1, padded_len - prefill_len, dtype=torch.long),
+                            ],
+                            dim=-1,
+                        )
+                        batch_tokens_list.append(user_tokens)
+                        block_size = page_params["page_block_size"]
+                        num_blocks_needed = (padded_len + block_size - 1) // block_size
+                        batch_page_tables.append(page_table[uid : uid + 1, :num_blocks_needed])
+                        batch_last_token_idxs.append(prefill_len - 1)
+
+                    tokens_stacked = torch.cat(batch_tokens_list, dim=0)  # [total_users, padded_len]
+                    page_table_stacked = torch.cat(batch_page_tables, dim=0)  # [total_users, num_blocks]
+                    padded_len = tokens_stacked.shape[1]
+
+                    # Reshape tokens for batch>1: concatenate per-row users along seq dim
+                    tokens_for_model = tokens_stacked.reshape(num_rows, -1)  # [num_rows, N*padded_len]
+
+                    (tokens_embd, rot_mats_global, rot_mats_local, page_table_tt, _) = model[
+                        model_id
+                    ].prepare_inputs_prefill(
+                        tokens_for_model,
+                        page_table=page_table_stacked,
+                        batched_prefill=True,
+                    )
+
+                    # Use get_last_token if all users' last tokens fall in the same 32-token tile
+                    min_tile = (min(batch_last_token_idxs) // 32) * 32
+                    max_tile = (max(batch_last_token_idxs) // 32) * 32
+                    get_last_token_val = min_tile if min_tile == max_tile else -1
+                    tt_logits = model[model_id].ttnn_prefill_forward(
+                        tokens_embd,
+                        rot_mats_global=rot_mats_global,
+                        rot_mats_local=rot_mats_local,
+                        user_id=0,  # Must be 0: each device sees page_table[0] after row-sharding
+                        page_table=page_table_tt,
+                        get_last_token=get_last_token_val,
+                        kv_cache=tt_kv_cache[model_id],
+                        batch_size=users_per_row_per_iter,
+                    )
+
+                    if get_last_token_val == -1:
+                        adjusted_last_idxs = batch_last_token_idxs
+                        seq_len_for_output = padded_len
+                    else:
+                        adjusted_last_idxs = [idx % 32 for idx in batch_last_token_idxs]
+                        seq_len_for_output = 32
+                    row_results = model[model_id].process_output_prefill_batched(
+                        tt_logits,
+                        adjusted_last_idxs,
+                        users_per_row=users_per_row_per_iter,
+                        seq_len_per_user=seq_len_for_output,
+                    )
+                    return row_results
+
+                # Warmup: compile with first batch
+                logger.info("Starting row-parallel prefill warmup...")
+                profiler.start(f"compile_prefill", iteration=batch_idx)
+                warmup_user_indices = [
+                    row * users_per_row_prefill + u for row in range(num_rows) for u in range(users_per_row_per_iter)
+                ]
+                warmup_results = _run_batched_prefill_iter(0, warmup_user_indices)
+                for row, uid in enumerate(warmup_user_indices):
+                    prefilled_token[uid] = torch.argmax(warmup_results[row].view(-1)).item()
+                profiler.end(f"compile_prefill", iteration=batch_idx)
+                logger.info("Finished row-parallel prefill warmup")
+
+                # Clear KV caches before real prefill (warmup wrote to them)
+                for i in range(len(model)):
+                    for layer_obj in model[i].layers:
+                        k_cache, v_cache = layer_obj.self_attn.layer_past
+                        ttnn.mul(k_cache, 0, output_tensor=k_cache)
+                        ttnn.mul(v_cache, 0, output_tensor=v_cache)
+
+                # Real prefill
+                logger.info(
+                    f"Starting row-parallel batched prefill ({num_prefill_iters} iters, "
+                    f"{users_per_row_per_iter} user/row/iter, {global_batch_size} users)..."
+                )
+                profiler.start(f"inference_prefill", iteration=batch_idx)
+                for iter_idx in range(num_prefill_iters):
+                    user_indices = [
+                        row * users_per_row_prefill + iter_idx * users_per_row_per_iter + u
+                        for row in range(num_rows)
+                        for u in range(users_per_row_per_iter)
+                    ]
+                    row_results = _run_batched_prefill_iter(iter_idx, user_indices)
+                    for row, uid in enumerate(user_indices):
+                        prefilled_token[uid] = torch.argmax(row_results[row].view(-1)).item()
+                    if iter_idx % 8 == 0:
+                        logger.info(f"  Prefilled batch {iter_idx+1}/{num_prefill_iters}")
+                profiler.end(f"inference_prefill", iteration=batch_idx)
+                logger.info(f"Row-parallel batched prefill finished ({num_prefill_iters} iterations)")
 
-            logger.info(f"Starting prefill...")
-            profiler.start(f"inference_prefill", iteration=batch_idx)
-            logits = generator.prefill_forward_text(
-                input_tokens_prefill_pt,
-                page_table=page_table,
-                kv_cache=tt_kv_cache,
-                prompt_lens=decoding_pos,
-                enable_trace=enable_prefill_trace,
-                warmup_prefill=False,  # we can warmup prefill ourselves above if we want to
-            )
-            prefilled_token = torch.argmax(logits, dim=-1)
-            profiler.end(f"inference_prefill", iteration=batch_idx)
-            logger.info(f"Prefill finished")
             logger.info(f"First generated token: '{tokenizer.decode(prefilled_token[0])}'")
 
         # Initialize generation state like tt_transformers

models/demos/gpt_oss/tt/attention/__init__.py

@@ -108,7 +108,15 @@ def __init__(
         self.scaling = config.scaling
 
     def __call__(
-        self, hidden_states, rope_mats, position_idx=None, page_table=None, kv_cache=None, is_decode=True, user_id=0
+        self,
+        hidden_states,
+        rope_mats,
+        position_idx=None,
+        page_table=None,
+        kv_cache=None,
+        is_decode=True,
+        user_id=0,
+        batch_size=1,
     ):
         """
         Forward pass - automatically dispatches to decode or prefill.
@@ -169,4 +177,5 @@ def __call__(
                 position_idx=position_idx,
                 page_table=page_table,
                 ccl_manager=self.ccl_manager,
+                batch_size=batch_size,
             )