diff --git a/neural_compressor/torch/algorithms/fp8_quant/_quant_common/helper_modules.py b/neural_compressor/torch/algorithms/fp8_quant/_quant_common/helper_modules.py index 05770f7b171..88da743db7e 100755 --- a/neural_compressor/torch/algorithms/fp8_quant/_quant_common/helper_modules.py +++ b/neural_compressor/torch/algorithms/fp8_quant/_quant_common/helper_modules.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import os import torch import torch.nn as nn import types @@ -1296,6 +1297,10 @@ def __init__(self, mod, parent, mod_extra_config, *args, **kwargs): self.register_scale("descale_amax", mod_extra_config.scale.inputs[3].type(torch.float32), self.scale_format) self.register_scale("scale_output", 1 / mod_extra_config.scale.outputs[0].type(torch.float32), self.scale_format) self.register_scale("scale_amax", 1 / self.descale_amax, self.scale_format) + self.qkv_slice_thld = int(os.getenv("VLLM_FUSEDSDPA_QKV_SLICE_SEQ_LEN_THLD", 8192)) + if self.qkv_slice_thld > 0: + self.q_chunk_size = int(os.getenv("VLLM_FUSEDSDPA_Q_SLICE_CHUNK_SIZE", self.qkv_slice_thld)) + self.kv_chunk_size = int(os.getenv("VLLM_FUSEDSDPA_KV_SLICE_CHUNK_SIZE", self.qkv_slice_thld)) def forward_qdq( self, @@ -1330,6 +1335,41 @@ def forward_qdq( seq_padding_type, ) return results + + def fp8_fsdpa_fwd(self, + q, + k, + v, + attn_mask, + dropout_p, + scale, + is_causal, + softmax_mode, + ): + results = torch.ops.hpu.fp8_sdpa_recomp_fwd( + q, + k, + v, + attn_mask, + dropout_p, + scale, + is_causal, + True, # requires_backward + softmax_mode, # softmax_mode + self.scale_q, # d_scale_q + self.scale_k, # d_scale_k + self.scale_v, # d_scale_v + self.scale_amax, # q_scale_s + self.scale_output, # q_scale_o + self.descale_amax, # d_scale_s + False, # is_amax_s + False, # is_amax_o + None, # valid_seq_len + "right", # seq_padding_type + (-1, -1), # window_size + None, # sink + ) + return results def forward_quant( self, @@ -1345,32 +1385,142 @@ def forward_quant( valid_seq_len=None, seq_padding_type="None", ): - sm_mode = softmax_mode if softmax_mode == "fp32" else "None" + sm_mode = softmax_mode if softmax_mode == "fp32" else "none" qinput = self.quant_q(q).detach() kinput = self.quant_k(k).detach() vinput = self.quant_v(v).detach() - results = self.fp8_fused_sdpa( - qinput, - kinput, - vinput, - attn_mask=attn_mask, - dropout_p=dropout_p, - is_causal=is_causal, - scale=scale, - softmax_mode=sm_mode, - d_scale_q=self.scale_q, - d_scale_k=self.scale_k, - d_scale_v=self.scale_v, - q_scale_s=self.scale_amax, - q_scale_o=self.scale_output, - d_scale_s=self.descale_amax, - is_amax_s=False, - valid_seq_len=valid_seq_len, - seq_padding_type=seq_padding_type, - ) - output = results[0] - d_out = self.dequant_output(output) - return d_out + q_len = q.shape[-2] + kv_len = kinput.size(-2) + + # for prefill with prefix caching + if self.qkv_slice_thld > 0 and q_len != 1 and q_len != kv_len and kv_len > self.qkv_slice_thld: + assert attn_mask is not None, "Attention mask is required for FSDPA with prefix caching." + ctx_len = kv_len - q_len + from habana_frameworks.torch.hpex.kernels.Fp8FusedSDPA import is_gqa, gqa_input_reshape_fwd, gqa_output_reshape + gqa = is_gqa(qinput, kinput) + if gqa: + qinput, kinput, vinput, attn_mask = gqa_input_reshape_fwd(qinput, kinput, vinput, attn_mask) + + num_q_chunks = (q_len + self.q_chunk_size - 1) // self.q_chunk_size + num_context_kv_chunks = (ctx_len + self.kv_chunk_size - 1) // self.kv_chunk_size + num_causal_kv_chunks = num_q_chunks + chunk_outputs = [] + for q_chunk_idx in range(num_q_chunks): + q_start = q_chunk_idx * self.q_chunk_size + q_end = min((q_chunk_idx + 1) * self.q_chunk_size, q_len) + q_chunk = qinput[..., q_start:q_end, :] + + last_out = None + last_m = None + last_linv = None + for kv_chunk_idx in range(num_context_kv_chunks): + kv_start = kv_chunk_idx * self.kv_chunk_size + kv_end = min((kv_chunk_idx + 1) * self.kv_chunk_size, ctx_len) + k_chunk = kinput[..., kv_start:kv_end, :] + v_chunk = vinput[..., kv_start:kv_end, :] + + chunk_res = self.fp8_fsdpa_fwd(q_chunk, k_chunk, v_chunk, None, dropout_p, scale, False, sm_mode) + chunk_out, chunk_m, chunk_linv = (gqa_output_reshape(x) for x in (chunk_res[:3])) if gqa else chunk_res[:3] + + chunk_m = chunk_m.to(torch.float32) + chunk_linv = chunk_linv.to(torch.float32) * 128.0 if softmax_mode != "fp32" else chunk_linv.to(torch.float32) + chunk_out = self.dequant_output(chunk_out).to(torch.float32) + + if kv_chunk_idx == 0: + last_out = chunk_out + last_m = chunk_m + last_linv = chunk_linv + else: + new_m = torch.maximum(last_m, chunk_m) + last_linv_rescaled = (1.0 / last_linv) * torch.exp(last_m - new_m) + chunk_linv_rescaled = (1.0 / chunk_linv) * torch.exp(chunk_m - new_m) + last_linv = 1.0 / (last_linv_rescaled + chunk_linv_rescaled) + last_out = (last_linv_rescaled * last_linv) * last_out + ( + chunk_linv_rescaled * last_linv) * chunk_out + last_m = new_m + + kv_causal_start = ctx_len + q_start + kv_causal_end = ctx_len + q_end + k_causal_chunk = kinput[..., kv_causal_start:kv_causal_end, :] + v_causal_chunk = vinput[..., kv_causal_start:kv_causal_end, :] + + bs = q_chunk.size(0) + q_chunk_len = q_chunk.size(-2) + if q_chunk.size(-2) < self.q_chunk_size: + mask = (1 - torch.tril( + torch.ones(bs, + 1, + 1, + q_chunk_len, + q_chunk_len, + dtype=q.dtype, + device=q.device))) * torch.finfo( + q.dtype).min + causal_chunk_res = self.fp8_fsdpa_fwd(q_chunk, k_causal_chunk, v_causal_chunk, mask, dropout_p, scale, False, sm_mode) + else: + causal_chunk_res = self.fp8_fsdpa_fwd(q_chunk, k_causal_chunk, v_causal_chunk, None, dropout_p, scale, True, sm_mode) + + causal_chunk_out, causal_chunk_m, causal_chunk_linv = (gqa_output_reshape(x) for x in (causal_chunk_res[:3])) if gqa else causal_chunk_res[:3] + causal_chunk_m = causal_chunk_m.to(torch.float32) + causal_chunk_linv = causal_chunk_linv.to(torch.float32) * 128.0 if softmax_mode != "fp32" else causal_chunk_linv.to(torch.float32) + causal_chunk_out = self.dequant_output(causal_chunk_out).to(torch.float32) + + if num_causal_kv_chunks == 1: + new_m = torch.maximum(last_m, causal_chunk_m) + last_linv_rescaled = (1.0 / last_linv) * torch.exp(last_m - new_m) + chunk_linv_rescaled = (1.0 / causal_chunk_linv) * torch.exp(causal_chunk_m - new_m) + last_linv = 1.0 / (last_linv_rescaled + chunk_linv_rescaled) + last_out = (last_linv_rescaled * last_linv) * last_out + ( + chunk_linv_rescaled * last_linv) * causal_chunk_out + last_m = new_m + else: + for kv_chunk_idx in range(0, q_chunk_idx): + kv_causal_start = ctx_len + kv_chunk_idx * self.q_chunk_size + kv_causal_end = ctx_len + (kv_chunk_idx + 1) * self.q_chunk_size + k_causal_chunk = kinput[..., kv_causal_start:kv_causal_end, :] + v_causal_chunk = vinput[..., kv_causal_start:kv_causal_end, :] + + chunk_res = self.fp8_fsdpa_fwd(q_chunk, k_causal_chunk, v_causal_chunk, None, dropout_p, scale, False, sm_mode) + + chunk_out, chunk_m, chunk_linv = (gqa_output_reshape(x) for x in (chunk_res[:3])) if gqa else chunk_res[:3] + chunk_m = chunk_m.to(torch.float32) + chunk_linv = chunk_linv.to(torch.float32) * 128.0 if softmax_mode != "fp32" else chunk_linv.to(torch.float32) + chunk_out = self.dequant_output(chunk_out).to(torch.float32) + + new_m = torch.maximum(last_m, chunk_m) + last_linv_rescaled = (1.0 / last_linv) * torch.exp(last_m - new_m) + chunk_linv_rescaled = (1.0 / chunk_linv) * torch.exp(chunk_m - new_m) + last_linv = 1.0 / (last_linv_rescaled + chunk_linv_rescaled) + last_out = (last_linv_rescaled * last_linv) * last_out + ( + chunk_linv_rescaled * last_linv) * chunk_out + last_m = new_m + + chunk_outputs.append(last_out) + output = torch.cat(chunk_outputs, dim=-2) + return output.to(q.dtype) + else: + results = self.fp8_fused_sdpa( + qinput, + kinput, + vinput, + attn_mask=attn_mask, + dropout_p=dropout_p, + is_causal=is_causal, + scale=scale, + softmax_mode=sm_mode, + d_scale_q=self.scale_q, + d_scale_k=self.scale_k, + d_scale_v=self.scale_v, + q_scale_s=self.scale_amax, + q_scale_o=self.scale_output, + d_scale_s=self.descale_amax, + is_amax_s=False, + valid_seq_len=valid_seq_len, + seq_padding_type=seq_padding_type, + ) + output = results[0] + d_out = self.dequant_output(output) + return d_out def forward_measure( self,